(12) United States Patent (10) Patent No.: US 7,890.267 B2 Glinsky (45) Date of Patent: Feb

Total Page:16

File Type:pdf, Size:1020Kb

(12) United States Patent (10) Patent No.: US 7,890.267 B2 Glinsky (45) Date of Patent: Feb US0078.90267B2 (12) United States Patent (10) Patent No.: US 7,890.267 B2 Glinsky (45) Date of Patent: Feb. 15, 2011 (54) PROGNOSTIC AND DIAGNOSTIC METHOD driven by the Polycomb Group (PcG) proteins BMI1 and Ezh2:...”. FOR CANCERTHERAPY Proceedings of the American Association for Cancer Research Annual Meeting, 47:984-985. (Abstract only).* (75) Inventor: Gennadi V. Glinsky, Loudonville, NY Raaphorst, F. M. et al. (2000) "Coexpression of BMI-1 and EZH2 (US) polycomb group genes in Reed- Sternberg cells of Hodgkin's dis ease”, American J. Pathology, 157(3): 709-715.* (73) Assignee: Ordway Research Institute, Albany, Veer, Van T L J et al. (2002) "Gene expression profiling predicts NY (US) clinical outcome of breast cancer', Nature, 415(6871): 530-536.* Affymetrix: “GeneChip human genome U95 set'. Data Sheet, (*) Notice: Subject to any disclaimer, the term of this Online 2003, XP0025,04985, retrieved from the Internet: patent is extended or adjusted under 35 URL:http://www.affymetrix.com/support/technical/datasheets/ U.S.C. 154(b) by 714 days. hgu95 datasheet.pdf>. Cheung et al., “Mapping determinants of human gene expression by (21) Appl. No.: 11/732,442 regional and genome-wide association”. Nature, 437: 1365-1369 (22) Filed: Apr. 2, 2007 (2005). Glinsky et al., “Gene expression profiling predicts clinical outcome (65) Prior Publication Data of prostate cancer'. J. Clin. Invest., 113(6):913-923 (2004). Glinsky et al., “Microarray analysis identifies a death-from-cancer US 2009/O 1707 15 A1 Jul. 2, 2009 signature predicting therapy failure in patients with multiple types of cancer, J. Clin. Invest., 115(6): 1503-1521 (2005). Related U.S. Application Data Imai et al., “C421A polymorphism in the human breast cancer resis (60) Provisional application No. 60/875,061, filed on Dec. tance protein gene is associated with low expression of Q141K pro 15, 2006, provisional application No. 60/823,577, tein and low-level drug resistance'. Mol. Cancer Ther. 1(8):611-616 filed on Aug. 25, 2006, provisional application No. (2002). Spielman et al., “Common genetic variants account for differences in 60/822.705, filed on Aug. 17, 2006, provisional appli gene expression among ethnic groups', Nat. Genetics, 39(2):226-231 cation No. 60/787,818, filed on Mar. 31, 2006. (2007). Varambally et al., “Integrative genomic and proteomic analysis of (51) Int. Cl. prostate cancer reveals signature of metastatic progression'. Cancer G06F 7/00 (2006.01) Cell, 8(5):393-406 (2005). (52) U.S. Cl. ............................. 702/19, 702/20; 703/11; 703/13:435/6: 436/501:536/24.5 * cited by examiner (58) Field of Classification Search ....................... None See application file for complete search history. Primary Examiner Mary K Zeman (74) Attorney, Agent, or Firm Ivor R. Elrifi; Mintz, Levin (56) References Cited Cohn Ferris Glovsky and Popeo PC U.S. PATENT DOCUMENTS (57) ABSTRACT 2006, OO73479 A1 4/2006 Frudakis ........................ 435/6 2009/0098538 A1* 4/2009 Glinsky ......................... 435/6 The present invention provides novel methods and kits for FOREIGN PATENT DOCUMENTS diagnosing the presence of cancer within a patient, and for WO WOO1,53834 * 7 2001 determining whether a Subject who has cancer is Susceptible WO WO 2004/O11625 A2 2, 2004 to different types of treatment regimens. The cancers to be WO WO O7,114896 * 10/2007 tested include, but are not limited to, prostate, breast, lung, WO WO 08/021483 * 2/2010 gastric, ovarian, bladder, lymphoma, mesothelioma, WO WO 03/060164 * 7 2010 medulloblastoma, glioma, and AML. Identification of OTHER PUBLICATIONS therapy-resistant patients early in their treatment regimen can lead to a change in therapy in order to achieve a more Suc Mimori et al. 2005, EJSO 31 : 376-380.* cessful outcome. One embodiment of the present invention is Hamer et al. 2002, Hybridoma and Hybridomics).* directed to a method for diagnosing cancer or predicting Berezovska, Olga P. et al. (2006) “Essential role for activation of the cancer-therapy outcome by detecting the expression levels of polycomb group (PcG) protein chromatin silencing pathway in meta static prostate cancer', Cell Cycle 5(16): 1886-1901.* multiple markers in the same cell at the same time, and Bild, Andrea H. et al. (2006) “Oncogenic pathway signatures in scoring their expression as being above a certain threshold, human cancers as a guide to targeted therapies', Nature, 439(7074): wherein the markers are from a particular pathway related to 353-357.* cancer, with the score being indicative or a cancer diagnosis Cao, R. etal. (2005) “Role of Bmi-1 and Ring 1A in H2A ubiquityla or a prognosis for cancer-therapy failure. This method can be tion and hox gene silencing'. Molecular Cell 20(6): 845-854.* used to diagnose cancer or predict cancer-therapy outcomes Glinsky, G.V. et al. (2004) “Classification of human breast cancer for a variety of cancers. The markers can come from any using gene expression profiling as a component of the Survival pre pathway involved in the regulation of cancer, including spe dictor algorithm”. Clinical Cancer Research, 10(7): 2272-2283.* cifically the PCG pathway and the “stemness' pathway. The Glinsky, G.V. et al. (2005) “Death-from-cancer signatures and stem cell contribution to metastatic cancer', Cell Cycle, 4(9): 1171-1175.* markers can be mRNA, microRNA, DNA, or protein. Glinsky, G.V. et al. (2006) “Integrative genomics and proteomics analysis reveals engagement of cooperating oncogenic pathways 2 Claims, 218 Drawing Sheets U.S. Patent Feb. 15, 2011 Sheet 1 of 218 US 7,890.267 B2 DEATH-FROM-CANCER SIGNATURE GENES (GLINSKY et al., JC, 2005) HITS; 3 2 12 SEARCHRESULTS FOR QUERY CCNB1 KNTC2 BUB1 HCFC1 FGFR2 Gbx2 Rnf2 USP22 ANK3 CES1 MK167 BMI1:40 HITS HOMOSAPIENS (HUMAN) GENOME VIEW BUILD 35.1 STATISTICS Fig. 1A U.S. Patent Feb. 15, 2011 Sheet 2 of 218 US 7,890.267 B2 ^ / U.S. Patent Feb. 15, 2011 Sheet 3 of 218 US 7,890.267 B2 <—————————————————————————————————————++——————> U.S. Patent Feb. 15, 2011 Sheet 4 of 218 US 7,890.267 B2 22600k 2261 Ok 22620 k 22630k CEU Phased Haplotypes YRIPhased Haplotypes Heterozygosity/5 Kb 2050 incClf II listasia: corol. Toowoc Cobrod O N-- Fig. 1 B-3 U.S. Patent Feb. 15, 2011 Sheet 5 of 218 US 7,890.267 B2 GENOTYPE AND ALLELE FREQUENCIES OF THE BMI1 ONCOGENESNP rs11012946 INDIFFERENTETHNICGROUPS 0.3 rS1 1 012946 HETEROZYGOTE ALLELE 0.25 FREQUENCY, FREQUENCY O. 2 O 15 HOMOZYGOTE FREQUENCY CEU CHBJPTYR POPULATIONS Fig. 1C GENOTYPE AND ALLELE FREQUENCIES OF THE NEAR-BMI1 ONCOGENESNPS IN DIFFERENTETHNIC GROUPS 0.8 ALLELE ALLELE rS 1 1 0 12952 FREQUENCY rS7099308 FREQUENCY 0.7 -1 0.6 HOMOZYGOTE HOMOZYGOTE >- FREQUENCY FREQUENCY U 0.5 2 D 0.4 SEE 0.3 HETEROZYGOTE HETEROZYGOTE 0.2 -- FREQUENCY FREQUENCY 0.1 O CEU CHBJPTYR POPULATIONS Fig. 1D U.S. Patent Feb. 15, 2011 Sheet 7 of 218 US 7,890.267 B2 ?----···---···«)*^suudpinab U.S. Patent Feb. 15, 2011 Sheet 8 of 218 US 7,890.267 B2 1 115OOk 1 1151 Ok 1 1152Ok 1 1153Ok CEUPhased Haplotypes YRIPhased Haplotypes CHB Phased Haplotypes JPTPhased Haplotypes Heterozygosity/5Kb U.S. Patent Feb. 15, 2011 Sheet 9 of 218 US 7,890.267 B2 HETEROZYGOTE GENOTYPE FREQUENCY INDIFFERENTETHNIC GROUPS FORSNPS OF DEATH-FROM-CANCERSIGNATURE GENES 0. 3 0. 2 J. J. J. EllCEU CHBJPT YR POPULATIONS Fig.1F U.S. Patent Feb. 15, 2011 Sheet 10 of 218 US 7,890.267 B2 HOMOZYGOTE GENOTYPE FREQUENCY INDIFFERENTETHNIC GROUPS FORSNPS OF DEATH-FROM-CANCERSIGNATURE GENES 0.45 0.4 HCFC1 O.35 0.3 BUB1 O.25 CCNB1 0.2 FGFR2 O.15 0.1 KNTC2 O.05 O CEU CHBJPTYR POPULATIONS Fig. 1G ALLELE FREQUENCY INDIFFERENTETHNICGROUPS FOR SNPS OF DEATH-FROM-CANCERSIGNATURE GENES 0.6 HCFC1 BUB1 0.5 CCNB1 FGFR2 0.4 0.3 KNTC2 0.2 0.1 CEUCHBJPTYR POPULATIONS Fig. 1H U.S. Patent Feb. 15, 2011 Sheet 11 of 218 US 7,890.267 B2 PROSTATE CANCER RECURRENCE SIGNATURE GENES (GLINSKY et al., JC, 2004) HTS: U 14 15 16 HTS: 4 2 SEARCH RESULTS FOR QUERY KLF6 Winf$ACHAF1ATCF2 KIAAO476 PPFIA3 CDS2 JUNBFOSER3 TPR1 ZFP36MGC5466:34. HITS HOMOSAPIENS (HUMAN) GENOME VIEWBUILD 35.1 STATISTICS Fig. 2A - GENOTYPE AND ALLELE FREQUENCIES OF THEKLF6 GENESNP rs2388864N DFFERENTETHNIC GROUPS 0.45 ?1 rs2388864 0.4 HETEROZYGOTE ALLELE 0.35 FREQUENCY FREQUENCY 0.3 | - HOMOZYGOTE 0.25 FREOUENCY 0.2 0.15 0.1 0.05 -7E7 F CEU CHBJPTYRI POPULATIONS Fig. 2B U.S. Patent Feb. 15, 2011 Sheet 12 of 218 US 7,890.267 B2 GENOTYPE AND ALLELE FREQUENCIES OF THE Wnt5 GENESNP rs9311564. IN DFFERENTETHNIC GROUPS TCF2 11 rs9311564 rS7209.295 Z CE -FREQUENCY-is 4 -------- o d SE | HOMOZYGOTE SE SE -FREQUENCY SE 2- 27 ft 2 CEU CHBJPT YR CEU CHBJPT YR POPULATIONS POPULATIONS CHAF1A KIAA0476 rS81 12822 rS6695083 >- - U 2 1. 1. O D O O LL SE f La 2f CEU CHBJPT YR CEU CHBJPT YR POPULATIONS POPULATIONS U.S. Patent Feb. 15, 2011 Sheet 13 of 218 US 7,890.267 B2 PPFA3 CDS2 rS 1040 1584 rS61 16661 f Z Z L D d O O L OY I- - CEU CHBJPT YRI CEU CHBJPT YR POPULATIONS POPULATIONS NEAR FOS CHAF1A -n rS8023205 rS1 1668886 - >- 2. U d 2 -1 SE - SE CEU CHBJPT YRI CEU CHBJPT YR POPULATIONS POPULATIONS Fig. 2D U.S. Patent Feb. 15, 2011 Sheet 14 of 218 US 7,890.267 B2 PROSTATE CANCER RECURRENCESIGNATURE GENES (VARAMBALLY et al., CANCER CELL, 2005) 4 + . 6 7 8 9 10 1 1. HTS: 4 14 8 3 5 D - sa - 18 19 20 21 22 X MT NOT PLACED HTS: 8 6 4 16 6 4 8 SEARCHRESULTS FORQUERY:STK6EZH2NUP62 ERBB2IPRALAUBE2IXPO1CDKN2AMSH2 VT1BUBA2SMARCC2 TRIM28 OCLN MCM6 RAN MAPK9ILF3 CSkHADH2STMN1 UHRF1 KPNA2 Cok4 MCM2 RAB27 NEXNKLK3 ACPPPRSS8 MLCKMAP2K4 TRAF3IP2PPP1R12APDLM5 CSRP1 ITGA5FLNACASP7 MMP23AACTA1DLGAP1 MAPKAPK3 BIRC2 CAV2TGFB11 PXNAMACR CRYABCFLAR: 165HTS HOMOSAPIENS (HUMAN) GENOME VIEW BUILD 35.1 STATISTICS -- Fig.
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.
    [Show full text]
  • A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast
    A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast By Jessica M. Vera B.S. University of Wisconsin-Madison, 2007 A thesis submitted to the Faculty of the Graduate School in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Molecular, Cellular, and Developmental Biology 2015 This thesis entitled: A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast written by Jessica M. Vera has been approved for the Department of Molecular, Cellular, and Developmental Biology Tom Blumenthal Robin Dowell Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline iii Vera, Jessica M. (Ph.D., Molecular, Cellular and Developmental Biology) A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast Thesis Directed by Robin Dowell Cryptic unstable transcripts (CUTs) are a largely unexplored class of nuclear exosome degraded, non-coding RNAs in budding yeast. It is highly debated whether CUT transcription has a functional role in the cell or whether CUTs represent noise in the yeast transcriptome. I sought to ascertain the extent of conserved CUT expression across a variety of Saccharomyces yeast strains to further understand and characterize the nature of CUT expression. To this end I designed a Hidden Markov Model (HMM) to analyze strand-specific RNA sequencing data from nuclear exosome rrp6Δ mutants to identify and compare CUTs in four different yeast strains: S288c, Σ1278b, JAY291 (S.cerevisiae) and N17 (S.paradoxus).
    [Show full text]
  • PAR-CLIP Data Indicate That Nrd1-Nab3
    Webb et al. Genome Biology 2014, 15:R8 http://genomebiology.com/2014/15/1/R8 RESEARCH Open Access PAR-CLIP data indicate that Nrd1-Nab3-dependent transcription termination regulates expression of hundreds of protein coding genes in yeast Shaun Webb2, Ralph D Hector1, Grzegorz Kudla3 and Sander Granneman1,2* Abstract Background: Nrd1 and Nab3 are essential sequence-specific yeast RNA binding proteins that function as a heterodimer in the processing and degradation of diverse classes of RNAs. These proteins also regulate several mRNA coding genes; however, it remains unclear exactly what percentage of the mRNA component of the transcriptome these proteins control. To address this question, we used the pyCRAC software package developed in our laboratory to analyze CRAC and PAR-CLIP data for Nrd1-Nab3-RNA interactions. Results: We generated high-resolution maps of Nrd1-Nab3-RNA interactions, from which we have uncovered hundreds of new Nrd1-Nab3 mRNA targets, representing between 20 and 30% of protein-coding transcripts. Although Nrd1 and Nab3 showed a preference for binding near 5′ ends of relatively short transcripts, they bound transcripts throughout coding sequences and 3′ UTRs. Moreover, our data for Nrd1-Nab3 binding to 3′ UTRs was consistent with a role for these proteins in the termination of transcription. Our data also support a tight integration of Nrd1-Nab3 with the nutrient response pathway. Finally, we provide experimental evidence for some of our predictions, using northern blot and RT-PCR assays. Conclusions: Collectively, our data support the notion that Nrd1 and Nab3 function is tightly integrated with the nutrient response and indicate a role for these proteins in the regulation of many mRNA coding genes.
    [Show full text]
  • RNA Polymerase II CTD Phosphatase Rtr1 Fine-Tunes Transcription Termination
    PLOS GENETICS RESEARCH ARTICLE RNA Polymerase II CTD phosphatase Rtr1 fine-tunes transcription termination 1☯ 1☯ 1 Jose F. VictorinoID , Melanie J. FoxID , Whitney R. Smith-KinnamanID , Sarah A. Peck 1 1 1 1 1 JusticeID , Katlyn H. BurrissID , Asha K. Boyd , Megan A. Zimmerly , Rachel R. Chan , 1 2,3 1,3¤ Gerald O. Hunter , Yunlong LiuID , Amber L. MosleyID * 1 Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 2 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 3 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America a1111111111 a1111111111 ☯ These authors contributed equally to this work. a1111111111 ¤ Current address: Amber L. Mosley, Department of Biochemistry and Molecular Biology, Indiana University a1111111111 School of Medicine, Indianapolis, Indiana, United States of America a1111111111 * [email protected] Abstract OPEN ACCESS RNA Polymerase II (RNAPII) transcription termination is regulated by the phosphorylation Citation: Victorino JF, Fox MJ, Smith-Kinnaman status of the C-terminal domain (CTD). The phosphatase Rtr1 has been shown to regulate WR, Peck Justice SA, Burriss KH, Boyd AK, et al. serine 5 phosphorylation on the CTD; however, its role in the regulation of RNAPII termina- (2020) RNA Polymerase II CTD phosphatase Rtr1 tion has not been explored. As a consequence of RTR1 deletion, interactions within the ter- fine-tunes transcription termination. PLoS Genet mination machinery and between the termination machinery and RNAPII were altered as 16(3): e1008317. https://doi.org/10.1371/journal.
    [Show full text]
  • Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.
    [Show full text]
  • A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
    Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context.
    [Show full text]
  • Measuring Gene Expression Part 3 Key Steps in Microarray Analysis
    Measuring Gene Expression Part 3 David Wishart Bioinformatics 301 [email protected] Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (peak picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Comet Tailing • Often caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution. Uneven Spotting/Blotting • Problems with print tips or with overly viscous solution • Problems with humidity in spottiing chamber High Background • Insufficient Blocking • Precipitation of labelled probe Gridding Errors Spotting errors Uneven hybridization Gridding errors Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (spot picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Microarray Scanning PMT Pinhole Detector lens Laser Beam-splitter Objective Lens Dye Glass Slide Microarray Principles Laser 1 Laser 2 Green channel Red channel Scan and detect with overlay images Image process confocal laser system and normalize and analyze Microarray Images • Resolution – standard 10µm [currently, max 5µm] – 100µm spot on chip = 10 pixels in diameter • Image format – TIFF (tagged image file format) 16 bit (64K grey levels) – 1cm x 1cm image at 16 bit = 2Mb (uncompressed) – other formats exist i.e. SCN (Stanford University) • Separate image for each fluorescent sample – channel 1, channel 2, etc. Image
    [Show full text]
  • Supporting Information
    Supporting Information Friedman et al. 10.1073/pnas.0812446106 SI Results and Discussion intronic miR genes in these protein-coding genes. Because in General Phenotype of Dicer-PCKO Mice. Dicer-PCKO mice had many many cases the exact borders of the protein-coding genes are defects in additional to inner ear defects. Many of them died unknown, we searched for miR genes up to 10 kb from the around birth, and although they were born at a similar size to hosting-gene ends. Out of the 488 mouse miR genes included in their littermate heterozygote siblings, after a few weeks the miRBase release 12.0, 192 mouse miR genes were found as surviving mutants were smaller than their heterozygote siblings located inside (distance 0) or in the vicinity of the protein-coding (see Fig. 1A) and exhibited typical defects, which enabled their genes that are expressed in the P2 cochlear and vestibular SE identification even before genotyping, including typical alopecia (Table S2). Some coding genes include huge clusters of miRNAs (in particular on the nape of the neck), partially closed eyelids (e.g., Sfmbt2). Other genes listed in Table S2 as coding genes are [supporting information (SI) Fig. S1 A and C], eye defects, and actually predicted, as their transcript was detected in cells, but weakness of the rear legs that were twisted backwards (data not the predicted encoded protein has not been identified yet, and shown). However, while all of the mutant mice tested exhibited some of them may be noncoding RNAs. Only a single protein- similar deafness and stereocilia malformation in inner ear HCs, coding gene that is differentially expressed in the cochlear and other defects were variable in their severity.
    [Show full text]
  • PAR-CLIP Data Indicate That Nrd1-Nab3-Dependent Transcription Termination Regulates Expression of Hundreds of Protein Coding Genes in Yeast
    Edinburgh Research Explorer PAR-CLIP data indicate that Nrd1-Nab3-dependent transcription termination regulates expression of hundreds of protein coding genes in yeast Citation for published version: Webb, S, Hector, RD, Kudla, G & Granneman, S 2014, 'PAR-CLIP data indicate that Nrd1-Nab3-dependent transcription termination regulates expression of hundreds of protein coding genes in yeast', Genome Biology, vol. 15, no. 1, R8. https://doi.org/10.1186/gb-2014-15-1-r8 Digital Object Identifier (DOI): 10.1186/gb-2014-15-1-r8 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Genome Biology Publisher Rights Statement: © 2014 Webb et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim.
    [Show full text]
  • Title: a Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic
    bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 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-NC 4.0 International license. 1 Title Page: 2 3 Title: A yeast phenomic model for the influence of Warburg metabolism on genetic 4 buffering of doxorubicin 5 6 Authors: Sean M. Santos1 and John L. Hartman IV1 7 1. University of Alabama at Birmingham, Department of Genetics, Birmingham, AL 8 Email: [email protected], [email protected] 9 Corresponding author: [email protected] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 1 bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 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-NC 4.0 International license. 26 Abstract: 27 Background: 28 Saccharomyces cerevisiae represses respiration in the presence of adequate glucose, 29 mimicking the Warburg effect, termed aerobic glycolysis. We conducted yeast phenomic 30 experiments to characterize differential doxorubicin-gene interaction, in the context of 31 respiration vs. glycolysis. The resulting systems level biology about doxorubicin 32 cytotoxicity, including the influence of the Warburg effect, was integrated with cancer 33 pharmacogenomics data to identify potentially causal correlations between differential 34 gene expression and anti-cancer efficacy.
    [Show full text]
  • Transcriptome Surveillance in S. Cerevisiae by Mrna Synthesis and Degradation Coupling and Selective Termination of Non-Coding Rnas
    Dissertation zur Erlangung des Doktorgrades der Fakultat¨ fur¨ Chemie und Pharmazie der Ludwig-Maximilians-Universitat¨ Munchen¨ Transcriptome surveillance in S. cerevisiae by mRNA synthesis and degradation coupling and selective termination of non-coding RNAs Daniel Schulz aus Simmern, Deutschland 2013 Erklarung¨ Diese Dissertation wurde im Sinne von x 7 der Promotionsordnung vom 28. November 2011 von Herrn Prof. Dr. Patrick Cramer betreut. Eidesstattliche Versicherung Diese Dissertation wurde eigenstandig¨ und ohne unerlaubte Hilfe erar- beitet. Munchen,¨ den 11.09.2013 Daniel Schulz Dissertation eingereicht am 13.09.2013 1. Gutachter: Prof. Dr. Patrick Cramer 2. Gutachter: PD Dr. Dietmar Martin Mundliche¨ Prufung¨ am 09.10.2013 Acknowledgments In the first place, I want to express my gratefulness to my dear parents Peter and Eva. Not only have they supported me throughout my whole life, but they have raised me to be the curious person I am. Questioning, not taking predefined opinions and statements for granted, being spontaneous and open minded and last but not least, being optimistic. I want to thank you for letting me make my own decisions early on and making my own experiences in life. I cannot be thankful enough for what you have given to me in life. This is why this thesis is also dedicated to you. I am also very thankful to my supervisor, Patrick Cramer. He is undoubtedly an outstand- ing person and a great leader. It felt good working with him from the first day on and to see him fight through his massive daily workload, still smiling and having an open ear when dropping into his room.
    [Show full text]
  • Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility.
    [Show full text]