DOCSLIB.ORG

Data Mining the Serous Ovarian Tumor Transcriptome

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Data Mining the Serous Ovarian Tumor Transcriptome DATA MINING THE SEROUS OVARIAN TUMOR TRANSCRIPTOME by Timothy Lee Tickle A dissertation submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics and Computational Biology Charlotte 2011 Approved by: Dr. M. Taghi Mostafavi Dr. Christine Richardson Dr. Susan Sell Dr. David L. Tait Dr. Jennifer Weller ii © 2011 Timothy Lee Tickle ALL RIGHTS RESERVED iii ABSTRACT TIMOTHY LEE TICKLE. Data mining the serous ovarian tumor transcriptome. (Under the direction of DR. M. TAGHI MOSTAFAVI AND DR. JENNIFER WALSH WELLER) Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate is 94%, late stage survival rates drop to 28%. It is because most cases are caught in late stages that high mortality is seen. Correct diagnosis is dependent on the presence of symptoms: ∼90% of diagnosed ovar- ian cancers are symptomatic. These symptoms tend to be unfocused and not acute. The goal of this project is to develop a transcript-level data set measuring ovarian tumor expression and associated paracrine signaling for later biomarker research. To this end, laser capture microdissection was used with exon based oligonucleotide ar- rays to measure the transcriptome of benign and malignant (Type II) serous ovarian surface epithelial-stromal tumors. In addition to profiling tumor, surrounding stro- mal tissue expression was measured to examine potential paracrine signaling. In total, ∼270 million measurements were performed using 50 microarrays. An initial analysis was performed to measure quality, and to compare our measurements against known ovarian cancer properties as established in the molecular genetics literature. Using ontological annotation and de novo pathway generation methods, major trends were defined in the data set including the following: apical surface and tight junction ac- tivity, mitotic activity, tumor suppression in benign tumors, epithelial-mesenchymal transitioning, known ovarian tumor oncogene activity, and evidence of paracrine sig- naling. A list of differentially expressed transcripts was defined which may be explored as biomarkers. The potential for meaningful future analysis is diverse. This data set will contribute to the capacity of the cancer genetics community to perform high resolution exploration of serous ovarian epithelial-stromal surface tumors, aiding in developing better diagnostics and therapeutics. iv DEDICATION This is dedicated to all the lives touched by cancer and those who fight for their cures. v ACKNOWLEDGEMENTS I would like to thank the Blumenthal Cancer Center, the Department of Gyne- cologic Oncology, the Sonya Hanko Wyatt Ovarian Cancer Research Endowment at Carolinas Medical Center, the Hemby Cancer Research Endowment, and the Health Services Foundation (Carolinas HealthCare System) for their continued support and passion for ovarian cancer research. Thank you to the Microarray Core Facility at the Cannon Research Center (Carolinas Medical Center) for hybridizing the exon ç samples and for access to Partek® Genomics Suite . Thank you to the Bioinformat- ics Research Center at the North Carolina Research Campus for access to software and expertise. Thank you to the GAANN fellowship program, for their support and interest in my academic experience. Thank you to the College of Computing and Informatics and the Department of Bioinformatics and Genomics for their guidance and untold support in navigating my PhD candidacy. vi TABLE OF CONTENTS LIST OF TABLES ix LIST OF FIGURES xii CHAPTER 1: INTRODUCTION 1 1.1 Ovarian Tumor Clinicopathologic Features 1 1.2 Ovarian Tumor Histology 3 1.3 Serous Ovarian Surface Epithelial-Stromal Tumorigenesis 5 1.4 TumorsandtheSurroundingStroma 8 1.5 Ovarian Tumor Epidemiology 10 1.6 Ovarian Cancer Biomarkers 12 1.7 Alternative Splicing and Tumorigenesis 14 1.8 Microarray Technology 15 1.9 Prior Studies 18 1.10 Experimental Design 21 1.11 Summary 22 CHAPTER 2: MATERIALS AND METHODS 24 2.1 Rationale for the Key Features of the Wet Lab Methods 24 2.1.1 Laser Capture Microdissection 24 2.1.2 Microdissection Schema 25 2.2 Wet Lab Methods 25 2.2.1 Patient Sample Harvesting 27 2.2.2 Hemotoxylin and Eosin staining (Histopathology Confirmation) 28 2.2.3 HistoGene Staining 29 2.2.4 Laser Capture Microdissection 29 2.2.5 RNA Extraction 29 2.2.6 Tissue Scraped RNA Extraction 30 2.2.7 RNA Purification 30 vii 2.2.8 RNA Integrity Measurements 31 2.2.9 Complementary DNA Generation 31 2.2.10 SPIA Amplification 31 2.2.11 Nanodrop Amplified Complementary DNA Quantification 32 2.2.12 ST-Complementary DNA Conversion 32 2.2.13 Fragmentation and Labeling 33 2.2.14 Exon Microarray Hybridization Recipe 33 2.3 Analysis Pipeline 33 2.3.1 In Silico Quality Control 33 2.3.2 Initial Data Generation 34 2.3.3 Inferential Statistics: Gene-level 35 2.3.4 Inferential Statistics: Transcript-level 37 2.3.5 Biological Interpretation 38 CHAPTER 3: RESULTS 42 3.1 Study Sample Details 42 3.2 Laser Capture Microdissection 45 3.3 In Silico Quality Control 46 3.3.1 Rudimentary Microarray Processing Quality Control 46 3.3.2 73 Samples 49 3.3.3 Outlier Experimental Samples 53 3.3.4 Sample Factors: Random and Experimental 53 3.4 Gene-level Analysis 59 3.5 Transcript-level Analysis 62 3.6 Biological Contextualization 65 3.6.1 Chromosomal Location 65 3.6.2 Gene Ontology Analysis 68 3.6.3 Pathway Analysis 68 viii 3.6.4 Gene Interaction Analysis 69 CHAPTER 4: DISCUSSION 74 4.1 Notable Comparisons 74 4.2 Control Sample Evaluations 74 4.3 Ontology Enrichment 75 4.3.1 Cell Junctions 77 4.3.2 Spindle and Condensed Chromosomes 87 4.3.3 Terms Related to Cell Projection 88 4.3.4 Terms Related to Plasma Membranes 91 4.3.5 Cytoskeleton 101 4.3.6 Extracellular Region 101 4.4 Major Trends in Ontologies 105 4.5 NrCAM Expression 108 4.6 Chromosomal Location 109 4.7 Deriving a Gene Network 109 4.8 Future Directions 113 BIBLIOGRAPHY 115 APPENDIX A: LCM IMAGES 136 APPENDIX B: TRANSCRIPT / TRANSCRIPT CLUSTER LISTS 147 APPENDIX C: BIOLOGICAL CONTEXT RESULTS DETAIL 168 ix LIST OF TABLES TABLE 2.1: Planned a posteriori contrasts performed after the ANOVA. 37 TABLE 3.1: Sampling model design. 43 TABLE 3.2: 75 samples counts detail. 44 TABLE 3.3: Basic patient demographic and pathology details. 44 TABLE 3.4: Experiment sample count after all levels of quality control. 54 TABLE 3.5: A posteriori contrasts (transcript cluster level). 61 TABLE 3.6: Transcript level a posteriori comparisons and contrasts. 64 TABLE 3.7: Summary of benigh biological annotation. 66 TABLE 3.8: Summary of malignant biological annotation. 67 TABLE 4.1: Summary of unique Cellular Component term counts. 76 TABLE 4.2: Summary of transcripts related to cell junctions (1 of 3). 84 TABLE 4.3: (continued). 85 TABLE 4.4: (continued). 86 TABLE 4.5: Summary of transcripts related to spindles. 89 TABLE 4.6: Summary of transcripts related to cell projections. 92 TABLE 4.7: Summary of transcripts related to plasma membrane (1 of 6). 95 TABLE 4.8: (continued). 96 TABLE 4.9: (continued). 97 TABLE 4.10: (continued). 98 TABLE 4.11: (continued). 99 TABLE 4.12: (continued). 100 TABLE 4.13: Summary of transcripts related to the cytoskeleton (1 of 2). 102 TABLE 4.14: (continued). 103 TABLE 4.15: Summary of transcripts related to the extracellular region. 106 TABLE B.1: Complete benign tumor transcript list (1 of 4). 147 TABLE B.2: (continued). 148 x TABLE B.3: (continued). 149 TABLE B.4: (continued). 150 TABLE B.5: Complete benign tumor transcript-cluster list (1 of 3). 151 TABLE B.6: (continued). 152 TABLE B.7: (continued). 153 TABLE B.8: Complete malignant tumor transcript list (1 of 3). 154 TABLE B.9: (continued). 155 TABLE B.10:(continued). 156 TABLE B.11:Complete malignant tumor transcript-cluster list (1 of 3). 157 TABLE B.12:(continued). 158 TABLE B.13:(continued). 159 TABLE B.14:RefSeq numbers and fold-changes for benign T transcripts (1 of 3).160 TABLE B.15:(continued). 161 TABLE B.16:(continued). 162 TABLE B.17:(continued). 163 TABLE B.18:RefSeq numbers and fold-changes (malignant, tumor) (1 of 3). 164 TABLE B.19:(continued). 165 TABLE B.20:(continued). 166 TABLE B.21:RefSeq numbers and fold-changes for the benign TS transcript. 167 TABLE C.1: DAVID results for the benign transcript lists (1 of 4). 168 TABLE C.2: (continued). 169 TABLE C.3: (continued). 170 TABLE C.4: (continued). 171 TABLE C.5: DAVID results for the malignant transcript lists (1 of 6). 172 TABLE C.6: (continued). 173 TABLE C.7: (continued). 174 TABLE C.8: (continued). 175 xi TABLE C.9: (continued). 176 TABLE C.10:(continued). 177 TABLE C.11:DAVID results for the benign transcript cluster lists (1 of 2). 178 TABLE C.12:(continued). 179 TABLE C.13:DAVID results for the malignant transcript cluster lists (1 of 6). 180 TABLE C.14:(continued). 181 TABLE C.15:(continued). 182 TABLE C.16:(continued). 183 TABLE C.17:(continued). 184 TABLE C.18:(continued). 185 xii LIST OF FIGURES FIGURE 1.1: Human Exon 1.0 ST microarray chip synthesis. 19 FIGURE 2.1: Microdissection schema of benign sample. 26 FIGURE 2.2: Microdissection schema of malignant sample. 27 FIGURE 2.3: Flow chart of wet lab protocols used in sample preparation. 40 FIGURE 2.4: Sample preparation protocol quality checks. 41 FIGURE 3.1: Percentages of sample pairing schemes. 43 FIGURE 3.2: An image progression, capturing normal epithelium.
Load more

Recommended publications
  • Small Cell Ovarian Carcinoma: Genomic Stability and Responsiveness to Therapeutics
    Gamwell et al. Orphanet Journal of Rare Diseases 2013, 8:33 http://www.ojrd.com/content/8/1/33 RESEARCH Open Access Small cell ovarian carcinoma: genomic stability and responsiveness to therapeutics Lisa F Gamwell1,2, Karen Gambaro3, Maria Merziotis2, Colleen Crane2, Suzanna L Arcand4, Valerie Bourada1,2, Christopher Davis2, Jeremy A Squire6, David G Huntsman7,8, Patricia N Tonin3,4,5 and Barbara C Vanderhyden1,2* Abstract Background: The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Method: The tumourigenic potential of BIN-67 cells was determined and the tumours formed in a xenograft model was compared to human SCCOHT. DNA sequencing, spectral karyotyping and high density SNP array analysis was performed. The sensitivity of the BIN-67 cells to standard chemotherapeutic agents and to vesicular stomatitis virus (VSV) and the JX-594 vaccinia virus was tested. Results: BIN-67 cells were capable of forming spheroids in hanging drop cultures. When xenografted into immunodeficient mice, BIN-67 cells developed into tumours that reflected the hypercalcemia and histology of human SCCOHT, notably intense expression of WT-1 and vimentin, and lack of expression of inhibin. Somatic mutations in TP53 and the most common activating mutations in KRAS and BRAF were not found in BIN-67 cells by DNA sequencing.
    [Show full text]
  • Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
    Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only.
    [Show full text]
  • Regulation of Cdc42 and Its Effectors in Epithelial Morphogenesis Franck Pichaud1,2,*, Rhian F
    © 2019. Published by The Company of Biologists Ltd | Journal of Cell Science (2019) 132, jcs217869. doi:10.1242/jcs.217869 REVIEW SUBJECT COLLECTION: ADHESION Regulation of Cdc42 and its effectors in epithelial morphogenesis Franck Pichaud1,2,*, Rhian F. Walther1 and Francisca Nunes de Almeida1 ABSTRACT An overview of Cdc42 Cdc42 – a member of the small Rho GTPase family – regulates cell Cdc42 was discovered in yeast and belongs to a large family of small – polarity across organisms from yeast to humans. It is an essential (20 30 kDa) GTP-binding proteins (Adams et al., 1990; Johnson regulator of polarized morphogenesis in epithelial cells, through and Pringle, 1990). It is part of the Ras-homologous Rho subfamily coordination of apical membrane morphogenesis, lumen formation and of GTPases, of which there are 20 members in humans, including junction maturation. In parallel, work in yeast and Caenorhabditis elegans the RhoA and Rac GTPases, (Hall, 2012). Rho, Rac and Cdc42 has provided important clues as to how this molecular switch can homologues are found in all eukaryotes, except for plants, which do generate and regulate polarity through localized activation or inhibition, not have a clear homologue for Cdc42. Together, the function of and cytoskeleton regulation. Recent studies have revealed how Rho GTPases influences most, if not all, cellular processes. important and complex these regulations can be during epithelial In the early 1990s, seminal work from Alan Hall and his morphogenesis. This complexity is mirrored by the fact that Cdc42 can collaborators identified Rho, Rac and Cdc42 as main regulators of exert its function through many effector proteins.
    [Show full text]
  • Transcriptome Analyses of Rhesus Monkey Pre-Implantation Embryos Reveal A
    Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Transcriptome analyses of rhesus monkey pre-implantation embryos reveal a reduced capacity for DNA double strand break (DSB) repair in primate oocytes and early embryos Xinyi Wang 1,3,4,5*, Denghui Liu 2,4*, Dajian He 1,3,4,5, Shengbao Suo 2,4, Xian Xia 2,4, Xiechao He1,3,6, Jing-Dong J. Han2#, Ping Zheng1,3,6# Running title: reduced DNA DSB repair in monkey early embryos Affiliations: 1 State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 2 Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China 3 Yunnan Key Laboratory of Animal Reproduction, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 4 University of Chinese Academy of Sciences, Beijing, China 5 Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China 6 Primate Research Center, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China * Xinyi Wang and Denghui Liu contributed equally to this work 1 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press # Correspondence: Jing-Dong J. Han, Email: [email protected]; Ping Zheng, Email: [email protected] Key words: rhesus monkey, pre-implantation embryo, DNA damage 2 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press ABSTRACT Pre-implantation embryogenesis encompasses several critical events including genome reprogramming, zygotic genome activation (ZGA) and cell fate commitment.
    [Show full text]
  • Genomic Profiling of Adult Acute Lymphoblastic Leukemia by Single
    SUPPLEMENTARY APPENDIX Genomic profiling of adult acute lymphoblastic leukemia by single nucleotide polymorphism oligonucleotide microarray and comparison to pediatric acute lymphoblastic leukemia Ryoko Okamoto,1 Seishi Ogawa,2 Daniel Nowak,1 Norihiko Kawamata,1 Tadayuki Akagi,1,3 Motohiro Kato,2 Masashi Sanada,2 Tamara Weiss,4 Claudia Haferlach,4 Martin Dugas,5 Christian Ruckert,5 Torsten Haferlach,4 and H. Phillip Koeffler1,6 1Division of Hematology and Oncology, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, CA, USA; 2Cancer Genomics Project, Graduate School of Medicine, University of Tokyo, Tokyo, Japan; 3Department of Stem Cell Biology, Graduate School of Medical Science, Kanazawa University 4MLL Munich Leukemia Laboratory, Munich, Germany; 5Department of Medical Informatics and Biomathematics, University of Münster, Münster, Germany; 6Cancer Science Institute of Singapore, National University of Singapore, Singapore Citation: Okamoto R, Ogawa S, Nowak D, Kawamata N, Akagi T, Kato M, Sanada M, Weiss T, Haferlach C, Dugas M, Ruckert C, Haferlach T, and Koeffler HP. Genomic profiling of adult acute lymphoblastic leukemia by single nucleotide polymorphism oligonu- cleotide microarray and comparison to pediatric acute lymphoblastic leukemia. Haematologica 2010;95(9):1481-1488. doi:10.3324/haematol.2009.011114 Online Supplementary Data ed by PCR of genomic DNA and subsequent direct sequencing of SNP in a region of CNN-LOH in an ALL sample versus the corresponding Design and Methods matched normal sample (Online Supplementary
    [Show full text]
  • Genome-Wide Analysis of Pax8 Binding Provides New Insights Into
    Ruiz-Llorente et al. BMC Genomics 2012, 13:147 http://www.biomedcentral.com/1471-2164/13/147 RESEARCH ARTICLE Open Access Genome-wide analysis of Pax8 binding provides new insights into thyroid functions Sergio Ruiz-Llorente1,2, Enrique Carrillo SantadePau1,3,4, Ana Sastre-Perona1, Cristina Montero-Conde1,2, Gonzalo Gómez-López3, James A Fagin2, Alfonso Valencia3, David G Pisano3 and Pilar Santisteban1* Abstract Background: The transcription factor Pax8 is essential for the differentiation of thyroid cells. However, there are few data on genes transcriptionally regulated by Pax8 other than thyroid-related genes. To better understand the role of Pax8 in the biology of thyroid cells, we obtained transcriptional profiles of Pax8-silenced PCCl3 thyroid cells using whole genome expression arrays and integrated these signals with global cis-regulatory sequencing studies performed by ChIP-Seq analysis Results: Exhaustive analysis of Pax8 immunoprecipitated peaks demonstrated preferential binding to intragenic regions and CpG-enriched islands, which suggests a role of Pax8 in transcriptional regulation of orphan CpG regions. In addition, ChIP-Seq allowed us to identify Pax8 partners, including proteins involved in tertiary DNA structure (CTCF) and chromatin remodeling (Sp1), and these direct transcriptional interactions were confirmed in vivo. Moreover, both factors modulate Pax8-dependent transcriptional activation of the sodium iodide symporter (Nis) gene promoter. We ultimately combined putative and novel Pax8 binding sites with actual target gene expression regulation to define Pax8-dependent genes. Functional classification suggests that Pax8-regulated genes may be directly involved in important processes of thyroid cell function such as cell proliferation and differentiation, apoptosis, cell polarity, motion and adhesion, and a plethora of DNA/protein-related processes.
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]
  • PGC-1Α Protects from Notch-Induced Kidney Fibrosis Development
    BASIC RESEARCH www.jasn.org PGC-1a Protects from Notch-Induced Kidney Fibrosis Development † ‡ ‡ Seung Hyeok Han,* Mei-yan Wu, § Bo Young Nam, Jung Tak Park,* Tae-Hyun Yoo,* ‡ † † † † Shin-Wook Kang,* Jihwan Park, Frank Chinga, Szu-Yuan Li, and Katalin Susztak *Department of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea; †Renal Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; ‡Severance Biomedical Science Institute, Brain Korea 21 PLUS, Yonsei University College of Medicine, Seoul, Korea; and §Department of Nephrology, The First Hospital of Jilin University, Changchun, China ABSTRACT Kidney fibrosis is the histologic manifestation of CKD. Sustained activation of developmental pathways, such as Notch, in tubule epithelial cells has been shown to have a key role in fibrosis development. The molecular mechanism of Notch-induced fibrosis, however, remains poorly understood. Here, we show that, that expression of peroxisomal proliferation g-coactivator (PGC-1a) and fatty acid oxidation-related genes are lower in mice expressing active Notch1 in tubular epithelial cells (Pax8-rtTA/ICN1) compared to littermate controls. Chromatin immunoprecipitation assays revealed that the Notch target gene Hes1 directly binds to the regulatory region of PGC-1a. Compared with Pax8-rtTA/ICN1 transgenic animals, Pax8-rtTA/ICN1/Ppargc1a transgenic mice showed improvement of renal structural alterations (on his- tology) and molecular defect (expression of profibrotic genes). Overexpression of PGC-1a restored mi- tochondrial content and reversed the fatty acid oxidation defect induced by Notch overexpression in vitro in tubule cells. Furthermore, compared with Pax8-rtTA/ICN1 mice, Pax8-rtTA/ICN1/Ppargc1a mice exhibited improvement in renal fatty acid oxidation gene expression and apoptosis.
    [Show full text]
  • The Polarity Protein Par6 Induces Cell Proliferation and Is Overexpressed in Breast Cancer
    Research Article The Polarity Protein Par6 Induces Cell Proliferation and Is Overexpressed in Breast Cancer Marissa E. Nolan,1,3 Victoria Aranda,3 Sangjun Lee,4 Balasubramanian Lakshmi,3 Srinjan Basu,2 D. Craig Allred,4 and Senthil K. Muthuswamy1,2,3 1Graduate Program in Genetics, Stony Brook University, Stony Brook, New York; 2Watson School ofBiological Sciences, 3Cold Spring Harbor Laboratory, Cold Spring Harbor, New York; and 4Department ofPathology and Immunology, Washington University School ofMedicine, St. Louis, Missouri Abstract proteins Cdc42/Rac (12–15). These interactions are required for The polarity protein complex Par6/atypical protein kinase Par6 regulation ofcell biological processes. For instance, Par6 (aPKC)/Cdc42 regulates polarization processes during regulates aPKC-induced phosphorylation ofLgl (16), Par1 (17), and epithelial morphogenesis, astrocyte migration, and axon Numb proteins (18) during establishment ofapical-basal polarity in epithelial cells. Likewise, Par6-associated aPKC regulates glycogen specification. We, as well as others, have shown that this h h complex is also required for disruption of apical-basal polarity synthase kinase 3 (GSK3 ) activity to induce polarized migration during the oncogene ErbB2-induced transformation and trans- ofastrocytes (19) and promote cell death during three-dimensional forming growth factor B–induced epithelial-mesenchymal epithelial morphogenesis (6). Thus, the Par6 complex can serve as a transition ofmammary epithelial cells. Here, we report that signaling node that regulates diverse biological processes by controlling activation ofdownstream pathways. expression ofPar6 by itselfin mammary epithelial cells induces epidermal growth factor–independent cell proliferation and In addition to its role during normal cell biology, Par6 also development ofhyperplastic three-dimensional acini without regulates initiation and progression ofcell transformation.For affecting apical-basal polarity.
    [Show full text]
  • A Novel Hybrid Cytokine IL233 Mediates Regeneration
    www.nature.com/scientificreports OPEN A Novel Hybrid Cytokine IL233 Mediates regeneration following Doxorubicin-Induced Nephrotoxic Received: 6 July 2018 Accepted: 4 February 2019 Injury Published: xx xx xxxx Vikram Sabapathy, Nardos Tesfaye Cheru, Rebecca Corey, Saleh Mohammad & Rahul Sharma Kidney injury, whether due to ischemic insults or chemotherapeutic agents, is exacerbated by infammation, whereas Tregs are protective. We recently showed that IL-2 and IL-33, especially as a hybrid cytokine (IL233 - bearing IL-2 and IL-33 activities in one molecule), potentiated Tregs and group 2 innate lymphoid cells (ILC2) to prevent renal injury. Recent studies have indicated a reparative function for Tregs and ILC2. Here, using doxorubicin-induced nephrotoxic renal injury model, we investigated whether IL233 administration either before, late or very late after renal injury can restore kidney structure and function. We found that IL233 treatment even 2-weeks post-doxorubicin completely restored kidney function accompanied with an increase Treg and ILC2 in lymphoid and renal compartments, augmented anti-infammatory cytokines and attenuated proinfammatory cytokine levels. IL233 treated mice had reduced infammation, kidney injury (Score values - saline: 3.34 ± 0.334; IL233 pre: 0.42 ± 0.162; IL233 24 hrs: 1.34 ± 0.43; IL233 1 week: 1.2 ± 0.41; IL233 2 week: 0.47 ± 0.37; IL233 24 hrs + PC61: 3.5 ± 0.74) and fbrosis in all treatment regimen as compared to saline controls. Importantly, mice treated with IL233 displayed a reparative program in the kidneys, as evidenced by increased expression of genes for renal progenitor-cells and nephron segments. Our fndings present the frst evidence of an immunoregulatory cytokine, IL233, which could be a potent therapeutic strategy that augments Treg and ILC2 to not only inhibit renal injury, but also promote regeneration.
    [Show full text]
  • Genome-Wide Expression Analysis Ofptf1a- and Ascl1-Deficient Mice
    The Journal of Neuroscience, April 24, 2013 • 33(17):7299–7307 • 7299 Development/Plasticity/Repair Genome-Wide Expression Analysis of Ptf1a- and Ascl1-Deficient Mice Reveals New Markers for Distinct Dorsal Horn Interneuron Populations Contributing to Nociceptive Reflex Plasticity Hendrik Wildner,1 Rebecca Das Gupta,2 Dominique Bro¨hl,3 Paul A. Heppenstall,4 Hanns Ulrich Zeilhofer,1,2* and Carmen Birchmeier3* 1Institute of Pharmacology and Toxicology, University of Zurich, CH-8057 Zurich, Switzerland, 2Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, CH-8093 Zurich, Switzerland, 3Department of Neuroscience, Max Delbru¨ck Center for Molecular Medicine, D-13125 Berlin, Germany, and 4Mouse Biology Unit, European Molecular Biology Laboratory, I-00015 Monterotondo, Italy Inhibitoryinterneuronsofthespinaldorsalhornplaycriticalrolesintheprocessingofnoxiousandinnocuoussensoryinformation.They form a family of morphologically and functionally diverse neurons that likely fall into distinct subtypes. Traditional classifications rely mainly on differences in dendritic tree morphology and firing patterns. Although useful, these markers are not comprehensive and cannot be used to drive specific genetic manipulations targeted at defined subsets of neurons. Here, we have used genome-wide expres- sion profiling of spinal dorsal horns of wild-type mice and of two strains of transcription factor-deficient mice (Ptf1aϪ/Ϫ and Ascl1/ Mash1Ϫ/Ϫ mice) to identify new genetic markers for specific subsets of dorsal horn inhibitory interneurons. Ptf1aϪ/Ϫ mice lack all inhibitory interneurons in the dorsal horn, whereas only the late-born inhibitory interneurons are missing in Ascl1Ϫ/Ϫ mice. We found 30 genes that were significantly downregulated in the dorsal horn of Ptf1aϪ/Ϫ mice. Twenty-one of those also showed reduced expression in Ascl1Ϫ/Ϫ mice.
    [Show full text]
  • Downloaded by Academic Researchers from Academia.Nferx.Com and Will Be Made Accessible to Non-Academic Researchers Upon Reasonable Request
    G C A T T A C G G C A T genes Article A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets Deeksha Doddahonnaiah 1,†, Patrick J. Lenehan 1,† , Travis K. Hughes 1, David Zemmour 1, Enrique Garcia-Rivera 1 , A. J. Venkatakrishnan 1, Ramakrishna Chilaka 2, Apoorv Khare 2, Akhil Kasaraneni 2, Abhinav Garg 2, Akash Anand 2, Rakesh Barve 2, Viswanathan Thiagarajan 2 and Venky Soundararajan 1,2,* 1 nference, One Main Street, Cambridge, MA 02142, USA; [email protected] (D.D.); [email protected] (P.J.L.); [email protected] (T.K.H.); [email protected] (D.Z.); [email protected] (E.G.-R.); [email protected] (A.J.V.) 2 nference Labs, Bengaluru, Karnataka 560017, India; [email protected] (R.C.); [email protected] (A.K.); [email protected] (A.K.); [email protected] (A.G.); [email protected] (A.A.); [email protected] (R.B.); [email protected] (V.T.) * Correspondence: [email protected] † These authors contributed equally. Abstract: Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been Citation: Doddahonnaiah, D.; adequately explored. Here we deployed an NLP framework to objectively quantify associations Lenehan, P.J.; Hughes, T.K.; between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type Zemmour, D.; Garcia-Rivera, E.; terms across over 26 million biomedical documents.
    [Show full text]