Supplementary Table S1: Search Terms
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Legends for Supplemental Figures and Tables Figure S1. Expression of Tlx during retinogenesis. (A) Staged embryos were stained for β- galactosidase knocked into the Tlx locus to indicate Tlx expression. Tlx was expressed in the neural blast layer in the early phase of neural retina development (blue signal). (B) Expression of Tlx in neural retina was quantified using Q-PCR at multiple developmental stages. Figure S2. Expression of p27kip1 and cyclin D1 (Ccnd1) at various developmental stages in wild-type or Tlx-/- retinas. (A) Q-PCR analysis of p27kip1 mRNA expression. (B) Western blotting analysis of p27kip1 protein expression. (C) Q-PCR analysis of cyclin D1 mRNA expression. Figure S3. Q-PCR analysis of mRNA expression of Sf1 (A), Lrh1 (B), and Atn1 (C) in wild-type mouse retinas. RNAs from testis and liver were used as controls. Table S1. List of genes dysregulated both at E15.5 and P0 Tlx-/- retinas. Gene E15.5 P0 Cluste Gene Title Fold Fold r Name p-value p-value Change Change nuclear receptor subfamily 0, group B, Nr0b1 1.65 0.0024 2.99 0.0035 member 1 1 Pou4f3 1.91 0.0162 2.39 0.0031 POU domain, class 4, transcription factor 3 1 Tcfap2d 2.18 0.0000 2.37 0.0001 transcription factor AP-2, delta 1 Zic5 1.66 0.0002 2.02 0.0218 zinc finger protein of the cerebellum 5 1 Zfpm1 1.85 0.0030 1.88 0.0025 zinc finger protein, multitype 1 1 Pten 1.60 0.0155 1.82 0.0131 phospatase and tensin homolog 2 Itgb5 -1.85 0.0063 -1.85 0.0007 integrin beta 5 2 Gpr49 6.86 0.0001 15.16 0.0001 G protein-coupled receptor 49 3 Cmkor1 2.60 0.0007 2.72 0.0013 -
Contig Protein Description Symbol Anterior Posterior Ratio
Table S2. List of proteins detected in anterior and posterior intestine pooled samples. Data on protein expression are mean ± SEM of 4 pools fed the experimental diets. The number of the contig in the Sea Bream Database (http://nutrigroup-iats.org/seabreamdb) is indicated. Contig Protein Description Symbol Anterior Posterior Ratio Ant/Pos C2_6629 1,4-alpha-glucan-branching enzyme GBE1 0.88±0.1 0.91±0.03 0.98 C2_4764 116 kDa U5 small nuclear ribonucleoprotein component EFTUD2 0.74±0.09 0.71±0.05 1.03 C2_299 14-3-3 protein beta/alpha-1 YWHAB 1.45±0.23 2.18±0.09 0.67 C2_268 14-3-3 protein epsilon YWHAE 1.28±0.2 2.01±0.13 0.63 C2_2474 14-3-3 protein gamma-1 YWHAG 1.8±0.41 2.72±0.09 0.66 C2_1017 14-3-3 protein zeta YWHAZ 1.33±0.14 4.41±0.38 0.30 C2_34474 14-3-3-like protein 2 YWHAQ 1.3±0.11 1.85±0.13 0.70 C2_4902 17-beta-hydroxysteroid dehydrogenase 14 HSD17B14 0.93±0.05 2.33±0.09 0.40 C2_3100 1-acylglycerol-3-phosphate O-acyltransferase ABHD5 ABHD5 0.85±0.07 0.78±0.13 1.10 C2_15440 1-phosphatidylinositol phosphodiesterase PLCD1 0.65±0.12 0.4±0.06 1.65 C2_12986 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase delta-1 PLCD1 0.76±0.08 1.15±0.16 0.66 C2_4412 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase gamma-2 PLCG2 1.13±0.08 2.08±0.27 0.54 C2_3170 2,4-dienoyl-CoA reductase, mitochondrial DECR1 1.16±0.1 0.83±0.03 1.39 C2_1520 26S protease regulatory subunit 10B PSMC6 1.37±0.21 1.43±0.04 0.96 C2_4264 26S protease regulatory subunit 4 PSMC1 1.2±0.2 1.78±0.08 0.68 C2_1666 26S protease regulatory subunit 6A PSMC3 1.44±0.24 1.61±0.08 -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Gene Expression Profiles of Estrogen Receptor–Positive and Estrogen Receptor–Negative Breast Cancers Are Detectable in Histologically Normal Breast Epithelium
Published OnlineFirst November 8, 2010; DOI: 10.1158/1078-0432.CCR-10-1369 Clinical Cancer Human Cancer Biology Research Gene Expression Profiles of Estrogen Receptor–Positive and Estrogen Receptor–Negative Breast Cancers Are Detectable in Histologically Normal Breast Epithelium Kelly Graham1, Xijin Ge4, Antonio de las Morenas2, Anusri Tripathi3, and Carol L. Rosenberg1,2,3 Abstract Purpose: Previously, we found that gene expression in histologically normal breast epithelium (NlEpi) from women at high breast cancer risk can resemble gene expression in NlEpi from cancer-containing breasts. Therefore, we hypothesized that gene expression characteristic of a cancer subtype might be seen in NlEpi of breasts containing that subtype. Experimental Design: We examined gene expression in 46 cases of microdissected NlEpi from untreated women undergoing breast cancer surgery. From 30 age-matched cases [15 estrogen receptor (ER)þ,15ERÀ] we used Affymetryix U133A arrays. From 16 independent cases (9 ERþ,7ERÀ), we validated selected genes using quantitative real-time PCR (qPCR). We then compared gene expression between NlEpi and invasive breast cancer using four publicly available data sets. Results: We identified 198 genes that are differentially expressed between NlEpi from breasts with ERþ (NlEpiERþ) compared with ERÀ cancers (NlEpiERÀ). These include genes characteristic of ERþ and ERÀ cancers (e.g., ESR1, GATA3, and CX3CL1, FABP7). qPCR validated the microarray results in both the 30 original cases and the 16 independent cases. Gene expression in NlEpiERþ and NlEpiERÀ resembled gene expression in ERþ and ERÀ cancers, respectively: 25% to 53% of the genes or probes examined in four external data sets overlapped between NlEpi and the corresponding cancer subtype. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
The Regulatory Roles of Phosphatases in Cancer
Oncogene (2014) 33, 939–953 & 2014 Macmillan Publishers Limited All rights reserved 0950-9232/14 www.nature.com/onc REVIEW The regulatory roles of phosphatases in cancer J Stebbing1, LC Lit1, H Zhang, RS Darrington, O Melaiu, B Rudraraju and G Giamas The relevance of potentially reversible post-translational modifications required for controlling cellular processes in cancer is one of the most thriving arenas of cellular and molecular biology. Any alteration in the balanced equilibrium between kinases and phosphatases may result in development and progression of various diseases, including different types of cancer, though phosphatases are relatively under-studied. Loss of phosphatases such as PTEN (phosphatase and tensin homologue deleted on chromosome 10), a known tumour suppressor, across tumour types lends credence to the development of phosphatidylinositol 3--kinase inhibitors alongside the use of phosphatase expression as a biomarker, though phase 3 trial data are lacking. In this review, we give an updated report on phosphatase dysregulation linked to organ-specific malignancies. Oncogene (2014) 33, 939–953; doi:10.1038/onc.2013.80; published online 18 March 2013 Keywords: cancer; phosphatases; solid tumours GASTROINTESTINAL MALIGNANCIES abs in sera were significantly associated with poor survival in Oesophageal cancer advanced ESCC, suggesting that they may have a clinical utility in Loss of PTEN (phosphatase and tensin homologue deleted on ESCC screening and diagnosis.5 chromosome 10) expression in oesophageal cancer is frequent, Cao et al.6 investigated the role of protein tyrosine phosphatase, among other gene alterations characterizing this disease. Zhou non-receptor type 12 (PTPN12) in ESCC and showed that PTPN12 et al.1 found that overexpression of PTEN suppresses growth and protein expression is higher in normal para-cancerous tissues than induces apoptosis in oesophageal cancer cell lines, through in 20 ESCC tissues. -
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. -
Antigen-Specific Memory CD4 T Cells Coordinated Changes in DNA
Downloaded from http://www.jimmunol.org/ by guest on September 24, 2021 is online at: average * The Journal of Immunology The Journal of Immunology published online 18 March 2013 from submission to initial decision 4 weeks from acceptance to publication http://www.jimmunol.org/content/early/2013/03/17/jimmun ol.1202267 Coordinated Changes in DNA Methylation in Antigen-Specific Memory CD4 T Cells Shin-ichi Hashimoto, Katsumi Ogoshi, Atsushi Sasaki, Jun Abe, Wei Qu, Yoichiro Nakatani, Budrul Ahsan, Kenshiro Oshima, Francis H. W. Shand, Akio Ametani, Yutaka Suzuki, Shuichi Kaneko, Takashi Wada, Masahira Hattori, Sumio Sugano, Shinichi Morishita and Kouji Matsushima J Immunol Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Author Choice option Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Freely available online through http://www.jimmunol.org/content/suppl/2013/03/18/jimmunol.120226 7.DC1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material Permissions Email Alerts Subscription Author Choice Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 24, 2021. Published March 18, 2013, doi:10.4049/jimmunol.1202267 The Journal of Immunology Coordinated Changes in DNA Methylation in Antigen-Specific Memory CD4 T Cells Shin-ichi Hashimoto,*,†,‡ Katsumi Ogoshi,* Atsushi Sasaki,† Jun Abe,* Wei Qu,† Yoichiro Nakatani,† Budrul Ahsan,x Kenshiro Oshima,† Francis H. -
(12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown Et Al
US 20030082511A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown et al. (43) Pub. Date: May 1, 2003 (54) IDENTIFICATION OF MODULATORY Publication Classification MOLECULES USING INDUCIBLE PROMOTERS (51) Int. Cl." ............................... C12O 1/00; C12O 1/68 (52) U.S. Cl. ..................................................... 435/4; 435/6 (76) Inventors: Steven J. Brown, San Diego, CA (US); Damien J. Dunnington, San Diego, CA (US); Imran Clark, San Diego, CA (57) ABSTRACT (US) Correspondence Address: Methods for identifying an ion channel modulator, a target David B. Waller & Associates membrane receptor modulator molecule, and other modula 5677 Oberlin Drive tory molecules are disclosed, as well as cells and vectors for Suit 214 use in those methods. A polynucleotide encoding target is San Diego, CA 92121 (US) provided in a cell under control of an inducible promoter, and candidate modulatory molecules are contacted with the (21) Appl. No.: 09/965,201 cell after induction of the promoter to ascertain whether a change in a measurable physiological parameter occurs as a (22) Filed: Sep. 25, 2001 result of the candidate modulatory molecule. Patent Application Publication May 1, 2003 Sheet 1 of 8 US 2003/0082511 A1 KCNC1 cDNA F.G. 1 Patent Application Publication May 1, 2003 Sheet 2 of 8 US 2003/0082511 A1 49 - -9 G C EH H EH N t R M h so as se W M M MP N FIG.2 Patent Application Publication May 1, 2003 Sheet 3 of 8 US 2003/0082511 A1 FG. 3 Patent Application Publication May 1, 2003 Sheet 4 of 8 US 2003/0082511 A1 KCNC1 ITREXCHO KC 150 mM KC 2000000 so 100 mM induced Uninduced Steady state O 100 200 300 400 500 600 700 Time (seconds) FIG. -
Emerging Roles of PHLPP Phosphatases in Cell Signaling
PA61CH26_Newton ARjats.cls September 22, 2020 12:5 Annual Review of Pharmacology and Toxicology PHLPPing the Script: Emerging Roles of PHLPP Phosphatases in Cell Signaling Timothy R. Baffi,∗ Ksenya Cohen Katsenelson,∗ and Alexandra C. Newton Department of Pharmacology, University of California, San Diego, La Jolla, California 92093-0721, USA; email: [email protected] Annu. Rev. Pharmacol. Toxicol.2021. 61:26.1–26.21 Keywords The Annual Review of Pharmacology and Toxicology is PHLPP, Akt, PKC, phosphatase, phosphorylation, cancer, transcription online at pharmtox.annualreviews.org https://doi.org/10.1146/annurev-pharmtox-031820- Abstract 122108 Whereas protein kinases have been successfully targeted for a variety of dis- Copyright © 2021 by Annual Reviews. eases, protein phosphatases remain an underutilized therapeutic target, in All rights reserved part because of incomplete characterization of their effects on signaling net- ∗ These authors contributed equally to this article works. The pleckstrin homology domain leucine-rich repeat protein phos- Annu. Rev. Pharmacol. Toxicol. 2021.61. Downloaded from www.annualreviews.org phatase (PHLPP) is a relatively new player in the cell signaling field, and new Access provided by University of California - San Diego on 11/11/20. For personal use only. roles in controlling the balance among cell survival, proliferation, and apop- tosis are being increasingly identified. Originally characterized for its tumor- suppressive function in deactivating the prosurvival kinase Akt, PHLPP may have an opposing role in promoting survival, as recent evidence suggests. Additionally, identification of the transcription factor STAT1 as a substrate unveils a role for PHLPP as a critical mediator of transcriptional programs in cancer and the inflammatory response. -
Rats and Axolotls Share a Common Molecular Signature After Spinal Cord Injury Enriched in Collagen-1
Rats and axolotls share a common molecular signature after spinal cord injury enriched in collagen-1 Athanasios Didangelos1, Katalin Bartus1, Jure Tica1, Bernd Roschitzki2, Elizabeth J. Bradbury1 1Wolfson CARD King’s College London, United Kingdom. 2Centre for functional Genomics, ETH Zurich, Switzerland. Running title: spinal cord injury in rats and axolotls Correspondence: A Didangelos: [email protected] SUPPLEMENTAL FIGURES AND LEGENDS Supplemental Fig. 1: Rat 7 days microarray differentially regulated transcripts. A-B: Protein-protein interaction networks of upregulated (A) and downregulated (B) transcripts identified by microarray gene expression profiling of rat SCI (4 sham versus 4 injured spinal cord samples) 7 days post-injury. Microarray expression data and experimental information is publicly available online (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45006) and is also summarised in Supplemental Table 1. Protein-protein interaction networks were performed in StringDB using the full range of protein interaction scores (0.15 – 0.99) to capture maximum evidence of proteins’ interactions. Networks were then further analysed for betweeness centrality and gene ontology (GO) annotations (BinGO) in Cytoscape. Node colour indicates betweeness centrality while edge colour and thickness indicate interaction score based on predicted functional links between nodes (green: low values; red: high values). C-D: The top 10 upregulated (C) or downregulated (D) transcripts sorted by betweeness centrality score in protein-protein interaction networks shown in A & B. E-F: Biological process GO analysis was performed on networks of upregulated and downregulated genes using BinGO in Cytoscape. Graphs indicate the 20 most significant GO categories and the number of genes in each category. -
Supplementary Table 1: Genes Affected by Anoikis. A, Ratio of Signal
Supplementary Table 1: Genes affected by anoikis. a, ratio of signal intensity of nonanchored cells anchorage dependent cells (CasKoSrc) over anchored cells; b, induced by Src transformation of Cx43KO cells; c, decreased by Src transformation of Cx43Ko cells; *, induced by normalization of Src transformed cells by neighboring nontransformed cells. Gene Symbol Probe Set Fold Changea Gene Name increased Selenbp1 1450699_at 23.22 selenium binding protein 1 Dscr1l1 1450243_a_at 10.77 Down syndrome critical region gene 1-like 1 Dscr1l1 1421425_a_at 4.29 Down syndrome critical region gene 1-like 1 Ttyh1 1426617_a_at 6.70 tweety homolog 1 (Drosophila) 5730521E12Rik 1419065_at 6.16 RIKEN cDNA 5730521E12 gene c 6330406I15Rik 1452244_at 5.87 RIKEN cDNA 6330406I15 gene AF067063 1425160_at 5.73 clone L2 uniform group of 2-cell-stage gene family mRNA Morc 1419418_a_at 5.55 microrchidia c Gpr56 1421118_a_at 5.43 G protein-coupled receptor 56 Pax6 1452526_a_at 5.06 paired box gene 6 Tgfbi 1415871_at 3.73 transforming growth factor beta induced Adarb1 1434932_at 3.70 adenosine deaminase RNA-specific B1 Ddx3y 1452077_at 3.30 DEAD (Asp-Glu-Ala-Asp) box polypeptide 3 Y-linked b Ampd3 1422573_at 3.20 AMP deaminase 3 Gli2 1459211_at 3.07 GLI-Kruppel family member GLI2 Selenbp2 1417580_s_at 2.96 selenium binding protein 2 Adamts1 1450716_at 2.80 a disintegrin-like and metalloprotease with thrombospondin type 1 motif 1 Dusp15 1426189_at 2.70 dual specificity phosphatase-like 15 Dpep3 1429035_at 2.60 dipeptidase 3 Sepp1 1452141_a_at 2.57 selenoprotein P plasma