Glucocorticoid Signature in a Neuronal Genomic Context Issue Date: 2016-05-10 Glucocorticoid Signature in a Neuronal Genomic Context
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Do Persistent Organic Pollutants Interact
Do persistent organic pollutants interact with the stress response? Individual compounds, and their mixtures, interaction with the glucocorticoid receptor Wilson, J., Berntsen, H. F., Elisabeth Zimmer, K., Verhaegen, S., Frizzell, C., Ropstad, E., & Connolly, L. (2016). Do persistent organic pollutants interact with the stress response? Individual compounds, and their mixtures, interaction with the glucocorticoid receptor. Toxicology Letters, 241, 121-132. https://doi.org/10.1016/j.toxlet.2015.11.014 Published in: Toxicology Letters Document Version: Peer reviewed version Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights © 2015 Elsevier Ireland Ltd This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal 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 Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [email protected]. Download date:26. -
Supplementary Data
Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2). -
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Deregulated Gene Expression Pathways in Myelodysplastic Syndrome Hematopoietic Stem Cells
Leukemia (2010) 24, 756–764 & 2010 Macmillan Publishers Limited All rights reserved 0887-6924/10 $32.00 www.nature.com/leu ORIGINAL ARTICLE Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells A Pellagatti1, M Cazzola2, A Giagounidis3, J Perry1, L Malcovati2, MG Della Porta2,MJa¨dersten4, S Killick5, A Verma6, CJ Norbury7, E Hellstro¨m-Lindberg4, JS Wainscoat1 and J Boultwood1 1LRF Molecular Haematology Unit, NDCLS, John Radcliffe Hospital, Oxford, UK; 2Department of Hematology Oncology, University of Pavia Medical School, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; 3Medizinische Klinik II, St Johannes Hospital, Duisburg, Germany; 4Division of Hematology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; 5Department of Haematology, Royal Bournemouth Hospital, Bournemouth, UK; 6Albert Einstein College of Medicine, Bronx, NY, USA and 7Sir William Dunn School of Pathology, University of Oxford, Oxford, UK To gain insight into the molecular pathogenesis of the the World Health Organization.6,7 Patients with refractory myelodysplastic syndromes (MDS), we performed global gene anemia (RA) with or without ringed sideroblasts, according to expression profiling and pathway analysis on the hemato- poietic stem cells (HSC) of 183 MDS patients as compared with the the French–American–British classification, were subdivided HSC of 17 healthy controls. The most significantly deregulated based on the presence or absence of multilineage dysplasia. In pathways in MDS include interferon signaling, thrombopoietin addition, patients with RA with excess blasts (RAEB) were signaling and the Wnt pathways. Among the most signifi- subdivided into two categories, RAEB1 and RAEB2, based on the cantly deregulated gene pathways in early MDS are immuno- percentage of bone marrow blasts. -
Functions of the Mineralocorticoid Receptor in the Hippocampus By
Functions of the Mineralocorticoid Receptor in the Hippocampus by Aaron M. Rozeboom A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Cellular and Molecular Biology) in The University of Michigan 2008 Doctoral Committee: Professor Audrey F. Seasholtz, Chair Professor Elizabeth A. Young Professor Ronald Jay Koenig Associate Professor Gary D. Hammer Assistant Professor Jorge A. Iniguez-Lluhi Acknowledgements There are more people than I can possibly name here that I need to thank who have helped me throughout the process of writing this thesis. The first and foremost person on this list is my mentor, Audrey Seasholtz. Between working in her laboratory as a research assistant and continuing my training as a graduate student, I spent 9 years in Audrey’s laboratory and it would be no exaggeration to say that almost everything I have learned regarding scientific research has come from her. Audrey’s boundless enthusiasm, great patience, and eager desire to teach students has made my time in her laboratory a richly rewarding experience. I cannot speak of Audrey’s laboratory without also including all the past and present members, many of whom were/are not just lab-mates but also good friends. I also need to thank all the members of my committee, an amazing group of people whose scientific prowess combined with their open-mindedness allowed me to explore a wide variety of interests while maintaining intense scientific rigor. Outside of Audrey’s laboratory, there have been many people in Ann Arbor without whom I would most assuredly have gone crazy. -
1 Evidence for Gliadin Antibodies As Causative Agents in Schizophrenia
1 Evidence for gliadin antibodies as causative agents in schizophrenia. C.J.Carter PolygenicPathways, 20 Upper Maze Hill, Saint-Leonard’s on Sea, East Sussex, TN37 0LG [email protected] Tel: 0044 (0)1424 422201 I have no fax Abstract Antibodies to gliadin, a component of gluten, have frequently been reported in schizophrenia patients, and in some cases remission has been noted following the instigation of a gluten free diet. Gliadin is a highly immunogenic protein, and B cell epitopes along its entire immunogenic length are homologous to the products of numerous proteins relevant to schizophrenia (p = 0.012 to 3e-25). These include members of the DISC1 interactome, of glutamate, dopamine and neuregulin signalling networks, and of pathways involved in plasticity, dendritic growth or myelination. Antibodies to gliadin are likely to cross react with these key proteins, as has already been observed with synapsin 1 and calreticulin. Gliadin may thus be a causative agent in schizophrenia, under certain genetic and immunological conditions, producing its effects via antibody mediated knockdown of multiple proteins relevant to the disease process. Because of such homology, an autoimmune response may be sustained by the human antigens that resemble gliadin itself, a scenario supported by many reports of immune activation both in the brain and in lymphocytes in schizophrenia. Gluten free diets and removal of such antibodies may be of therapeutic benefit in certain cases of schizophrenia. 2 Introduction A number of studies from China, Norway, and the USA have reported the presence of gliadin antibodies in schizophrenia 1-5. Gliadin is a component of gluten, intolerance to which is implicated in coeliac disease 6. -
Wo 2010/075007 A2
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date 1 July 2010 (01.07.2010) WO 2010/075007 A2 (51) International Patent Classification: (81) Designated States (unless otherwise indicated, for every C12Q 1/68 (2006.01) G06F 19/00 (2006.01) kind of national protection available): AE, AG, AL, AM, C12N 15/12 (2006.01) AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, (21) International Application Number: DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, PCT/US2009/067757 HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (22) International Filing Date: KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, 11 December 2009 ( 11.12.2009) ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PE, PG, PH, PL, PT, RO, RS, RU, SC, SD, (25) Filing Language: English SE, SG, SK, SL, SM, ST, SV, SY, TJ, TM, TN, TR, TT, (26) Publication Language: English TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (30) Priority Data: (84) Designated States (unless otherwise indicated, for every 12/3 16,877 16 December 2008 (16.12.2008) US kind of regional protection available): ARIPO (BW, GH, GM, KE, LS, MW, MZ, NA, SD, SL, SZ, TZ, UG, ZM, (71) Applicant (for all designated States except US): DODDS, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, W., Jean [US/US]; 938 Stanford Street, Santa Monica, TM), European (AT, BE, BG, CH, CY, CZ, DE, DK, EE, CA 90403 (US). -
Identification of GA-Binding Protein Transcription Factor Alpha Subunit
International Journal of Molecular Sciences Article Identification of GA-Binding Protein Transcription Factor Alpha Subunit (GABPA) as a Novel Bookmarking Factor Shunya Goto 1, Masashi Takahashi 1, Narumi Yasutsune 1, Sumiki Inayama 1, Dai Kato 2, Masashi Fukuoka 3, Shu-ichiro Kashiwaba 1 and Yasufumi Murakami 1,2,* 1 Department of Biological Science and Technology, Faculty of Industrial Science and Technology, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan; [email protected] (S.G.); [email protected] (M.T.); [email protected] (N.Y.); [email protected] (S.I.); [email protected] (S.K.) 2 Order-MadeMedical Research Inc., 208Todai-Kashiwa VP, 5-4-19 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-0882, Japan; [email protected] 3 Department of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan; [email protected] * Correspondence: [email protected]; Tel.: +81-3-5876-1717 (ext. 1919); Fax: +81-3-5876-1470 Received: 7 February 2019; Accepted: 27 February 2019; Published: 4 March 2019 Abstract: Mitotic bookmarking constitutes a mechanism for transmitting transcriptional patterns through cell division. Bookmarking factors, comprising a subset of transcription factors (TFs), and multiple histone modifications retained in mitotic chromatin facilitate reactivation of transcription in the early G1 phase. However, the specific TFs that act as bookmarking factors remain largely unknown. Previously, we identified the “early G1 genes” and screened TFs that were predicted to bind to the upstream region of these genes, then identified GA-binding protein transcription factor alpha subunit (GABPA) and Sp1 transcription factor (SP1) as candidate bookmarking factors. -
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. -
GABPA Is a Master Regulator of Luminal Identity and Restrains Aggressive Diseases in Bladder Cancer
Cell Death & Differentiation (2020) 27:1862–1877 https://doi.org/10.1038/s41418-019-0466-7 ARTICLE GABPA is a master regulator of luminal identity and restrains aggressive diseases in bladder cancer 1,2,3 3,4 5 2,5 5 3 5 5 Yanxia Guo ● Xiaotian Yuan ● Kailin Li ● Mingkai Dai ● Lu Zhang ● Yujiao Wu ● Chao Sun ● Yuan Chen ● 5 6 3 1,2 1,2 3,7 Guanghui Cheng ● Cheng Liu ● Klas Strååt ● Feng Kong ● Shengtian Zhao ● Magnus Bjorkhölm ● Dawei Xu 3,7 Received: 3 June 2019 / Revised: 20 November 2019 / Accepted: 21 November 2019 / Published online: 4 December 2019 © The Author(s) 2019. This article is published with open access Abstract TERT promoter mutations occur in the majority of glioblastoma, bladder cancer (BC), and other malignancies while the ETS family transcription factors GABPA and its partner GABPB1 activate the mutant TERT promoter and telomerase in these tumors. GABPA depletion or the disruption of the GABPA/GABPB1 complex by knocking down GABPB1 was shown to inhibit telomerase, thereby eliminating the tumorigenic potential of glioblastoma cells. GABPA/B1 is thus suggested as a cancer therapeutic target. However, it is unclear about its role in BC. Here we unexpectedly observed that GABPA ablation 1234567890();,: 1234567890();,: inhibited TERT expression, but robustly increased proliferation, stem, and invasive phenotypes and cisplatin resistance in BC cells, while its overexpression exhibited opposite effects, and inhibited in vivo metastasizing in a xenograft transplant model. Mechanistically, GABPA directly activates the transcription of FoxA1 and GATA3, key transcription factors driving luminal differentiation of urothelial cells. Consistently, TCGA/GEO dataset analyses show that GABPA expression is correlated positively with luminal while negatively with basal signatures. -
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L
www.nature.com/scientificreports OPEN A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L. Patel-Murray1, Miriam Adam2, Nhan Huynh2, Brook T. Wassie2, Pamela Milani2 & Ernest Fraenkel 2,3* High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with benefcial efects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these efects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specifc assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. Unknown modes of action of drug candidates can lead to unpredicted consequences on efectiveness and safety. Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that afect a specifc target or phenotype1–3. However, these advances have not dramatically changed the rate of drug approvals. Between 2000 and 2015, 86% of drug can- didates failed to earn FDA approval, with toxicity or a lack of efcacy being common reasons for their clinical trial termination4,5. -
Long, Noncoding RNA Dysregulation in Glioblastoma
cancers Review Long, Noncoding RNA Dysregulation in Glioblastoma Patrick A. DeSouza 1,2 , Xuan Qu 1, Hao Chen 1,3, Bhuvic Patel 1 , Christopher A. Maher 2,4,5,6 and Albert H. Kim 1,6,* 1 Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; [email protected] (P.A.D.); [email protected] (X.Q.); [email protected] (H.C.); [email protected] (B.P.) 2 Department of Internal Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; [email protected] 3 Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA 4 Department of Biomedical Engineering, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA 5 McDonnell Genome Institute, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA 6 Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA * Correspondence: [email protected] Simple Summary: Developing effective therapies for glioblastoma (GBM), the most common primary brain cancer, remains challenging due to the heterogeneity within tumors and therapeutic resistance that drives recurrence. Noncoding RNAs are transcribed from a large proportion of the genome and remain largely unexplored in their contribution to the evolution of GBM tumors. Here, we will review the general mechanisms of long, noncoding RNAs and the current knowledge of how these impact heterogeneity and therapeutic resistance in GBM. A better understanding of the molecular drivers required for these aggressive tumors is necessary to improve the management and outcomes Citation: DeSouza, P.A.; Qu, X.; of this challenging disease.