DOCSLIB.ORG

Identification of Mechanisms of Gemcitabine Resistance Using a Whole Genome Shrna Screen in a Novel in Vitro Coculture Model of Pancreatic Cancer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Identification of Mechanisms of Gemcitabine Resistance Using a Whole Genome Shrna Screen in a Novel in Vitro Coculture Model of Pancreatic Cancer Identification of mechanisms of gemcitabine resistance using a whole genome shRNA screen in a novel in vitro coculture model of pancreatic cancer. Graham David Mills Wolfson College Cancer Research UK Cambridge Institute University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy August 2018 Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee. 1 Abstract Identification of mechanisms of gemcitabine resistance using a whole genome shRNA screen in a novel in vitro coculture model of pancreatic cancer. Graham David Mills Pancreatic ductal adenocarcinoma (PDAC) remains one of the most difficult to treat major cancers, with a 5-year survival of less than 5%, and little to no improvement in prognosis over the previous fifty years. The complexity and heterogeneity of the pancreatic tumour and its highly desmoplastic stromal microenvironment present major physical and biochemical barriers to the delivery and efficacy of therapeutics. Improving the success rate of therapeutic interventions for this cancer requires both a detailed understanding of stromal dynamics and drug resistance within the tumour, as well validated in vitro and in vivo models through which novel therapeutic targets can be identified and investigated. Within this project I focused initially on further developing, validating, and evaluating mechanistically a coculture model of gemcitabine resistance, established previously. I demonstrated that the gemcitabine resistance observed in coculture was a product of cell- cell contact, transient in nature, and related to cell density-mediated signalling processes. I discovered that the mesenchymal-like cell lines thought to drive the resistance effect within the model were epithelial cancer cells in origin, rather than a cancer associated fibroblast (CAF) line, verified through genotype and protein signature experiments. This finding was expanded through demonstration that true pancreatic CAFs did not induce gemcitabine resistance in the coculture model and hence demonstrated the real challenge in isolating true pancreatic CAFs from genetically engineered mouse PDAC tumour tissue using canonical techniques. Nevertheless, the in vitro coculture model data was congruent with in vivo and clinical gemcitabine efficacy data, suggesting it may have utility as a lower cost model for evaluating efficacy of novel drug combinations with the aim of subsequent translation into the clinic. The coculture model was used as the foundation of a genome-wide shRNA depletion screen to identify sensitizers of gemcitabine resistance in PDAC in vitro culture. The primary screen and subsequent validation screen identified 19 high confidence genes with potential 2 translational value, of which knockdown sensitised to gemcitabine within this model. This is alongside an expanded list of 444 genes significantly contributing to the resistance phenotype, representing pathways such as MTORC signalling and DNA damage response. This dataset serves as the first shRNA-mediated coculture screen for gemcitabine resistance in PDAC. My findings lay credence to the value of this model of gemcitabine resistance for preclinical use and provides a robust and voluminous shRNA dataset supporting target identification for the ablation of gemcitabine chemoresistance in PDAC. 3 Acknowledgements I would like to express sincere gratitude towards my supervisor Professor Duncan Jodrell for his support, guidance, and encouragement throughout my doctoral studies. Further, I would like to extend this gratitude to Dr Frances Richards, who has provided unwavering and truly invaluable scientific and pastoral support during my time in the laboratory. I would further like to thank Professor Greg Hannon, my secondary supervisor, for scientific guidance throughout my project as well as Dr Nicolas Erard, a fellow doctoral candidate of the Hannon laboratory, with whom I collaborated with extensively through my degree. I also would like to express thanks to the numerous members of the Jodrell laboratory who provided collaboration and support throughout my time, including Ms Jo Bramhall, Dr Séverine Mollard, Dr Siang Boon Koh, Dr Aarthi Gopinathan, and Dr Sandra Bernaldo de Quirós Fernández. The value of pastoral support and guidance provided by Dr Ann Kaminski will forever be appreciated, as will the funding by Cancer Research UK, and the scientific support of the CRUK CI core facilities, most notably from Chandra Sekhar Reddy Chilamakuri in Bioinformatics. Finally, I would like to acknowledge the unconditional support and encouragement from my parents, John and Felicity Mills, my sister Laura Mills, as well as from friendships old and new. A sympathetic friend can be quite as dear as a brother. 4 List of Abbreviations CAF Cancer-associated fibroblast CDA Cytidine deaminase CRISPR Clustered regularly interspaced short palindromic repeats ECM Extracellular matrix EMT Epithelial-mesenchymal transition FLC Fibroblast-like cell GEDA Genetically engineered mouse-derived allograft GEMM Genetically engineered mouse model GSEA Gene set enrichment analysis IF Immunofluorescence LSL Lox-stop-lox PDAC Pancreatic ductal adenocarcinoma PK/PD Pharmacokinetic/pharmacodynamic PSC Pancreatic stellate cell shRNA short hairpin RNA TCGA The cancer genome atlas 5 Table of Contents 1. Introduction ............................................................................................................... 11 1.1 Introduction to PDAC ................................................................................................ 11 1.1.1 Biology of the pancreas ..................................................................................... 11 1.1.2 Pathology of disease .......................................................................................... 12 1.1.3 Molecular and genetic characterisation ............................................................ 14 1.2 Clinical management of PDAC ................................................................................... 16 1.2.1 Current clinical paradigms ................................................................................. 16 1.2.2 Novel targeted strategies .................................................................................. 17 1.2.3 Challenges in treatment resistance ................................................................... 19 1.2.4 Molecular basis of resistance ............................................................................ 19 1.2.5 Approaches to combat resistance ..................................................................... 21 1.3 Role of stroma and tumour micro-environment in PDAC ......................................... 21 1.3.1 Cell type composition and introduction ............................................................ 21 1.3.2 Pro- and anti-tumourigenic components .......................................................... 23 1.3.3 Targeting stroma for clinical benefit .................................................................. 24 1.4 Use of models to identify and develop new treatment strategies ........................... 26 1.4.1 Overview ............................................................................................................ 26 1.4.2 In silico approaches for drug target identification ............................................ 26 1.4.3 In vivo model development ............................................................................... 26 1.4.4 The role of in vitro models in the drug development process .......................... 28 1.4.5 Development of novel in vitro models .............................................................. 29 1.4.6 Screening techniques for drug development in vitro ........................................ 31 1.4.7 Incorporating stromal analysis into in vitro models in PDAC ............................ 31 1.5 Project aims ............................................................................................................... 33 6 2. Materials and methods .............................................................................................. 34 2.1 Materials ........................................................................................................................ 34 2.1.1 Reagents .................................................................................................................. 34 2.1.2 Cell lines ................................................................................................................... 34 2.1.3 Cell culture reagents ................................................................................................ 35 2.1.4 Buffers.....................................................................................................................
Load more

Recommended publications
  • LANTUS® (Insulin Glargine [Rdna Origin] Injection)
    Rev. March 2007 Rx Only LANTUS® (insulin glargine [rDNA origin] injection) LANTUS® must NOT be diluted or mixed with any other insulin or solution. DESCRIPTION LANTUS® (insulin glargine [rDNA origin] injection) is a sterile solution of insulin glargine for use as an injection. Insulin glargine is a recombinant human insulin analog that is a long-acting (up to 24-hour duration of action), parenteral blood-glucose-lowering agent. (See CLINICAL PHARMACOLOGY). LANTUS is produced by recombinant DNA technology utilizing a non- pathogenic laboratory strain of Escherichia coli (K12) as the production organism. Insulin glargine differs from human insulin in that the amino acid asparagine at position A21 is replaced by glycine and two arginines are added to the C-terminus of the B-chain. Chemically, it is 21A- B B Gly-30 a-L-Arg-30 b-L-Arg-human insulin and has the empirical formula C267H404N72O78S6 and a molecular weight of 6063. It has the following structural formula: LANTUS consists of insulin glargine dissolved in a clear aqueous fluid. Each milliliter of LANTUS (insulin glargine injection) contains 100 IU (3.6378 mg) insulin glargine. Inactive ingredients for the 10 mL vial are 30 mcg zinc, 2.7 mg m-cresol, 20 mg glycerol 85%, 20 mcg polysorbate 20, and water for injection. Inactive ingredients for the 3 mL cartridge are 30 mcg zinc, 2.7 mg m-cresol, 20 mg glycerol 85%, and water for injection. The pH is adjusted by addition of aqueous solutions of hydrochloric acid and sodium hydroxide. LANTUS has a pH of approximately 4. CLINICAL PHARMACOLOGY Mechanism of Action: The primary activity of insulin, including insulin glargine, is regulation of glucose metabolism.
    [Show full text]
  • Drug Information Center Highlights of FDA Activities
    Drug Information Center Highlights of FDA Activities – 3/1/21 – 3/31/21 FDA Drug Safety Communications & Drug Information Updates: Ivermectin Should Not Be Used to Treat or Prevent COVID‐19: MedWatch Update 3/5/21 The FDA advised consumers against the use of ivermectin for the treatment or prevention of COVID‐19 following reports of patients requiring medical support and hospitalization after self‐medicating. Ivermectin has not been approved for this use and is not an anti‐viral drug. Health professionals are encouraged to report adverse events associated with ivermectin to MedWatch. COVID‐19 EUA FAERS Public Dashboard 3/15/21 The FDA launched an update to the FDA Adverse Event Reporting System (FAERS) Public Dashboard that provides weekly updates of adverse event reports submitted to FAERS for drugs and therapeutic biologics used under an Emergency Use Authorization (EUA) during the COVID‐19 public health emergency. Monoclonal Antibody Products for COVID‐19 – Fact Sheets Updated to Address Variants 3/18/21 The FDA authorized revised fact sheets for health care providers to include susceptibility of SARS‐CoV‐2 variants to each of the monoclonal antibody products available through EUA for the treatment of COVID‐19 (bamlanivimab, bamlanivimab and etesevimab, and casirivimab and imdevimab). Abuse and Misuse of the Nasal Decongestant Propylhexedrine Causes Serious Harm 3/25/21 The FDA warned that abuse and misuse of the nasal decongestant propylhexedrine, sold OTC in nasal decongestant inhalers, has been increasingly associated with cardiovascular and mental health problems. The FDA has recommended product design changes to support safe use, such as modifications to preclude tampering and limits on the content within the device.
    [Show full text]
  • Supplemental Figures Tables Importance of the MHC (SLA) in Swine Health and Biomedical Research
    Supplemental Material: Annu. Rev. Anim. Biosci. 2020. 8:171-198 https://doi.org/10.1146/annurev-animal-020518-115014 Importance of the Major Histocompatibility Complex (Swine Leukocyte Antigen) in Swine Health and Biomedical Research Hammer, Ho, Ando, Rogel-Gaillard, Charles, Tector, Tector, and Lunney Supplemental Figures Tables Importance of the MHC (SLA) in swine health and biomedical research Annual Review Animal Biosciences AV08 Lunney Supplemental Figure 1. Chromosomal mapping of the human (HLA complex) and swine MHC (SLA complex) a b HSA 6p21 SSC 7p11-7q11 MOG MOG Class I Class III Class I Class II Class III RING1 Centromere Class II RING1 HLA complex SLA complex Human Leucocyte Antigen Swine Leucocyte Antigen Supplemental Figure 1. Chromosomal mapping of the human (HLA complex) and swine MHC (SLA complex). A. Schematic representation of the chromosome mapping and orientation of the HLA complex on HSA 6p21 and of the pig SLA complex on both sides of the centromere on swine chromosome 7 (SSC7). B. Fluorescent in situ hybridization (FISH) map demonstrating SLA location on SSC7 p11 using a YAC clone containing SLA class Ia genes (adapted from Velten F, Rogel-Gaillard C, Renard C, Pontarotti P, Tazi-Ahnini R, et al. 1998. A first map of the porcine major histocompatibility complex class I region. Tissue Antigens 51:183–94). Supplemental Figure 2. Detailed Physical Map of SLA genes Position Name Position Name Position Name Position Name Position Name 7 22 595 564 22 606 449+ MOG 23 190 757 23 207 872- DHX16 23 705 030 23 706 737- LTB
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Sequences, Annotation and Single Nucleotide Polymorphism of The
    Nova Southeastern University NSUWorks Biology Faculty Articles Department of Biological Sciences 6-2008 Sequences, Annotation and Single Nucleotide Polymorphism of the Major Histocompatibility Complex in the Domestic Cat Naoya Yuhki National Cancer Institute at Frederick James C. Mullikin National Human Genome Research Institute Thomas W. Beck National Cancer Institute at Frederick Robert M. Stephens National Cancer Institute at Frederick Stephen J. O'Brien National Cancer Institute at Frederick, [email protected] Follow this and additional works at: https://nsuworks.nova.edu/cnso_bio_facarticles Part of the Genetics and Genomics Commons, and the Zoology Commons NSUWorks Citation Yuhki, Naoya; James C. Mullikin; Thomas W. Beck; Robert M. Stephens; and Stephen J. O'Brien. 2008. "Sequences, Annotation and Single Nucleotide Polymorphism of the Major Histocompatibility Complex in the Domestic Cat." PLoS ONE 7, (3 e2674): 1-17. https://nsuworks.nova.edu/cnso_bio_facarticles/773 This Article is brought to you for free and open access by the Department of Biological Sciences at NSUWorks. It has been accepted for inclusion in Biology Faculty Articles by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Sequences, Annotation and Single Nucleotide Polymorphism of the Major Histocompatibility Complex in the Domestic Cat Naoya Yuhki1*, James C. Mullikin2, Thomas Beck3, Robert Stephens4, Stephen J. O’Brien1 1 Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland,
    [Show full text]
  • Baqsimi, INN-Glucagon
    17 October 2019 EMA/CHMP/602404/2019 Committee for Medicinal Products for Human Use (CHMP) Assessment report BAQSIMI International non-proprietary name: glucagon Procedure No. EMEA/H/C/003848/0000 Note Assessment report as adopted by the CHMP with all information of a commercially confidential nature deleted. Official address Domenico Scarlattilaan 6 ● 1083 HS Amsterdam ● The Netherlands Address for visits and deliveries Refer to www.ema.europa.eu/how-to-find-us Send us a question Go to www.ema.europa.eu/contact Telephone +31 (0)88 781 6000 An agency of the European Union Table of contents 1. Background information on the procedure .............................................. 7 1.1. Submission of the dossier ...................................................................................... 7 1.2. Steps taken for the assessment of the product ......................................................... 8 2. Scientific discussion ................................................................................ 9 2.1. Problem statement ............................................................................................... 9 2.1.1. Disease or condition ........................................................................................... 9 2.1.2. Epidemiology .................................................................................................. 10 2.1.3. Biologic features, Aetiology and pathogenesis ..................................................... 10 2.1.4. Clinical presentation, diagnosis .........................................................................
    [Show full text]
  • Type 2 Diabetes Adult Outpatient Insulin Guidelines
    Diabetes Coalition of California TYPE 2 DIABETES ADULT OUTPATIENT INSULIN GUIDELINES GENERAL RECOMMENDATIONS Start insulin if A1C and glucose levels are above goal despite optimal use of other diabetes 6,7,8 medications. (Consider insulin as initial therapy if A1C very high, such as > 10.0%) 6,7,8 Start with BASAL INSULIN for most patients 1,6 Consider the following goals ADA A1C Goals: A1C < 7.0 for most patients A1C > 7.0 (consider 7.0-7.9) for higher risk patients 1. History of severe hypoglycemia 2. Multiple co-morbid conditions 3. Long standing diabetes 4. Limited life expectancy 5. Advanced complications or 6. Difficult to control despite use of insulin ADA Glucose Goals*: Fasting and premeal glucose < 130 Peak post-meal glucose (1-2 hours after meal) < 180 Difference between premeal and post-meal glucose < 50 *for higher risk patients individualize glucose goals in order to avoid hypoglycemia BASAL INSULIN Intermediate-acting: NPH Note: NPH insulin has elevated risk of hypoglycemia so use with extra caution6,8,15,17,25,32 Long-acting: Glargine (Lantus®) Detemir (Levemir®) 6,7,8 Basal insulin is best starting insulin choice for most patients (if fasting glucose above goal). 6,7 8 Start one of the intermediate-acting or long-acting insulins listed above. Start insulin at night. When starting basal insulin: Continue secretagogues. Continue metformin. 7,8,20,29 Note: if NPH causes nocturnal hypoglycemia, consider switching NPH to long-acting insulin. 17,25,32 STARTING DOSE: Start dose: 10 units6,7,8,11,12,13,14,16,19,20,21,22,25 Consider using a lower starting dose (such as 0.1 units/kg/day32) especially if 17,19 patient is thin or has a fasting glucose only minimally above goal.
    [Show full text]
  • Pharmacokinetic/Pharmacodynamic Modelling-Based Translational Approach Applied to the Anticancer Drug Gemcitabine in Advanced Pancreatic and Ovarian Cancer”
    Departamento de Farmacia y Tecnología Farmacéutica Facultad de Farmacia y Nutrición UNIVERSIDAD DE NAVARRA “Pharmacokinetic/Pharmacodynamic Modelling-Based Translational Approach applied to the Anticancer Drug Gemcitabine in Advanced Pancreatic and Ovarian Cancer” María García-Cremades Mira Pamplona, 2017 Departamento de Farmacia y Tecnología Farmacéutica Facultad de Farmacia y Nutrición UNIVERSIDAD DE NAVARRA TESIS DOCTORAL “Pharmacokinetic/Pharmacodynamic Modelling-Based Translational Approach applied to the Anticancer Drug Gemcitabine in Advanced Pancreatic and Ovarian Cancer” Trabajo presentado por María García-Cremades Mira para obtener el Grado de Doctor Fdo. María García-Cremades Mira Pamplona, 2017 UNIVERSIDAD DE NAVARRA FACULTAD DE FARMACIA Y NUTRICIÓN Departamento de Farmacia y Tecnología Farmacéutica D. JOSÉ IGNACIO FERNÁNDEZ DE TROCÓNIZ FERNÁNDEZ, Doctor en Farmacia y Catedrático del Departamento de Farmacia y Tecnología Farmacéutica. Certifica: Que el presente trabajo, titulado “Pharmacokinetic/pharmacodynamic modelling-based translational approach applied to the anticancer drug gemcitabine in advanced pancreatic and ovarian cancer”, presentado por DÑA. MARÍA GARCÍA-CREMADES MIRA para optar al grado de Doctor en Farmacia, ha sido realizado bajo su dirección en los Departamentos de Farmacia y Tecnología Farmacéutica. Considerando finalizado el trabajo autorizan su presentación a fin de que pueda ser juzgado y calificado por el Tribunal correspondiente. Y para que así conste, firma la presente: Fdo.: Dr. José Ignacio F Trocóniz Pamplona, 2017 “El mundo es de los que hacen de cada momento una gran aventura” AGRADECIMIENTOS Quisiera comenzar expresando mi agradecimiento a la Universidad de Navarra y al Departamentos de Farmacia y Tecnología Farmacéutica por haberme posibilitado la realización de esta tesis doctoral. Al Dr. Iñaki Trocóniz me gustaría agradecerle la confianza y motivación que desde el primer día me ha dado.
    [Show full text]
  • Does Treatment with Duloxetine for Neuropathic Pain Impact Glycemic Control?
    Clinical Care/Education/Nutrition ORIGINAL ARTICLE Does Treatment With Duloxetine for Neuropathic Pain Impact Glycemic Control? 1 1 THOMAS HARDY, MD, PHD MARY ARMBRUSTER, MSN, CDE betic peripheral neuropathic pain (DPNP) 2 3,4 RICHARD SACHSON, MD ANDREW J.M. BOULTON, MD, FRCP vary from 3% to Ͼ20% (4). 1 SHUYI SHEN, PHD For many agents, the data supporting effectiveness is limited. In addition, nearly all treatments are associated with OBJECTIVE — We examined changes in metabolic parameters in clinical trials of duloxetine safety or tolerability issues. One category for diabetic peripheral neuropathic pain (DPNP). of side effects common to a number of the antidepressant and anticonvulsant drugs RESEARCH DESIGN AND METHODS — Data were pooled from three similarly de- is related to metabolic changes. Weight signed clinical trials. Adults with diabetes and DPNP (n ϭ 1,024) were randomized to 60 mg ϭ gain is seen with tricyclic antidepressants duloxetine q.d., 60 mg b.i.d., or placebo for 12 weeks. Subjects (n 867) were re-randomized (TCAs) (5) and anticonvulsants (e.g., val- to 60 mg duloxetine b.i.d. or routine care for an additional 52 weeks. Mean changes in plasma glucose, lipids, and weight were evaluated. Regression and subgroup analyses were used to proate, gabapentin, pregabalin) (6). identify relationships between metabolic measures and demographic, clinical, and electrophys- Changes in plasma glucose have also been iological parameters. reported with TCAs (7,8) and phenytoin (9), and dyslipidemia can be seen with RESULTS — Duloxetine treatment resulted in modest increases in fasting plasma glucose carbamazepine (10). in short- and long-term studies (0.50 and 0.67 mmol/l, respectively).
    [Show full text]
  • Baricitinib Counteracts Metaflammation, Thus Protecting Against Diet-Induced Metabolic Abnormalities in Mice
    Original Article Baricitinib counteracts metaflammation, thus protecting against diet-induced metabolic abnormalities in mice Debora Collotta 1,5, William Hull 2,5, Raffaella Mastrocola 3, Fausto Chiazza 1, Alessia Sofia Cento 3, Catherine Murphy 2, Roberta Verta 1, Gustavo Ferreira Alves 1, Giulia Gaudioso 4, Francesca Fava 4, Magdi Yaqoob 2, Manuela Aragno 3, Kieran Tuohy 4, Christoph Thiemermann 2,5,**, Massimo Collino 1,5,* ABSTRACT Objective: Recent evidence suggests the substantial pathogenic role of the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway in the development of low-grade chronic inflammatory response, known as “metaflammation,” which contributes to obesity and type 2 diabetes. In this study, we investigated the effects of the JAK1/2 inhibitor baricitinib, recently approved for the treatment of rheumatoid arthritis, in a murine high-fat-high sugar diet model. Methods: Male C57BL/6 mice were fed with a control normal diet (ND) or a high-fat-high sugar diet (HD) for 22 weeks. A sub-group of HD fed mice was treated with baricitinib (10 mg/kg die, p.o.) for the last 16 weeks (HD þ Bar). Results: HD feeding resulted in obesity, insulin-resistance, hypercholesterolemia and alterations in gut microbial composition. The metabolic abnormalities were dramatically reduced by chronic baricitinib administration. Treatment of HD mice with baricitinib did not change the diet- induced alterations in the gut, but restored insulin signaling in the liver and skeletal muscle, resulting in improvements of diet-induced myo- steatosis, mesangial expansion and associated proteinuria. The skeletal muscle and renal protection were due to inhibition of the local JAK2- STAT2 pathway by baricitinib.
    [Show full text]
  • The Microbiota-Produced N-Formyl Peptide Fmlf Promotes Obesity-Induced Glucose
    Page 1 of 230 Diabetes Title: The microbiota-produced N-formyl peptide fMLF promotes obesity-induced glucose intolerance Joshua Wollam1, Matthew Riopel1, Yong-Jiang Xu1,2, Andrew M. F. Johnson1, Jachelle M. Ofrecio1, Wei Ying1, Dalila El Ouarrat1, Luisa S. Chan3, Andrew W. Han3, Nadir A. Mahmood3, Caitlin N. Ryan3, Yun Sok Lee1, Jeramie D. Watrous1,2, Mahendra D. Chordia4, Dongfeng Pan4, Mohit Jain1,2, Jerrold M. Olefsky1 * Affiliations: 1 Division of Endocrinology & Metabolism, Department of Medicine, University of California, San Diego, La Jolla, California, USA. 2 Department of Pharmacology, University of California, San Diego, La Jolla, California, USA. 3 Second Genome, Inc., South San Francisco, California, USA. 4 Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA. * Correspondence to: 858-534-2230, [email protected] Word Count: 4749 Figures: 6 Supplemental Figures: 11 Supplemental Tables: 5 1 Diabetes Publish Ahead of Print, published online April 22, 2019 Diabetes Page 2 of 230 ABSTRACT The composition of the gastrointestinal (GI) microbiota and associated metabolites changes dramatically with diet and the development of obesity. Although many correlations have been described, specific mechanistic links between these changes and glucose homeostasis remain to be defined. Here we show that blood and intestinal levels of the microbiota-produced N-formyl peptide, formyl-methionyl-leucyl-phenylalanine (fMLF), are elevated in high fat diet (HFD)- induced obese mice. Genetic or pharmacological inhibition of the N-formyl peptide receptor Fpr1 leads to increased insulin levels and improved glucose tolerance, dependent upon glucagon- like peptide-1 (GLP-1). Obese Fpr1-knockout (Fpr1-KO) mice also display an altered microbiome, exemplifying the dynamic relationship between host metabolism and microbiota.
    [Show full text]
  • Journal of Translational Medicine Biomed Central
    Journal of Translational Medicine BioMed Central Review Open Access CD177: A member of the Ly-6 gene superfamily involved with neutrophil proliferation and polycythemia vera David F Stroncek*, Lorraine Caruccio and Maria Bettinotti Address: From the Department of Transfusion Medicine, Warren G. Magnuson Clinical Center, National Institutes of Health, Bethesda, MD, USA Email: David F Stroncek* - [email protected]; Lorraine Caruccio - [email protected]; Maria Bettinotti - [email protected] * Corresponding author Published: 29 March 2004 Received: 22 December 2003 Accepted: 29 March 2004 Journal of Translational Medicine 2004, 2:8 This article is available from: http://www.translational-medicine.com/content/2/1/8 © 2004 Stroncek et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. CD177PRV-1NB1neutrophilspolycythemia veramyelopoiesis Abstract Genes in the Leukocyte Antigen 6 (Ly-6) superfamily encode glycosyl-phosphatidylinositol (GPI) anchored glycoproteins (gp) with conserved domains of 70 to 100 amino acids and 8 to 10 cysteine residues. Murine Ly-6 genes encode important lymphocyte and hematopoietic stem cell antigens. Recently, a new member of the human Ly-6 gene superfamily has been described, CD177. CD177 is polymorphic and has at least two alleles, PRV-1 and NB1. CD177 was first described as PRV-1, a gene that is overexpressed in neutrophils from approximately 95% of patients with polycythemia vera and from about half of patients with essential thrombocythemia. CD177 encodes NB1 gp, a 58–64 kD GPI gp that is expressed by neutrophils and neutrophil precursors.
    [Show full text]