DOCSLIB.ORG

Pax6 Maintains Pancreatic Beta-Cell Identity by Repressing Alternative Islet Cell Genes

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Pax6 maintains pancreatic beta-cell identity by repressing alternative islet cell genes Legends to Supplemental Figures and Tables Supplementary Figure S1: Expression of β cell transcription factors in islets of db/db mice. Expression level of Nkx6.1, MafA and Pdx1 mRNA as a function of blood glucose levels in db/db and control mice. The mice used for this analysis are the same as in figure 1E. Note the early and deeper drop in MafA expression suggesting greater sensitivity to high blood glucose levels as compared with the other factors. Supplementary Figure S2: Pax6 deletion in adult β cells. A. Representative images of islets from Pax6lox/lox (lox/lox) and MIP-CreER; Pax6lox/lox (βPAX6) mice stained for PAX6 (green) and insulin (blue). Mice are 3 months old, 1 week after injection of tamoxifen or vehicle. Original magnification 400x. B. Number of Pax6 positive β cells in Pax6lox/lox mice (no Cre), MIP-CreER; Pax6lox/lox; Rosa26-LSL-YFP mice without tamoxifen (no TM), and MIP- CreER; Pax6lox/lox; Rosa26-LSL-YFP mice 1 week after tamoxifen injection (βPAX6). n=3 for each group of mice. C. Pax6 mRNA in sorted YFP+ β cells from MIP-CreER; Rosa26-LSL-YFP (YFP, n=5) and MIP-CreER; Pax6lox/lox; Rosa26-LSL-YFP (βPAX6, n=3) mice. Mice were 6 weeks old, 1 week after tamoxifen injection. D. Measurements of blood glucose following tamoxifen (TM) injection in an additional cohort of βPAX6 mice showing development of severe hyperglycemia. n=24 lox/lox and 23 βPAX6 mice. ***P < 0.001 as determined by 2-tailed Student’s t-test. Supplemental Figure S3: Correlation between blood ketones and insulin or glucagon in βPAX6 mice. Blood ketones in βPAX6 mice are plotted against plasma insulin (A) and plasma glucagon (B). Note that the highest levels of ketone bodies were observed in hyperglucagonemic mice. Supplementary Figure S4: TUNEL staining for detection of cell death in βPAX6 islets. Pancreatic sections from lox/lox and βPAX6 mice at different time points after tamoxifen injection as indicated, stained for insulin (red), TUNEL (green) and DNA (blue). Original magnification 400x. Supplemental Figure S5: Immunostaining of islet hormones following Pax6 deletion. Immunohistochemical staining of pancreatic sections from control and βPAX6 mice for chromogranin A, glucagon, somatostatin, pancreatic polypeptide and ghrelin. βPAX6 mice were 1 week or 1 month after tamoxifen. Ghrelin cells appeared shortly after Pax6 deletion, while expansion of glucagon and somatostatin cells was seen only 1 month after deletion. Original magnification 400x. Supplemental Figure S6: Ultrastructural resemblance of granules in βPAX6 islet cells and in gastric ghrelin cells. Left panel, EM images of cells from a βPAX6 islet, 2 months after tamoxifen administration. Original magnification 1000x or 3000x (indicated). Right panel, EM images of gastric ghrelin cells, from Kojima et al. (with permission). Image C is presented in main figure 3E and is shown here again for comparison with the gastric ghrelin cells. Supplemental Figure S7: Origins of somatostatin and pancreatic polypeptide-expressing cells in βPAX6 mice. Pax6-deleted β cells do not express detectable somatostatin or pancreatic polypeptide. Staining for somatostatin or pancreatic polypeptide (red), YFP (green) and insulin (blue). Original magnification 400x. Supplemental Figure S8: Changes in protein level of key β cell factors following Pax6 ablation. A. Representative islets stained for insulin and for β cell specific markers that were differentially expressed in βPAX6 as observed by RNA-seq. Maf- A and Glut2 were most dramatically reduced in βPAX6 islets. Original magnification 400x. B. Elevation of Ngn3 protein in Pax6-deficient β cells. Immunohistochemistry of Ngn3 in lox/lox and βPAX6 islets, 1 week after tamoxifen. The pancreas of an embryonic day 14.5 mouse serves as a positive control. Original magnification 400x. Supplemental Table S1 Expression levels of β cell genes following Pax6 deletion, divided into functional categories. Upregulated genes are highlighted in red and downregulated are in green. FC=fold change. We considered FC of 1.5 and FDR<0.05 as significant change in expression. Supplemental Table S2 Expression levels of specific and general transcription factors in β cells, obtained from RNA-seq analysis of sorted beta cells from βPAX6 mice as described in Figure 5. We considered FC of 1.5 and FDR<0.05 as significant change in expression. Supplemental Table S3 List of genes significantly changed during maturation of mouse beta cells between post-natal day 18 and p35. RNA was extracted from sorted beta cells. Supplemental Table S4 A combined list of all Pax6 bound sites obtained from ChIP-seq analysis on min6 cells, and their association with histone marks (H3K27ac, H3K4me1, H3K27me3, H3K9ac) and TF maps (Nkx6-1, Foxa2, Pdx1, MafA, NeuroD1) as well as expression data from RNAseq analysis on βPAX6 mice (1-3). Pax6 ChIP-seq was done in three replicates and all peaks were combined for our analyses. For full description of integrated analysis see second and third tab in the S4 file. Supplemental Table S5 A short list obtained from table S4, showing central islet genes that are directly regulated by Pax6 including details of nearby Pax6 binding sites, additional previously published TFs binding sites and histone marks as well as differential expression data from βPAX6 RNA-seq. Supplemental Table S6 A combined list of all PAX6 bound sites obtained from ChIP-seq analysis on EndoC-betaH2 cells, and their association with histone marks (H3K27ac, H3K4me1, H3K4me3, H3K27me3) and TF maps (PDX1, NKX2-2, NKX6-1, FOXA2, MAFB) performed on human islets or sorted beta cells (4, 5). Pax6 ChIP-seq was done in two replicates and all peaks were combined for our analyses. For full description of integrated analysis see 2nd and 3rd tab in the S6 file. For a list of common genes bound by Pax6 in Min6 and EndoC cells, see 4th tab. Supplemental Table S7 Primary antibodies used for immunohistochemistry, including their dilutions and commercial details, as well as sequence of primers used for qPCR in this study and for amplifying mouse genomic regions cloned into the luciferase reporter vector. Supplemental References 1. Avrahami D, Li C, Zhang J, Schug J, Avrahami R, Rao S, Stadler MB, Burger L, Schubeler D, Glaser B, et al. Aging-Dependent Demethylation of Regulatory Elements Correlates with Chromatin State and Improved beta Cell Function. Cell Metab. 2015;22(4):619-32. 2. Taylor BL, Liu FF, and Sander M. Nkx6.1 is essential for maintaining the functional state of pancreatic beta cells. Cell Rep. 2013;4(6):1262-75. 3. Tennant BR, Robertson AG, Kramer M, Li L, Zhang X, Beach M, Thiessen N, Chiu R, Mungall K, Whiting CJ, et al. Identification and analysis of murine pancreatic islet enhancers. Diabetologia. 2013;56(3):542-52. 4. Bramswig NC, Everett LJ, Schug J, Dorrell C, Liu C, Luo Y, Streeter PR, Naji A, Grompe M, and Kaestner KH. Epigenomic plasticity enables human pancreatic alpha to beta cell reprogramming. J Clin Invest. 2013;123(3):1275-84. 5. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, Akerman I, Tena JJ, Moran I, Gomez-Marin C, van de Bunt M, et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk- associated variants. Nature genetics. 2014;46(2):136-43. 1.8 1.6 1.4 1.2 WT 1 0.8 db/db-4W 0.6 db/db-5-7W 0.4 db/db-3M Nkx6.1 relave expression 0.2 0 0 100 200 300 400 500 600 700 Blood glucose (mg/dL) 1.6 1.4 1.2 1 WT 0.8 db/db-4W 0.6 db/db-5-7W 0.4 MafA relave expression db/db-3M 0.2 0 0 100 200 300 400 500 600 700 Blood glucose (mg/dL) 2 1.8 1.6 1.4 1.2 WT 1 db/db-4W 0.8 db/db-5-7W 0.6 Pdx1 relave expression 0.4 db/db-3M 0.2 0 0 100 200 300 400 500 600 700 Blood glucose (mg/dL) Supplemental Figure S1 A lox/lox No TM βPAX6 INSULIN PAX6 PAX6 50 μm B C D 100 1.2 700 lox/lox βPAX6 ) *** 600 *** 80 1 dL *** 500 *** 0.8 *** 60 400 0.6 40 300 0.4 *** 200 20 Pax6 HprtRNA / 0.2 Blood glucose (mg/ Pax6+ / Ins+ cells (%) 100 0 0 0 0 50 100 150 200 Days aer TM injecFon Supplemental Figure S2 A B 7 8 6 7 ) 6 5 mM 5 4 4 3 3 2 3-OH-butyrate ( 3-OH-butyrate (mM) 2 1 1 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0 20 40 60 80 100 120 140 160 180 Plasma insulin (ng/ml) Plasma glucagon (pg) Supplemental Figure S3 lox/lox βPAX6 – 12 days βPAX6 – 7 months lox/lox βPAX6 – 6 months βPAX6 – 6 months 20 μm INSULIN TUNEL DAPI Supplemental Figure S4 Control βPAX6-1 week βPAX6-1 month Chromogranin 20 μm Glucagon Somatostatin Pancreaticpolypeptide Ghrelin Supplemental Figure S5 βPAX6 islets Oxyntic gland in the stomach A D 2 μm E μm x1000 2 B C 2 μm x1000 1 μm x3000 0.5 μm Supplemental Figure S6 YFP INSULIN SOMATOSTATIN YFP INSULIN PANCREATIC POLYPEPTIDE 1 week 50 μm 6 months Supplemental Figure S7 A PDX1 MafA Nkx-6.1 Nkx-2.2 GLUT2 lox/lox INSULIN PAX6 β 50 μm B lox/lox E14.5 βPAX6 βPAX6 NGN3 20 μm Supplemental Figure S8 Supplementary table S1 Insulin Secretion Gene Full name FC FDR Control βPAX6 Acox2 acyl-Coenzyme A oxidase 2, branched chain 2.43 0.839 1.00 1.90 Acss1 acyl-CoA synthetase short-chain family member 1 3.31 0.612 1.21 2.13 Acss3 acyl-CoA synthetase short-chain family member 3 2.73 0.710 1.21 2.42 Aldoa aldolase A, fructose-bisphosphate 1.16 0.822 1944.54 2064.87 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit C3 (subunit Atp5G3 9) 1.39 0.312 778.06 1031.06 Cox4I1 cytochrome c oxidase subunit IV isoform 1 1.46 0.167 611.77 823.55 Gpi1 glucose phosphate isomerase 1 1.37 0.450 412.57 496.70 Nnt nicotinamide nucleotide transhydrogenase -1.93 0.009 952.05 456.20 Pcsk1 proprotein
Recommended publications
  • Resolving Transcriptional States and Predicting Lineages in the Annelid Capitella Teleta Using 1 Single-Cell Rnaseq 2 3 Abhinav

    Resolving Transcriptional States and Predicting Lineages in the Annelid Capitella Teleta Using 1 Single-Cell Rnaseq 2 3 Abhinav

    bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.342709; this version posted October 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Resolving transcriptional states and predicting lineages in the annelid Capitella teleta using 2 single-cell RNAseq 3 4 Abhinav Sur1 and Néva P. Meyer2* 5 6 1Unit on Cell Specification and Differentiation, National Institute of Child Health and Human 7 Development (NICHD), Bethesda, Maryland, USA, 20814 8 9 2Department of Biology, Clark University, 950 Main Street, Worcester, Massachusetts, USA, 10 01610. 11 12 13 [email protected] 14 [email protected] 15 16 *Corresponding author 17 18 19 20 21 Keywords: neurogenesis, single-cell RNAseq, annelid, cell type, differentiation trajectory, 22 pseudotime, RNA velocity, gene regulatory network. 23 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.342709; this version posted October 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 24 Abstract 25 Evolution and diversification of cell types has contributed to animal evolution. However, gene 26 regulatory mechanisms underlying cell fate acquisition during development remains largely 27 uncharacterized in spiralians. Here we use a whole-organism, single-cell transcriptomic approach 28 to map larval cell types in the annelid Capitella teleta at 24- and 48-hours post gastrulation 29 (stages 4 and 5).
  • Strategies to Increase ß-Cell Mass Expansion

    Strategies to Increase ß-Cell Mass Expansion

    This electronic thesis or dissertation has been downloaded from the King’s Research Portal at https://kclpure.kcl.ac.uk/portal/ Strategies to increase -cell mass expansion Drynda, Robert Lech Awarding institution: King's College London The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without proper acknowledgement. END USER LICENCE AGREEMENT Unless another licence is stated on the immediately following page this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ You are free to copy, distribute and transmit the work Under the following conditions: Attribution: You must attribute the work in the manner specified by the author (but not in any way that suggests that they endorse you or your use of the work). Non Commercial: You may not use this work for commercial purposes. No Derivative Works - You may not alter, transform, or build upon this work. Any of these conditions can be waived if you receive permission from the author. Your fair dealings and other rights are in no way affected by the above. Take down policy If you believe that this document breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 Strategies to increase β-cell mass expansion A thesis submitted by Robert Drynda For the degree of Doctor of Philosophy from King’s College London Diabetes Research Group Division of Diabetes & Nutritional Sciences Faculty of Life Sciences & Medicine King’s College London 2017 Table of contents Table of contents .................................................................................................
  • 1 Evidence for Gliadin Antibodies As Causative Agents in Schizophrenia

    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.
  • Downloaded from the National Database for Autism Research (NDAR)

    Downloaded from the National Database for Autism Research (NDAR)

    International Journal of Molecular Sciences Article Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions Elizabeth C. Lee y and Valerie W. Hu * Department of Biochemistry and Molecular Medicine, The George Washington University, School of Medicine and Health Sciences, Washington, DC 20037, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-202-994-8431 Current address: W. Harry Feinstone Department of Molecular Microbiology and Immunology, y Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. Received: 25 August 2020; Accepted: 17 September 2020; Published: 19 September 2020 Abstract: Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group.
  • Core Transcriptional Regulatory Circuitries in Cancer

    Core Transcriptional Regulatory Circuitries in Cancer

    Oncogene (2020) 39:6633–6646 https://doi.org/10.1038/s41388-020-01459-w REVIEW ARTICLE Core transcriptional regulatory circuitries in cancer 1 1,2,3 1 2 1,4,5 Ye Chen ● Liang Xu ● Ruby Yu-Tong Lin ● Markus Müschen ● H. Phillip Koeffler Received: 14 June 2020 / Revised: 30 August 2020 / Accepted: 4 September 2020 / Published online: 17 September 2020 © The Author(s) 2020. This article is published with open access Abstract Transcription factors (TFs) coordinate the on-and-off states of gene expression typically in a combinatorial fashion. Studies from embryonic stem cells and other cell types have revealed that a clique of self-regulated core TFs control cell identity and cell state. These core TFs form interconnected feed-forward transcriptional loops to establish and reinforce the cell-type- specific gene-expression program; the ensemble of core TFs and their regulatory loops constitutes core transcriptional regulatory circuitry (CRC). Here, we summarize recent progress in computational reconstitution and biologic exploration of CRCs across various human malignancies, and consolidate the strategy and methodology for CRC discovery. We also discuss the genetic basis and therapeutic vulnerability of CRC, and highlight new frontiers and future efforts for the study of CRC in cancer. Knowledge of CRC in cancer is fundamental to understanding cancer-specific transcriptional addiction, and should provide important insight to both pathobiology and therapeutics. 1234567890();,: 1234567890();,: Introduction genes. Till now, one critical goal in biology remains to understand the composition and hierarchy of transcriptional Transcriptional regulation is one of the fundamental mole- regulatory network in each specified cell type/lineage.
  • From Inverse Agonism to 'Paradoxical Pharmacology' Richard A

    From Inverse Agonism to 'Paradoxical Pharmacology' Richard A

    International Congress Series 1249 (2003) 27-37 From inverse agonism to 'Paradoxical Pharmacology' Richard A. Bond*, Kenda L.J. Evans, Zsirzsanna Callaerts-Vegh Department of Pharmacological and Pharmaceutical Sciences, University of Houston, 521 Science and Research Bldg 2, 4800 Caltioun, Houston, TX 77204-5037, USA Received 16 April 2003; accepted 16 April 2003 Abstract The constitutive or spontaneous activity of G protein-coupled receptors (GPCRs) and compounds acting as inverse agonists is a recent but well-established phenomenon. Dozens of receptor subtypes for numerous neurotransmitters and hormones have been shown to posses this property. However, do to the apparently low percentage of receptors in the spontaneously active state, the physiologic relevance of these findings remains questionable. The possibility that the reciprocal nature of the effects of agonists and inverse agonists may extend to cellular signaling is discussed, and that this may account for the beneficial effects of certain p-adrenoceptor inverse agonists in the treatment of heart failure. © 2003 Elsevier Science B.V. All rights reserved. Keywords. Inverse agonism; GPCR; Paradoxical pharmacology 1. Brief history of inverse agonism at G protein-coupled receptors For approximately three-quarters of a century, ligands that interacted with G protein- coupled receptors (GPCRs) were classified either as agonists or antagonists. Receptors were thought to exist in a single quiescent state that could only induce cellular signaling upon agonist binding to the receptor to produce an activated state of the receptor. In this model, antagonists had no cellular signaling ability on their own, but did bind to the receptor and prevented agonists from being able to bind and activate the receptor.
  • Molecular Mechanisms Involved Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis

    Molecular Mechanisms Involved Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis

    Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2010 Molecular Mechanisms Involved Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis Ryan Fassnacht Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Physiology Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/2246 This Thesis is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. Ryan C. Fassnacht 2010 All Rights Reserved Molecular Mechanisms Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. by Ryan Christopher Fassnacht, B.S. Hampden Sydney University, 2005 M.S. Virginia Commonwealth University, 2010 Director: Valeria Mas, Ph.D., Associate Professor of Surgery and Pathology Division of Transplant Department of Surgery Virginia Commonwealth University Richmond, Virginia July 9, 2010 Acknowledgement The Author wishes to thank his family and close friends for their support. He would also like to thank the members of the molecular transplant team for their help and advice. This project would not have been possible with out the help of Dr. Valeria Mas and her endearing
  • Supplemental Table 1 Enriched Genes in Cortical Astrocytes from Aged

    Supplemental Table 1 Enriched Genes in Cortical Astrocytes from Aged

    Supplemental Table 1 Enriched genes in cortical astrocytes from aged and young-adult mice * Genes were present in the astrocyte module from the WGCNA analysis, and contains astrocyte enriched genes compared to microglia and oligodendrocytes # = Fold change of aged astrocyte expression over the average expression of all analyzed samples (microglia, astrocytes: young, old, with and without myelin contamination) $ ; aged = genes only present in the aged astrocyte top 1000 list (used to compare with lists from Cahoy, Lovatt, Doyle; see Fig. 4B), all = genes present in all astrocyte top 1000 lists Gene Symbol* Aged astr. (log2) Young astr.(log2) FC (aged/ aver.)# Location Ptprz1 15.37 15.02 18.76 Plasma Membrane Slc7a10 14.49 14.44 18.28 Plasma Membrane Gjb6 15.13 14.42 18.18 Plasma Membrane Dclk1 14.63 14.28 17.18 unknown Hes5 15.69 15.55 16.94 Nucleus Fgfr3 15.27 14.46 16.54 Plasma Membrane Entpd2 13.85 13.56 15.92 Cytoplasm Grin2c 14.93 14.87 15.75 Plasma Membrane Slc1a2 15.51 15.39 15.58 Plasma Membrane Fjx1 14.36 13.98 14.52 Extracellular Space Slc6a1 14.20 14.16 14.47 Plasma Membrane Kcnk1 12.93 13.49 14.43 Plasma Membrane Ppap2b 16.16 16.10 14.37 Plasma Membrane Fam20a 14.48 14.72 14.00 Extracellular Space Dbx2 13.68 13.32 13.99 Nucleus Itih3 13.93 13.93 13.94 Extracellular Space Htra1 17.12 16.91 13.92 Extracellular Space Atp1a2 14.59 14.48 13.73 Plasma Membrane Scg3 15.71 15.72 13.68 Extracellular Space F3 15.59 15.08 13.51 Plasma Membrane Mmd2 14.22 14.60 13.50 unknown Nrcam 13.73 13.88 13.47 Plasma Membrane Cldn10a 13.37 13.57 13.46
  • Serum Levels of Spexin and Kisspeptin Negatively Correlate with Obesity and Insulin Resistance in Women

    Serum Levels of Spexin and Kisspeptin Negatively Correlate with Obesity and Insulin Resistance in Women

    Physiol. Res. 67: 45-56, 2018 https://doi.org/10.33549/physiolres.933467 Serum Levels of Spexin and Kisspeptin Negatively Correlate With Obesity and Insulin Resistance in Women P. A. KOŁODZIEJSKI1, E. PRUSZYŃSKA-OSZMAŁEK1, E. KOREK4, M. SASSEK1, D. SZCZEPANKIEWICZ1, P. KACZMAREK1, L. NOGOWSKI1, P. MAĆKOWIAK1, K. W. NOWAK1, H. KRAUSS4, M. Z. STROWSKI2,3 1Department of Animal Physiology and Biochemistry, Poznan University of Life Sciences, Poznan, Poland, 2Department of Hepatology and Gastroenterology & The Interdisciplinary Centre of Metabolism: Endocrinology, Diabetes and Metabolism, Charité-University Medicine Berlin, Berlin, Germany, 3Department of Internal Medicine, Park-Klinik Weissensee, Berlin, Germany, 4Department of Physiology, Karol Marcinkowski University of Medical Science, Poznan, Poland Received August 18, 2016 Accepted June 19, 2017 On-line November 10, 2017 Summary Corresponding author Spexin (SPX) and kisspeptin (KISS) are novel peptides relevant in P. A. Kolodziejski, Department of Animal Physiology and the context of regulation of metabolism, food intake, puberty and Biochemistry, Poznan University of Life Sciences, Wolynska Street reproduction. Here, we studied changes of serum SPX and KISS 28, 60-637 Poznan, Poland. E-mail: [email protected] levels in female non-obese volunteers (BMI<25 kg/m2) and obese patients (BMI>35 kg/m2). Correlations between SPX or Introduction KISS with BMI, McAuley index, QUICKI, HOMA IR, serum levels of insulin, glucagon, leptin, adiponectin, orexin-A, obestatin, Kisspeptin (KISS) and spexin (SPX) are peptides ghrelin and GLP-1 were assessed. Obese patients had lower SPX involved in regulation of body weight, metabolism and and KISS levels as compared to non-obese volunteers (SPX: sexual functions. In 2014, Kim and coworkers showed that 4.48±0.19 ng/ml vs.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • Modeling the Phenotype of Spinal Muscular Atrophy by the Direct Conversion of Human Fibroblasts to Motor Neurons

    Modeling the Phenotype of Spinal Muscular Atrophy by the Direct Conversion of Human Fibroblasts to Motor Neurons

    www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 7), pp: 10945-10953 Research Paper Modeling the phenotype of spinal muscular atrophy by the direct conversion of human fibroblasts to motor neurons Qi-Jie Zhang1,*, Jin-Jing Li1,*, Xiang Lin1, Ying-Qian Lu1, Xin-Xin Guo1, En-Lin Dong1, Miao Zhao1, Jin He1, Ning Wang1,2 and Wan-Jin Chen1,2 1 Department of Neurology and Institute of Neurology, First Affiliated Hospital, Fujian Medical University, Fuzhou, China 2 Fujian Key Laboratory of Molecular Neurology, Fuzhou, China * These authors have contributed equally to this work Correspondence to: Wan-Jin Chen, email: [email protected] Keywords: direct reprogramming; fibroblast; induced motor neuron; spinal muscular atrophy Received: June 08, 2016 Accepted: November 22, 2016 Published: January 13, 2017 ABSTRACT Spinal muscular atrophy (SMA) is a lethal autosomal recessive neurological disease characterized by selective degeneration of motor neurons in the spinal cord. In recent years, the development of cellular reprogramming technology has provided an alternative and effective method for obtaining patient-specific neuronsin vitro. In the present study, we applied this technology to the field of SMA to acquire patient- specific induced motor neurons that were directly converted from fibroblasts via the forced expression of 8 defined transcription factors. The infected fibroblasts began to grow in a dipolar manner, and the nuclei gradually enlarged. Typical Tuj1-positive neurons were generated at day 23. After day 35, induced neurons with multiple neurites were observed, and these neurons also expressed the hallmarks of Tuj1, HB9, ISL1 and CHAT. The conversion efficiencies were approximately 5.8% and 5.5% in the SMA and control groups, respectively.
  • Regulation of Adult Neurogenesis in Mammalian Brain

    Regulation of Adult Neurogenesis in Mammalian Brain

    International Journal of Molecular Sciences Review Regulation of Adult Neurogenesis in Mammalian Brain 1,2, 3, 3,4 Maria Victoria Niklison-Chirou y, Massimiliano Agostini y, Ivano Amelio and Gerry Melino 3,* 1 Centre for Therapeutic Innovation (CTI-Bath), Department of Pharmacy & Pharmacology, University of Bath, Bath BA2 7AY, UK; [email protected] 2 Blizard Institute of Cell and Molecular Science, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK 3 Department of Experimental Medicine, TOR, University of Rome “Tor Vergata”, 00133 Rome, Italy; [email protected] (M.A.); [email protected] (I.A.) 4 School of Life Sciences, University of Nottingham, Nottingham NG7 2HU, UK * Correspondence: [email protected] These authors contributed equally to this work. y Received: 18 May 2020; Accepted: 7 July 2020; Published: 9 July 2020 Abstract: Adult neurogenesis is a multistage process by which neurons are generated and integrated into existing neuronal circuits. In the adult brain, neurogenesis is mainly localized in two specialized niches, the subgranular zone (SGZ) of the dentate gyrus and the subventricular zone (SVZ) adjacent to the lateral ventricles. Neurogenesis plays a fundamental role in postnatal brain, where it is required for neuronal plasticity. Moreover, perturbation of adult neurogenesis contributes to several human diseases, including cognitive impairment and neurodegenerative diseases. The interplay between extrinsic and intrinsic factors is fundamental in regulating neurogenesis. Over the past decades, several studies on intrinsic pathways, including transcription factors, have highlighted their fundamental role in regulating every stage of neurogenesis. However, it is likely that transcriptional regulation is part of a more sophisticated regulatory network, which includes epigenetic modifications, non-coding RNAs and metabolic pathways.