1 Supplemental Methods in Vivo Mouse Studies

Total Page:16

File Type:pdf, Size:1020Kb

1 Supplemental Methods in Vivo Mouse Studies Supplemental Methods In vivo mouse studies – A subset of WT and qv4J animals were administered the STAT3 inhibitor S3I-201 in-vivo at 20mg/kg daily intraperitoneal for 14 days (1). Transaortic constriction (TAC) was performed on a subset of WT mice for 6 weeks to induce chronic pressure-overload and heart failure, as described (1, 2). Cardiac performance was assessed prior to surgery and at 6 weeks post TAC using Vevo 2100 (Visualsonics). Specifically, cardiac function was determined using the MS-400 transducer in the short axis M-mode. For heart extractions, mice were anesthetized with 2.5% gaseous isoflurane and oxygen and sacrificed with confirmation by the absence of response to probing of the lower extremities. Immunoblot analysis – Primary ventricular cardiac fibroblast lysate was analyzed using SDS-PAGE and immunoblotting, as described (1, 3, 4). Briefly, equal protein loading was achieved using standard BCA protein assay protocols and verified by Ponceau staining of immunoblots. The following antibodies were used for immunoblotting: Total STAT3 (1:1000, Cell Signaling, Catalog #: 4904), vimentin (1:1000, Abcam, Catalog # ab92547:) and GAPDH (1:5000, Fitzgerald, Catalog #: 10R-G109A). Immunofluorescence – Cardiac fibroblasts were seeded in 35 mm Matex glass bottom dishes until ~50-70% confluency. Cells were then fixed in 4% paraformaldehyde and permeabilized with TritonX-100 for 10 mins. Non-specific binding was blocked using blocking buffer comprised of 1.5% bovine serum albumin (BSA), 3% goat serum in 1xPBS ° (Invitrogen) overnight at 4 C. Primary antibodies [βIV-spectrin (1:100, Millipore, Clone# N393/76), total STAT3 (1:100, Cell Signaling, Catalog#: 4904), plakoglobin/anti-gamma catenin (1:400, Abcam, Catalog #: ab15153)] were prepared in blocking buffer and added for overnight incubation at 4°C. After washing, secondary Alexa Flour 568 anti-mouse (1:200, Invitrogen, Catalog #: A11004), 466 anti-rabbit (1:200, Invitrogen, Catalog #: A11008), and 633 phalloidin (1:200, Invitrogen, Catalog #: A22284) antibodies were prepared in blocking buffer and incubated for 2 hrs at 4°C. After washing, DAPI (Vecta Laboratories) was applied and dishes were stored at 4°C in the dark until they were imaged using confocal microscopy. Multiple random cells were selected from each field/preparation for analysis. STAT3 localization analysis was conducted by extracting color bands (blue and green) from images and removing background noise with a 3x3 median filter in MATLAB. Threshold values were set, and binary images were generated, as described (3). Histology – Whole hearts were fixed in 10% formalin, trimmed to reveal ventricles, processed into paraffin, and serially sectioned using microtome into 5 microns. Longitudinal heart sections were then stained using Masson’s Trichrome to evaluate amounts of interstitial and perivascular fibrosis. Percent fibrosis was quantified across the entire longitudinal section (not including atria) using custom software in MATLAB. Quantitative real-time PCR (qPCR) – Total RNA from mouse or human primary cardiac fibroblasts was extracted with TRIzol Reagent following manufacturer’s instructions. RNA 1 concentration was measured using Agilent 2100 Bioanalyzer (Aligent Technologies). SuperScript III reverse transcriptase VILO cDNA Synthesis kit (Invitrogen) was used for the first-strand complementary DNA synthesis (20ng/μL) (primers for mouse and human targets provided in Tables S4 and S5). qPCR reactions were performed in triplicate on cDNA samples in 96-well optical plates with SYBR Green Gene Expression Assays and SYBR Green Universal PCR Master Mix (Invitrogen). qPCR was performed at 95°C for 3 mins., 40 cycles of 95°C for 15 s. and 60°C for 1 min. on Applied Biosystems 7900HT Fast Real-Time PCR System or StepOnePlus Real-Time PCR System (Life Technologies). qPCR data were analyzed using relative standard curve method and 2 delta Ct was used to calculate fold changes in relative gene expression. qPCR products were confirmed by melt-curve analysis, amplicon length, and DNA sequencing. Rpl-7 levels were used as a normalization control. Experiments were conducted in technical triplicates. RNA sequencing - RNA was isolated from male and female wildtype (WT) and qv4J mouse primary cardiac fibroblasts and sequenced with Ocean Ridge BioSciences (Deerfield Beach, FL). Mice were sacrificed using isoflurane overdose, hearts were immediately excised, and cardiac fibroblasts were isolated and cultured at passage 1 conditions as previously described. Total RNA was isolated from the cardiac fibroblasts using the TRI Reagent® (Molecular Research Center). Total RNA was quantified by O.D. measurement and assessed for quality on a 1% agarose – 2% formaldehyde gel. The RNA was then treated with RNase free DNase I (Epicentre) and re-purified on RNeasy MinElute columns (Qiagen). The newly digested RNA samples were then assessed for quality by chip-based capillary electrophoresis on Agilent 2100 Bioanalyzer RNA 6000 Pico assays (Agilent Technologies). Amplified cDNA libraries suitable for sequencing were prepared from 200 ng of DNA-free total RNA using the Universal Plus mRNA-Seq Library Prep Kit (NuGEN Technologies, Inc.). The quality and size distribution of the amplified libraries were determined by chip-based capillary electrophoresis on Agilent 2100 Bioanalyzer High Sensitivity DNA assays (Agilent Technologies). Libraries were quantified using the Takara Library Quantification Kit. The libraries were pooled at equimolar concentrations and diluted prior to loading onto a flow-cell on an Illumina cBot. The libraries were extended and bridge amplified to create sequence clusters. The flow cell was transferred to the Illumina HiSeq 4000 instrument and sequenced using 150 nt paired-end reads plus a single index read. Quality-filtered and base-trimmed reads were used for alignment. Sequence alignment was performed using HISAT2 version 2.0.5. The read summarization program featureCounts2 version 1.5.1 was used for exon- and gene- level counting. Normalized RPKM values were calculated from the raw featureCounts read, and were then filtered to retain a list of genes with a minimum of approximately 50 mapped reads in 25% or more samples. The threshold of 50 mapped reads is considered the Reliable Quantification Threshold. The resulting gene list with calculated fold changes was analyzed using Ingenuity Pathway Analysis [QIAGEN (5)]. The software was used to identify highly indicated upstream regulators and disease and function networks. 2 Supplemental References 1. Unudurthi SD, et al. betaIV-Spectrin regulates STAT3 targeting to tune cardiac response to pressure overload. J Clin Invest. 2018;128(12):5561-72. 2. Glynn P, et al. Voltage-Gated Sodium Channel Phosphorylation at Ser571 Regulates Late Current, Arrhythmia, and Cardiac Function In Vivo. Circulation. 2015;132(7):567-77. 3. Hund TJ, et al. A betaIV spectrin/CaMKII signaling complex is essential for membrane excitability in mice. J Clin Invest. 2010;120(10):3508-19. 4. Hund TJ, et al. beta(IV)-Spectrin regulates TREK-1 membrane targeting in the heart. Cardiovasc Res. 2014;102(1):166-75. 5. Kramer A, Green J, Pollard J, Jr., and Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30(4):523-30. 3 Supplemental Table 1: Echocardiographic features in wildtype (WT) and qv4J mice at baseline and WT mice subjected to 6 weeks of transaortic constriction (TAC) WT qv4J WT TAC (N=5) (N=5) (N=5) HR (bpm) 446.71±11.83 392.14±23.24 448.46±8.78 EF (%) 63.24±0.60 49.49±0.85 34.89±2.20 LVID,d (mm) 2.94±0.03 3.34± 0.08* 4.28±0.12*# LVID,s (mm) 2.02±0.07 2.53±0.07* 3.58±0.13*# FS (%) 33.24±0.37 24.34±0.47* 16.55±1.19*# LVAW,d (mm) 0.75±0.02 0.62±0.03 0.95±0.07*# LVAW,s (mm) 1.19±0.04 0.83±0.05* 1.16±0.09# LVPW,d (mm) 0.78±0.17 0.632±0.06 0.80±0.10 LVPW,s (mm) 0.96±0.07 0.71±0.03 0.91±0.09 HR = heart rate; EF = ejection fraction; LVID,d = left ventricular inner chamber diameter in diastole; LVID,s = left ventricular inner chamber diameter in systole; FS = fractional shortening; LVAW,d = LV anterior wall thickness in diastole; LVAW,s = LV anterior wall thickness in systole; LVPW,d = LV posterior wall thickness in diastole; LVPW,s = LV posterior wall thickness in systole; *P<0.05 vs. WT #P<0.05 vs. qv4J Supplemental Table 2: Differentially regulated genes in qv4J fibroblasts with more than twofold difference from wildtype (WT) Gene Entrez fold change name Description gene ID from WT Serpina3n serine (or cysteine) peptidase inhibitor, clade A, member 3N 20716 14.74341 Ang2 angiogenin, ribonuclease A family, member 2 11731 14.61122** Lrrn4 leucine rich repeat neuronal 4 320974 12.29352 Iglj1 immunoglobulin lambda joining 1 404737 11.31642 Angptl7 angiopoietin-like 7 654812 10.29877 Upk3b uroplakin 3B 100647 8.24076 4 Rnase2a ribonuclease, RNase A family, 2A (liver, eosinophil-derived 93726 7.21412* neurotoxin) Atp10d ATPase, class V, type 10D 231287 5.85188* Traj32 T cell receptor alpha joining 32 100124357 5.80643* Erdr1 erythroid differentiation regulator 1 170942 5.74261** Col28a1 collagen, type XXVIII, alpha 1 213945 5.49104* Tagap T cell activation Rho GTPase activating protein 72536 5.37396* Rtn4rl2 reticulon 4 receptor-like 2 269295 5.06487* Ighd1-1 immunoglobulin heavy diversity 1-1 777657 5.00956 Rbp4 retinol binding protein 4, plasma 19662 4.927054 C3 complement component 3 12266 4.688854 Serpina3g serine (or cysteine) peptidase inhibitor, clade A, member 3G 20715 4.572000 Scn7a sodium channel, voltage-gated, type VII, alpha 20272 4.526735*
Recommended publications
  • Novel Binding Partners of PBF in Thyroid Tumourigenesis
    NOVEL BINDING PARTNERS OF PBF IN THYROID TUMOURIGENESIS By Neil Sharma A thesis presented to the College of Medical and Dental Sciences at the University of Birmingham for the Degree of Doctor of Philosophy Centre for Endocrinology, Diabetes and Metabolism, School of Clinical and Experimental Medicine August 2013 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. SUMMARY Thyroid cancer is the most common endocrine cancer, with a rising incidence. The proto-oncogene PBF is over-expressed in thyroid tumours, and the degree of over-expression is directly linked to patient survival. PBF causes transformation in vitro and tumourigenesis in vivo, with PBF-transgenic mice developing large, macro-follicular goitres, effects partly mediated by the internalisation and repression of the membrane-bound transporters NIS and MCT8. NIS repression leads to a reduction in iodide uptake, which may negatively affect the efficacy of radioiodine treatment, and therefore prognosis. Work within this thesis describes the use of tandem mass spectrometry to produce a list of potential binding partners of PBF. This will aid further research into the pathophysiology of PBF, not just in relation to thyroid cancer but also other malignancies.
    [Show full text]
  • Universidade Estadual De Campinas Instituto De Biologia
    UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE BIOLOGIA VERÔNICA APARECIDA MONTEIRO SAIA CEREDA O PROTEOMA DO CORPO CALOSO DA ESQUIZOFRENIA THE PROTEOME OF THE CORPUS CALLOSUM IN SCHIZOPHRENIA CAMPINAS 2016 1 VERÔNICA APARECIDA MONTEIRO SAIA CEREDA O PROTEOMA DO CORPO CALOSO DA ESQUIZOFRENIA THE PROTEOME OF THE CORPUS CALLOSUM IN SCHIZOPHRENIA Dissertação apresentada ao Instituto de Biologia da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do Título de Mestra em Biologia Funcional e Molecular na área de concentração de Bioquímica. Dissertation presented to the Institute of Biology of the University of Campinas in partial fulfillment of the requirements for the degree of Master in Functional and Molecular Biology, in the area of Biochemistry. ESTE ARQUIVO DIGITAL CORRESPONDE À VERSÃO FINAL DA DISSERTAÇÃO DEFENDIDA PELA ALUNA VERÔNICA APARECIDA MONTEIRO SAIA CEREDA E ORIENTADA PELO DANIEL MARTINS-DE-SOUZA. Orientador: Daniel Martins-de-Souza CAMPINAS 2016 2 Agência(s) de fomento e nº(s) de processo(s): CNPq, 151787/2F2014-0 Ficha catalográfica Universidade Estadual de Campinas Biblioteca do Instituto de Biologia Mara Janaina de Oliveira - CRB 8/6972 Saia-Cereda, Verônica Aparecida Monteiro, 1988- Sa21p O proteoma do corpo caloso da esquizofrenia / Verônica Aparecida Monteiro Saia Cereda. – Campinas, SP : [s.n.], 2016. Orientador: Daniel Martins de Souza. Dissertação (mestrado) – Universidade Estadual de Campinas, Instituto de Biologia. 1. Esquizofrenia. 2. Espectrometria de massas. 3. Corpo caloso.
    [Show full text]
  • Propranolol-Mediated Attenuation of MMP-9 Excretion in Infants with Hemangiomas
    Supplementary Online Content Thaivalappil S, Bauman N, Saieg A, Movius E, Brown KJ, Preciado D. Propranolol-mediated attenuation of MMP-9 excretion in infants with hemangiomas. JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2013.4773 eTable. List of All of the Proteins Identified by Proteomics This supplementary material has been provided by the authors to give readers additional information about their work. © 2013 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable. List of All of the Proteins Identified by Proteomics Protein Name Prop 12 mo/4 Pred 12 mo/4 Δ Prop to Pred mo mo Myeloperoxidase OS=Homo sapiens GN=MPO 26.00 143.00 ‐117.00 Lactotransferrin OS=Homo sapiens GN=LTF 114.00 205.50 ‐91.50 Matrix metalloproteinase‐9 OS=Homo sapiens GN=MMP9 5.00 36.00 ‐31.00 Neutrophil elastase OS=Homo sapiens GN=ELANE 24.00 48.00 ‐24.00 Bleomycin hydrolase OS=Homo sapiens GN=BLMH 3.00 25.00 ‐22.00 CAP7_HUMAN Azurocidin OS=Homo sapiens GN=AZU1 PE=1 SV=3 4.00 26.00 ‐22.00 S10A8_HUMAN Protein S100‐A8 OS=Homo sapiens GN=S100A8 PE=1 14.67 30.50 ‐15.83 SV=1 IL1F9_HUMAN Interleukin‐1 family member 9 OS=Homo sapiens 1.00 15.00 ‐14.00 GN=IL1F9 PE=1 SV=1 MUC5B_HUMAN Mucin‐5B OS=Homo sapiens GN=MUC5B PE=1 SV=3 2.00 14.00 ‐12.00 MUC4_HUMAN Mucin‐4 OS=Homo sapiens GN=MUC4 PE=1 SV=3 1.00 12.00 ‐11.00 HRG_HUMAN Histidine‐rich glycoprotein OS=Homo sapiens GN=HRG 1.00 12.00 ‐11.00 PE=1 SV=1 TKT_HUMAN Transketolase OS=Homo sapiens GN=TKT PE=1 SV=3 17.00 28.00 ‐11.00 CATG_HUMAN Cathepsin G OS=Homo
    [Show full text]
  • Defining Functional Interactions During Biogenesis of Epithelial Junctions
    ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
    [Show full text]
  • Meta-Analysis of Nasopharyngeal Carcinoma
    BMC Genomics BioMed Central Research article Open Access Meta-analysis of nasopharyngeal carcinoma microarray data explores mechanism of EBV-regulated neoplastic transformation Xia Chen†1,2, Shuang Liang†1, WenLing Zheng1,3, ZhiJun Liao1, Tao Shang1 and WenLi Ma*1 Address: 1Institute of Genetic Engineering, Southern Medical University, Guangzhou, PR China, 2Xiangya Pingkuang associated hospital, Pingxiang, Jiangxi, PR China and 3Southern Genomics Research Center, Guangzhou, Guangdong, PR China Email: Xia Chen - [email protected]; Shuang Liang - [email protected]; WenLing Zheng - [email protected]; ZhiJun Liao - [email protected]; Tao Shang - [email protected]; WenLi Ma* - [email protected] * Corresponding author †Equal contributors Published: 7 July 2008 Received: 16 February 2008 Accepted: 7 July 2008 BMC Genomics 2008, 9:322 doi:10.1186/1471-2164-9-322 This article is available from: http://www.biomedcentral.com/1471-2164/9/322 © 2008 Chen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Epstein-Barr virus (EBV) presumably plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC), but the molecular mechanism of EBV-dependent neoplastic transformation is not well understood. The combination of bioinformatics with evidences from biological experiments paved a new way to gain more insights into the molecular mechanism of cancer. Results: We profiled gene expression using a meta-analysis approach. Two sets of meta-genes were obtained. Meta-A genes were identified by finding those commonly activated/deactivated upon EBV infection/reactivation.
    [Show full text]
  • 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.
    [Show full text]
  • Location Analysis of Estrogen Receptor Target Promoters Reveals That
    Location analysis of estrogen receptor ␣ target promoters reveals that FOXA1 defines a domain of the estrogen response Jose´ e Laganie` re*†, Genevie` ve Deblois*, Ce´ line Lefebvre*, Alain R. Bataille‡, Franc¸ois Robert‡, and Vincent Gigue` re*†§ *Molecular Oncology Group, Departments of Medicine and Oncology, McGill University Health Centre, Montreal, QC, Canada H3A 1A1; †Department of Biochemistry, McGill University, Montreal, QC, Canada H3G 1Y6; and ‡Laboratory of Chromatin and Genomic Expression, Institut de Recherches Cliniques de Montre´al, Montreal, QC, Canada H2W 1R7 Communicated by Ronald M. Evans, The Salk Institute for Biological Studies, La Jolla, CA, July 1, 2005 (received for review June 3, 2005) Nuclear receptors can activate diverse biological pathways within general absence of large scale functional data linking these putative a target cell in response to their cognate ligands, but how this binding sites with gene expression in specific cell types. compartmentalization is achieved at the level of gene regulation is Recently, chromatin immunoprecipitation (ChIP) has been used poorly understood. We used a genome-wide analysis of promoter in combination with promoter or genomic DNA microarrays to occupancy by the estrogen receptor ␣ (ER␣) in MCF-7 cells to identify loci recognized by transcription factors in a genome-wide investigate the molecular mechanisms underlying the action of manner in mammalian cells (20–24). This technology, termed 17␤-estradiol (E2) in controlling the growth of breast cancer cells. ChIP-on-chip or location analysis, can therefore be used to deter- We identified 153 promoters bound by ER␣ in the presence of E2. mine the global gene expression program that characterize the Motif-finding algorithms demonstrated that the estrogen re- action of a nuclear receptor in response to its natural ligand.
    [Show full text]
  • Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
    Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen,
    [Show full text]
  • Renalase — a New Marker Or Just a Bystander in Cardiovascular Disease: Clinical and Experimental Data
    Kardiologia Polska 2016; 74, 9: 937–942; DOI: 10.5603/KP.a2016.0095 ISSN 0022–9032 ARTYKUŁ SPECJALNY / STATE-OF-THE-ART REVIEW Renalase — a new marker or just a bystander in cardiovascular disease: clinical and experimental data Dominika Musiałowska, Jolanta Małyszko 2nd Department of Nephrology, Medical University of Bialystok, Bialystok, Poland Dominika Musiałowska was born in 1985 in Bialystok. She graduated the Medical University of Bialystok in 2010 with the average grade of very good. During her studies she was awarded a scholarship from the Minister of Health for her academic results. She successfully completed a short-term fellowship supported by the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) in modulation of sympathetic nerve activity by renal denerva- tion in hypertensive renal transplant patients in the Nephrology Department in Heinrich Heine Universitat Duesseldorf, Germany (October 1, 2012 – March 29, 2013). She became a Doctor of Philosophy in 2014 after a public defence of her manuscript Renalase concentration in hypertensive patients. She works in 2nd Department of Nephrology at the Medical University of Bialystok, Poland. Jolanta Małyszko graduated with merit from the Medical University of Bialystok, Poland. She is a full Professor (2002) and from 2013 a chairman of 2nd Department on Nephrology, Medical University, Bialystok, Poland. Her clinical training was performed in the Intensive Care Unit and Nephrology Department, CHU Rouen, France, Nephrology Department, Heinrich-Heine Uni- versity, Dusseldorf, Germany (ERA-EDTA clinical scholarship), McKennon Hospital, Sioux Falls, South Dakota, United States (invasive cardiology), Kings’ College, London, United Kingdom, and Sourasky Hospital, Israel. During a Japanese Ministry of Education scholarship at Hamamatsu University School of Medicine she defended her PhD thesis on the role of FK 506 in transplanta- tion in 1995.
    [Show full text]
  • Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia.
    [Show full text]
  • Gene Loci Associated with Insulin Secretion in Islets from Nondiabetic Mice
    Gene loci associated with insulin secretion in islets from nondiabetic mice Mark P. Keller, … , Gary A. Churchill, Alan D. Attie J Clin Invest. 2019;129(10):4419-4432. https://doi.org/10.1172/JCI129143. Research Article Cell biology Genetics Graphical abstract Find the latest version: https://jci.me/129143/pdf The Journal of Clinical Investigation RESEARCH ARTICLE Gene loci associated with insulin secretion in islets from nondiabetic mice Mark P. Keller,1 Mary E. Rabaglia,1 Kathryn L. Schueler,1 Donnie S. Stapleton,1 Daniel M. Gatti,2 Matthew Vincent,2 Kelly A. Mitok,1 Ziyue Wang,3 Takanao Ishimura,2 Shane P. Simonett,1 Christopher H. Emfinger,1 Rahul Das,1 Tim Beck,4 Christina Kendziorski,3 Karl W. Broman,3 Brian S. Yandell,5 Gary A. Churchill,2 and Alan D. Attie1 1University of Wisconsin-Madison, Biochemistry Department, Madison, Wisconsin, USA. 2The Jackson Laboratory, Bar Harbor, Maine, USA. 3University of Wisconsin-Madison, Department of Biostatistics and Medical Informatics, Madison, Wisconsin, USA. 4Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom. 5University of Wisconsin-Madison, Department of Horticulture, Madison, Wisconsin, USA. Genetic susceptibility to type 2 diabetes is primarily due to β cell dysfunction. However, a genetic study to directly interrogate β cell function ex vivo has never been previously performed. We isolated 233,447 islets from 483 Diversity Outbred (DO) mice maintained on a Western-style diet, and measured insulin secretion in response to a variety of secretagogues. Insulin secretion from DO islets ranged greater than 1000-fold even though none of the mice were diabetic.
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]