DOCSLIB.ORG

Analysis of Protein-Protein Interaction Network Based on Transcriptome

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Analysis of Protein-Protein Interaction Network Based on Transcriptome Talebi et al. Journal of Animal Science and Technology (2018) 60:11 https://doi.org/10.1186/s40781-018-0171-y SHORT REPORT Open Access Analysis of protein-protein interaction network based on transcriptome profiling of ovine granulosa cells identifies candidate genes in cyclic recruitment of ovarian follicles Reza Talebi1, Ahmad Ahmadi1* and Fazlollah Afraz2 Abstract After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells infollicularphaseincomparisonwithlutealphase.Bycontrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment. Keywords: Protein–protein interaction network, Biomarkers, Ovarian follicles, Granulosa cells, Cyclic recruitment Introduction tools from systems biology going beyond simple gene ontol- Small antral follicles represent the fundamental develop- ogy (GO) terms like causal network modelling linking gene mental unit of the mammalian ovary and, as such, serve the expression analysis to gene interaction information are needs of the entire reproductive life span. After pubertal, sorely needed [5, 6]. Nowadays, biological networks allow a cohort of small antral follicles enters to gonadotrophin- comprehensive examination of the complex mechanisms sensitive development upon FSH (Follicle Stimulating for targeted empirical studies [7]. Numerous studies have Hormone) stimulation, called cyclic recruited follicles [1, 2]. been conducted in attempt to identify candidate genes of Cyclic recruitment, although obligatory, does not guarantee reproductive biology via gene networks analysis such as ovulation because growing follicles are vulnerable to atresia development of rat primordial follicles [8, 9], early embryo and thus may fall out from the growth trajectory [3]. development in mouse [10]andbovine[11], gene networks Among somatic cells of the antral follicles, granulosa cells of bovine oocytes [12] and bovine granulosa cells of small will undergo serious changes morphologically and physiolo- and large follicles [13]. Protein-protein interaction (PPI) net- gically during the processes of proliferation, differentiation, works are one the most known approaches for representa- ovulation, lutenization and atresia [4]. tion of candidate genes/proteins beyond of high-throughput To understand gene function of the cellular processes, studies [13–19]. genes must be studied in the context of networks. Emerging In previous study, we surprisingly have shown differ- ences in transcriptome profiles of ovine (sheep) granulosa * Correspondence: [email protected] cells between small antral follicles (1–3 mm) collected 1 Department of Animal Sciences, Faculty of Agriculture, Bu-Ali Sina during the follicular and luteal phases [20]. Therefore, the University, Hamedan, Iran Full list of author information is available at the end of the article specific purpose of the present study was to survey the © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Talebi et al. Journal of Animal Science and Technology (2018) 60:11 Page 2 of 7 differentially expressed genes (DEGs) from the previous database Version. 10.0 [21] containing neighborhoods, study using the analysis of PPI networks in order to identi- gene fusion, co-occurrence, homology, co-expression, fication of candidate genes that probably those are impres- experiments, databases and text mining, were utilized with sive in cyclic recruitment of ovarian follicles. the medium confidence score (> 0.4). Materials and methods Dataset assessment Network analysis and modules selection The main dataset of the present study was belonged to Cytoscape Version 3.1.1 [22] was used to plot and 663 DEGs in ovine granulosa cells between small antral analyze the centralities, clustering and modularity of the follicles (1–3 mm in diameter) collected during the PPI network. The MCODE (Molecular Complex Detec- follicular and luteal phases [20]. These DEGs afterwards tion) v1.4.0-beta2 was performed to screen modules of were annotated using a standard Ensembl gene annota- PPI network with degree cutoff = 2, node score cutoff = tion system. 0.2, k-core = 2, and max. Depth = 100 [14]. This is a well-known automated algorithm to find highly inter- Construction of protein-protein interaction (PPI) network connected subgraphs that detects densely connected A large PPI network was reconstructed from the 646 regions in large PPI networks that may represent genes/protein symbols (Additional file 1) of the Ensembl molecular complexes [23]. Also, the functional modules annotation (Ovis_aries.Oar_v3.1.91). In order to map pair- were chosen with the number of nodes ≥16 and nodes wise interactions, all computational methods in STRING score [19]. Fig. 1 The Un-directed PPI network for total genes/proteins differentially expressed (cut-off of P < 0.05 in corrected tests of Benjamini-Hochberg) in luteal vs. follicular phases of ovine estrous cycle. The PPI network illustrates degree (node size) and betweenness centrality (node color) and layout option of edge weighted spring embedded. Up- and down-regulated genes in luteal phase vs. follicular phase are shown with triangles and V shapes, respectively Talebi et al. Journal of Animal Science and Technology (2018) 60:11 Page 3 of 7 Enrichment analysis for the functional modules from the hub genes/proteins. These important proteins Biological significance of these predicted modules were were extracted from hub genes/proteins via network inferred by ClueGO [24] plugin of Cytoscape. The statis- analysis and modularity of the network. This small, but tical significance of the biological terms analyzed was important, graph is visualized by Cytoscape Version 3.1.1 calculated with Right-sided enrichment hypergeometric [22]. Using CluePedia v.1.1.7. [24]pluginofCytoscape, test and Benjamini and Hochberg P-value correction hub genes/proteins were placed in a directed gene net- [25] to reduce false positives- and negatives. Kappa sta- work based on its molecular function. tistics were used to link and grouping of the enriched terms and functional grouping of them as described by Result and discussion Bindea et al. [24]. The minimum connectivity of the Among 646 DEGs (Additional file 1), 498 proteins were pathway network (kappa score) was set to 0.4 units. annotated on the Total PPI network. Also, 2191 edges containing neighborhoods, gene fusion, co-occurrence, Identification of hub genes/proteins and its directed homology, co-expression, experiments, databases and text interaction network mining, were interacted between such genes/proteins In this study, all networks were utilized to identify hub (Fig. 1 and Additional file 3). The statistics of network genes/proteins which important in folliculogenesis containing network density, network diameter and cluster- during the ovine estrous cycle. Hubs were detected by ing coefficient were 0.018, 9 and 0.275, respectively. The 2 calculating the node degree distribution [16] using the power law of degree distribution was followed with an R 2 Network Analyzer (http://apps.cytoscape.org/apps/net- = 0.895. Meanwhile, R is computed on logarithmized workanalyzer) plugin of Cytoscape [24]. Degree gives a values. Some proteins in this network had high values in simple count of the number of interactions of a given all degree, stress and betweenness centralities, such as node [26]. Additionally, we utilized several centrality FYN, CDK1, RAC1, ACTG2, FGR, APP, MMP2, SYK, parameters including stress, betweenness and closeness CDH1, TGFB1, PTPN6, ITGB7, FOS, ACTN1, GNAI2, instead of using degree centrality itself. The mathemat- INSRR, BMP4, BMP2, LYN and HCK (Additional file 3). ical formulas for the calculation of centrality parameters This list was utilized to identify some candidate genes including stress, betweenes and clossness, are available among hubs as major regulators in cyclic recruitment of in Additional file 2 that has been retrieved from Zhuang ovine small antral follicles. et al. [26]. By extract direction of PPI from STRING Ver- From the total PPI network, thirteen modules were ex- sion. 10.0 [21], a directed gene network was reconstructed tracted, among which five subnetworks (modules of 1, 2, Table 1 Statistics for thirteen subnetworks identified by MCODE method in PPI network
Load more

Recommended publications
  • Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
    Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent
    [Show full text]
  • Mutations in CDK5RAP2 Cause Seckel Syndrome Go¨ Khan Yigit1,2,3,A, Karen E
    ORIGINAL ARTICLE Mutations in CDK5RAP2 cause Seckel syndrome Go¨ khan Yigit1,2,3,a, Karen E. Brown4,a,Hu¨ lya Kayserili5, Esther Pohl1,2,3, Almuth Caliebe6, Diana Zahnleiter7, Elisabeth Rosser8, Nina Bo¨ gershausen1,2,3, Zehra Oya Uyguner5, Umut Altunoglu5, Gudrun Nu¨ rnberg2,3,9, Peter Nu¨ rnberg2,3,9, Anita Rauch10, Yun Li1,2,3, Christian Thomas Thiel7 & Bernd Wollnik1,2,3 1Institute of Human Genetics, University of Cologne, Cologne, Germany 2Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany 3Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany 4Chromosome Biology Group, MRC Clinical Sciences Centre, Imperial College School of Medicine, Hammersmith Hospital, London, W12 0NN, UK 5Department of Medical Genetics, Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey 6Institute of Human Genetics, Christian-Albrechts-University of Kiel, Kiel, Germany 7Institute of Human Genetics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany 8Department of Clinical Genetics, Great Ormond Street Hospital for Children, London, WC1N 3EH, UK 9Cologne Center for Genomics, University of Cologne, Cologne, Germany 10Institute of Medical Genetics, University of Zurich, Schwerzenbach-Zurich, Switzerland Keywords Abstract CDK5RAP2, CEP215, microcephaly, primordial dwarfism, Seckel syndrome Seckel syndrome is a heterogeneous, autosomal recessive disorder marked by pre- natal proportionate short stature, severe microcephaly, intellectual disability, and Correspondence characteristic facial features. Here, we describe the novel homozygous splice-site Bernd Wollnik, Center for Molecular mutations c.383+1G>C and c.4005-9A>GinCDK5RAP2 in two consanguineous Medicine Cologne (CMMC) and Institute of families with Seckel syndrome. CDK5RAP2 (CEP215) encodes a centrosomal pro- Human Genetics, University of Cologne, tein which is known to be essential for centrosomal cohesion and proper spindle Kerpener Str.
    [Show full text]
  • Genetic Variations in the PSMA6 and PSMC6 Proteasome Genes Are Associated with Multiple Sclerosis and Response to Interferon‑Β Therapy in Latvians
    EXPERIMENTAL AND THERAPEUTIC MEDICINE 21: 478, 2021 Genetic variations in the PSMA6 and PSMC6 proteasome genes are associated with multiple sclerosis and response to interferon‑β therapy in Latvians NATALIA PARAMONOVA1, JOLANTA KALNINA1, KRISTINE DOKANE1, KRISTINE DISLERE1, ILVA TRAPINA1, TATJANA SJAKSTE1 and NIKOLAJS SJAKSTE1,2 1Genomics and Bioinformatics, Institute of Biology of The University of Latvia; 2Department of Medical Biochemistry of The University of Latvia, LV‑1004 Riga, Latvia Received July 8, 2020; Accepted December 8, 2020 DOI: 10.3892/etm.2021.9909 Abstract. Several polymorphisms in genes related to the Introduction ubiquitin‑proteasome system exhibit an association with pathogenesis and prognosis of various human autoimmune Multiple sclerosis (MS) is a lifelong demyelinating disease of diseases. Our previous study reported the association the central nervous system. The clinical onset of MS tends to between multiple sclerosis (MS) and the PSMA3‑rs2348071 be between the second and fourth decade of life. Similarly to polymorphism in the Latvian population. The current study other autoimmune diseases, women are affected 3‑4 times more aimed to evaluate the PSMA6 and PSMC6 genetic variations, frequently than men (1). About 10% of MS patients experience their interaction between each other and with the rs2348071, a primary progressive MS form characterized by the progres‑ on the susceptibility to MS risk and response to therapy in sion of neurological disability from the onset. In about 90% the Latvian population. PSMA6‑rs2277460, ‑rs1048990 and of MS patients, the disease undergoes the relapse‑remitting PSMC6‑rs2295826, ‑rs2295827 were genotyped in the MS MS course (RRMS); in most of these patients, the condition case/control study and analysed in terms of genotype‑protein acquires secondary progressive course (SPMS) (2).
    [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]
  • Pro-Inflammatory Tnfα and IL-1Β Differentially Regulate the Inflammatory Phenotype of Brain Microvascular Endothelial Cells Simon J
    O’Carroll et al. Journal of Neuroinflammation (2015) 12:131 JOURNAL OF DOI 10.1186/s12974-015-0346-0 NEUROINFLAMMATION RESEARCH Open Access Pro-inflammatory TNFα and IL-1β differentially regulate the inflammatory phenotype of brain microvascular endothelial cells Simon J. O’Carroll1,2, Dan Ting Kho1,3, Rachael Wiltshire1, Vicky Nelson1,3, Odunayo Rotimi1,2, Rebecca Johnson1,3, Catherine E. Angel4 and E. Scott Graham1,3* Abstract Background: The vasculature of the brain is composed of endothelial cells, pericytes and astrocytic processes. The endothelial cells are the critical interface between the blood and the CNS parenchyma and are a critical component of the blood-brain barrier (BBB). These cells are innately programmed to respond to a myriad of inflammatory cytokines or other danger signals. IL-1β and TNFα are well recognised pro-inflammatory mediators, and here, we provide compelling evidence that they regulate the function and immune response profile of human cerebral microvascular endothelial cells (hCMVECs) differentially. Methods: We used xCELLigence biosensor technology, which revealed global differences in the endothelial response between IL-1β and TNFα. xCELLigence is a label-free impedance-based biosensor, which is ideal for acute or long-term comparison of drug effects on cell behaviour. In addition, flow cytometry and multiplex cytokine arrays were used to show differences in the inflammatory responses from the endothelial cells. Results: Extensive cytokine-secretion profiling and cell-surface immune phenotyping confirmed that the immune response of the hCMVEC to IL-1β was different to that of TNFα. Interestingly, of the 38 cytokines, chemokines and growth factors measured by cytometric bead array, the endothelial cells secreted only 13.
    [Show full text]
  • Overexpression of Androgen Receptor in Prostate Cancer
    ALFONSO URBANUCCI Overexpression of Androgen Receptor in Prostate Cancer ACADEMIC DISSERTATION To be presented, with the permission of the board of Institute of Biomedical Technology of the University of Tampere, for public discussion in the Jarmo Visakorpi Auditorium, of the Arvo Building, Lääkärinkatu 1, Tampere, on January 20th, 2012, at 12 o’clock. UNIVERSITY OF TAMPERE ACADEMIC DISSERTATION University of Tampere, Institute of Biomedical Technology and BioMediTech Tampere University Hospital, Laboratory Centre Graduate Program in Biomedicine and Biotechnology (TGPBB) Finland Supervised by Reviewed by Professor Tapio Visakorpi Docent Auli Karhu University of Tampere University of Helsinki Finland Finland Docent Noora Kotaja University of Turku Finland Copyright ©2012 Tampere University Press and the author Distribution Tel. +358 40 190 9800 Bookshop TAJU Fax +358 3 3551 7685 P.O. Box 617 [email protected] 33014 University of Tampere www.uta.fi/taju Finland http://granum.uta.fi Cover design by Mikko Reinikka Acta Universitatis Tamperensis 1693 Acta Electronica Universitatis Tamperensis 1159 ISBN 978-951-44-8685-2 (print) ISBN 978-951-44-8686-9 (pdf) ISSN-L 1455-1616 ISSN 1456-954X ISSN 1455-1616 http://acta.uta.fi Tampereen Yliopistopaino Oy – Juvenes Print Tampere 2012 CONTENTS ABBREVIATIONS ..................................................................................................... 5 ABSTRACT ................................................................................................................ 7 SINTESI .....................................................................................................................
    [Show full text]
  • Supplemental Information Proximity Interactions Among Centrosome
    Current Biology, Volume 24 Supplemental Information Proximity Interactions among Centrosome Components Identify Regulators of Centriole Duplication Elif Nur Firat-Karalar, Navin Rauniyar, John R. Yates III, and Tim Stearns Figure S1 A Myc Streptavidin -tubulin Merge Myc Streptavidin -tubulin Merge BirA*-PLK4 BirA*-CEP63 BirA*- CEP192 BirA*- CEP152 - BirA*-CCDC67 BirA* CEP152 CPAP BirA*- B C Streptavidin PCM1 Merge Myc-BirA* -CEP63 PCM1 -tubulin Merge BirA*- CEP63 DMSO - BirA* CEP63 nocodazole BirA*- CCDC67 Figure S2 A GFP – + – + GFP-CEP152 + – + – Myc-CDK5RAP2 + + + + (225 kDa) Myc-CDK5RAP2 (216 kDa) GFP-CEP152 (27 kDa) GFP Input (5%) IP: GFP B GFP-CEP152 truncation proteins Inputs (5%) IP: GFP kDa 1-7481-10441-1290218-1654749-16541045-16541-7481-10441-1290218-1654749-16541045-1654 250- Myc-CDK5RAP2 150- 150- 100- 75- GFP-CEP152 Figure S3 A B CEP63 – – + – – + GFP CCDC14 KIAA0753 Centrosome + – – + – – GFP-CCDC14 CEP152 binding binding binding targeting – + – – + – GFP-KIAA0753 GFP-KIAA0753 (140 kDa) 1-496 N M C 150- 100- GFP-CCDC14 (115 kDa) 1-424 N M – 136-496 M C – 50- CEP63 (63 kDa) 1-135 N – 37- GFP (27 kDa) 136-424 M – kDa 425-496 C – – Inputs (2%) IP: GFP C GFP-CEP63 truncation proteins D GFP-CEP63 truncation proteins Inputs (5%) IP: GFP Inputs (5%) IP: GFP kDa kDa 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl Myc- 150- Myc- 100- CCDC14 KIAA0753 100- 100- 75- 75- GFP- GFP- 50- CEP63 50- CEP63 37- 37- Figure S4 A siCtl
    [Show full text]
  • Profiling Data
    Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8
    [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]
  • 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]
  • Inflammatory Cytokine Signalling by Protein Tyrosine Phosphatases in Pancreatic Β-Cells
    59 4 W J STANLEY and others PTPN1 and PTPN6 modulate 59: 4 325–337 Research cytokine signalling in β-cells Differential regulation of pro- inflammatory cytokine signalling by protein tyrosine phosphatases in pancreatic β-cells William J Stanley1,2, Prerak M Trivedi1,2, Andrew P Sutherland1, Helen E Thomas1,2 and Esteban N Gurzov1,2,3 Correspondence should be addressed 1 St. Vincent’s Institute of Medical Research, Melbourne, Australia to E N Gurzov 2 Department of Medicine, St. Vincent’s Hospital, The University of Melbourne, Melbourne, Australia Email 3 ULB Center for Diabetes Research, Universite Libre de Bruxelles (ULB), Brussels, Belgium esteban.gurzov@unimelb. edu.au Abstract Type 1 diabetes (T1D) is characterized by the destruction of insulin-producing β-cells Key Words by immune cells in the pancreas. Pro-inflammatory including TNF-α, IFN-γ and IL-1β f pancreatic β-cells are released in the islet during the autoimmune assault and signal in β-cells through f protein tyrosine phosphorylation cascades, resulting in pro-apoptotic gene expression and eventually phosphatases β-cell death. Protein tyrosine phosphatases (PTPs) are a family of enzymes that regulate f PTPN1 phosphorylative signalling and are associated with the development of T1D. Here, we f PTPN6 observed expression of PTPN6 and PTPN1 in human islets and islets from non-obese f cytokines diabetic (NOD) mice. To clarify the role of these PTPs in β-cells/islets, we took advantage f inflammation Journal of Molecular Endocrinology of CRISPR/Cas9 technology and pharmacological approaches to inactivate both proteins. We identify PTPN6 as a negative regulator of TNF-α-induced β-cell death, through JNK- dependent BCL-2 protein degradation.
    [Show full text]
  • Autosomal Recessive Primary Microcephaly
    Mahmood et al. Orphanet Journal of Rare Diseases 2011, 6:39 http://www.ojrd.com/content/6/1/39 REVIEW Open Access Autosomal recessive primary microcephaly (MCPH): clinical manifestations, genetic heterogeneity and mutation continuum Saqib Mahmood1, Wasim Ahmad2 and Muhammad J Hassan3* Abstract Autosomal Recessive Primary Microcephaly (MCPH) is a rare disorder of neurogenic mitosis characterized by reduced head circumference at birth with variable degree of mental retardation. In MCPH patients, brain size reduced to almost one-third of its original volume due to reduced number of generated cerebral cortical neurons during embryonic neurogensis. So far, seven genetic loci (MCPH1-7) for this condition have been mapped with seven corresponding genes (MCPH1, WDR62, CDK5RAP2, CEP152, ASPM, CENPJ, and STIL) identified from different world populations. Contribution of ASPM and WDR62 gene mutations in MCPH World wide is more than 50%. By and large, primary microcephaly patients are phenotypically indistinguishable, however, recent studies in patients with mutations in MCPH1, WDR62 and ASPM genes showed a broader clinical and/or cellular phenotype. It has been proposed that mutations in MCPH genes can cause the disease phenotype by disturbing: 1) orientation of mitotic spindles, 2) chromosome condensation mechanism during embryonic neurogenesis, 3) DNA damage- response signaling, 4) transcriptional regulations and microtubule dynamics, 5) certain unknown centrosomal mechanisms that control the number of neurons generated by neural precursor cells. Recent discoveries of mammalian models for MCPH have open up horizons for researchers to add more knowledge regarding the etiology and pathophysiology of MCPH. High incidence of MCPH in Pakistani population reflects the most probable involvement of consanguinity.
    [Show full text]