Dragon's Blood Regulates Rac1-WAVE2-Arp2/3 Signaling
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Snapshot: Axon Guidance Pasterkamp R
494 1 Cell Cell ??? SnapShot: Axon Guidance 153 SnapShot: XXXXXXXXXXXXXXXXXXXXXXXXXX 1 2 , ??MONTH?? ??DATE??, 200? ©200? Elsevier Inc. 200?©200? ElsevierInc. , ??MONTH?? ??DATE??, DOI R. Jeroen Pasterkamp and Alex L. Kolodkin , April11, 2013©2013Elsevier Inc. DOI http://dx.doi.org/10.1016/j.cell.2013.03.031 AUTHOR XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 1 AFFILIATIONDepartment of XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX Neuroscience and Pharmacology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; 2Department of Neuroscience, HHMI, The Johns Hopkins University School of Medicine, Baltimore, MD 21212, USA Axon attraction and repulsion Surround repulsion Selective fasciculation Topographic mapping Self-avoidance Wild-type Dscam1 mutant Sema3A A Retina SC/Tectum EphB EphrinB P D D Mutant T A neuron N P A V V Genomic DNA P EphA EphrinA Slit Exon 4 (12) Exon 6 (48) Exon 9 (33) Exon 17 (2) Netrin Commissural axon guidance Surround repulsion of peripheral Grasshopper CNS axon Retinotectal mapping at the CNS midline nerves in vertebrates fasciculation in vertebrates Drosophila mushroom body XXXXXXXXX NEURITE/CELL Isoneuronal Sema3 Slit Sema1/4-6 Heteroneuronal EphrinA EphrinB FasII Eph Genomic DNA Pcdh-α (14) Pcdh-β (22) Pcdh-γ (22) Variable Con Variable Con Nrp Plexin ** *** Netrin LAMELLIPODIA Con DSCAM ephexin Starburst amacrine cells in mammalian retina Ras-GTP Vav See online version for legend and references. α-chimaerin GEFs/GAPs Robo FARP Ras-GDP LARG RhoGEF Kinases DCC cc0 GTPases PKA cc1 FAK Regulatory Mechanisms See online versionfor??????. Cdc42 GSK3 cc2 Rac PI3K P1 Rho P2 cc3 Abl Proteolytic cleavage P3 Regulation of expression (TF, miRNA, FILOPODIA srGAP Cytoskeleton regulatory proteins multiple isoforms) Sos Trio Pcdh Cis inhibition DOCK180 PAK ROCK Modulation of receptors’ output LIMK Myosin-II Colin Forward and reverse signaling Actin Trafcking and endocytosis NEURONAL GROWTH CONE Microtubules SnapShot: Axon Guidance R. -
Regulation of the Mammalian Target of Rapamycin Complex 2 (Mtorc2)
Regulation of the Mammalian Target Of Rapamycin Complex 2 (mTORC2) Inauguraldissertation Zur Erlangung der Würde eines Doktors der Philosophie vorgelegt der Philosophisch-Naturwissenschaftlichen Fakultät der Universität Basel von Klaus-Dieter Molle aus Heilbronn, Deutschland Basel, 2006 Genehmigt von der Philosophisch-Naturwissenschaftlichen Fakultät Auf Antrag von Prof. Michael N. Hall und Prof. Markus Affolter. Basel, den 21.11.2006 Prof. Hans-Peter Hauri Dekan Summary The growth controlling mammalian Target of Rapamycin (mTOR) is a conserved Ser/Thr kinase found in two structurally and functionally distinct complexes, mTORC1 and mTORC2. The tumor suppressor TSC1-TSC2 complex inhibits mTORC1 by acting on the small GTPase Rheb, but the role of TSC1-TSC2 and Rheb in the regulation of mTORC2 is unclear. Here we examined the role of TSC1-TSC2 in the regulation of mTORC2 in human embryonic kidney 293 cells. Induced knockdown of TSC1 and TSC2 (TSC1/2) stimulated mTORC2-dependent actin cytoskeleton organization and Paxillin phosphorylation. Furthermore, TSC1/2 siRNA increased mTORC2-dependent Ser473 phosphorylation of plasma membrane bound, myristoylated Akt/PKB. This suggests that loss of Akt/PKB Ser473 phosphorylation in TSC mutant cells, as reported previously, is due to inhibition of Akt/PKB localization rather than inhibition of mTORC2 activity. Amino acids and overexpression of Rheb failed to stimulate mTORC2 signaling. Thus, TSC1-TSC2 also inhibits mTORC2, but possibly independently of Rheb. Our results suggest that mTORC2 hyperactivation may contribute to the pathophysiology of diseases such as cancer and Tuberous Sclerosis Complex. i Acknowledgement During my PhD studies in the Biozentrum I received a lot of support from many people around me who I mention here to express my gratefulness. -
Lymphocyte/WBC-Based Comprehensive Testing Via Next-Gen Sequencing NF1/SPRED1 and Other Rasopathy Related Conditions on Blood/Saliva
Last Updated October 2019 Institutional Price TAT Genetic Test CPT Codes Z codes Specimen Requirements (USD$) (working days) Lymphocyte/WBC-based Comprehensive Testing via Next-Gen Sequencing NF1/SPRED1 and Other RASopathy Related Conditions on Blood/Saliva NF1- only NGS testing and copy number analysis for the NF1 gene (NF1-NG) This testing includes an average coverage of >1600x to allow for the identification of (1) 3-6ml whole blood in EDTA (purple topped) tubes mosaicism as low as 3-5% of the alleles. In addition, novel variants identified in the $1,000 81408 25 (2) Oragene 575 saliva kit (provided by the MGL) ZB6A9 NF1 gene will be confirmed via RNA-based analysis at no additional charge. RNA- $1,600 (RUSH) 81479 15 (RUSH) (3) DNA sample (25ul volume at 3ug, O.D. value at based testing will also be provided to non-founder, multigenerational families with 260:280 ≥1.8) “classic” NF1 at no additional charge if next-generation sequencing is found negative. (1) 3-6ml whole blood in EDTA (purple topped) tubes SPRED1-only NGS testing and copy number analysis for SPRED1 (SPD1-NG) $800 81405 25 (2) Oragene 575 saliva kit (provided by the MGL) This testing includes an average coverage of >1600x to allow for the identification of ZB6AC $1,400 (RUSH) 81479 15 (RUSH) (3) DNA sample (25ul volume at 3ug, O.D. value at mosaicism as low as 3-5% of the alleles after comprehensive NF1 analysis. 260:280 ≥1.8) NF1/SPRED1 NGS testing and copy number analysis for NF1 and SPRED1 (NFSP-NG) (1) 3-6ml whole blood in EDTA (purple topped) tubes 81408 This testing includes an average coverage of >1600x to allow for the identification of $1,100 25 (2) Oragene 575 saliva kit (provided by the MGL) 81405 ZB6A8 mosaicism as low as 3-5% of the alleles. -
The Atypical Guanine-Nucleotide Exchange Factor, Dock7, Negatively Regulates Schwann Cell Differentiation and Myelination
The Journal of Neuroscience, August 31, 2011 • 31(35):12579–12592 • 12579 Cellular/Molecular The Atypical Guanine-Nucleotide Exchange Factor, Dock7, Negatively Regulates Schwann Cell Differentiation and Myelination Junji Yamauchi,1,3,5 Yuki Miyamoto,1 Hajime Hamasaki,1,3 Atsushi Sanbe,1 Shinji Kusakawa,1 Akane Nakamura,2 Hideki Tsumura,2 Masahiro Maeda,4 Noriko Nemoto,6 Katsumasa Kawahara,5 Tomohiro Torii,1 and Akito Tanoue1 1Department of Pharmacology and 2Laboratory Animal Resource Facility, National Research Institute for Child Health and Development, Setagaya, Tokyo 157-8535, Japan, 3Department of Biological Sciences, Tokyo Institute of Technology, Midori, Yokohama 226-8501, Japan, 4IBL, Ltd., Fujioka, Gumma 375-0005, Japan, and 5Department of Physiology and 6Bioimaging Research Center, Kitasato University School of Medicine, Sagamihara, Kanagawa 252-0374, Japan In development of the peripheral nervous system, Schwann cells proliferate, migrate, and ultimately differentiate to form myelin sheath. In all of the myelination stages, Schwann cells continuously undergo morphological changes; however, little is known about their underlying molecular mechanisms. We previously cloned the dock7 gene encoding the atypical Rho family guanine-nucleotide exchange factor (GEF) and reported the positive role of Dock7, the target Rho GTPases Rac/Cdc42, and the downstream c-Jun N-terminal kinase in Schwann cell migration (Yamauchi et al., 2008). We investigated the role of Dock7 in Schwann cell differentiation and myelination. Knockdown of Dock7 by the specific small interfering (si)RNA in primary Schwann cells promotes dibutyryl cAMP-induced morpholog- ical differentiation, indicating the negative role of Dock7 in Schwann cell differentiation. It also results in a shorter duration of activation of Rac/Cdc42 and JNK, which is the negative regulator of myelination, and the earlier activation of Rho and Rho-kinase, which is the positive regulator of myelination. -
MUC4/MUC16/Muc20high Signature As a Marker of Poor Prognostic for Pancreatic, Colon and Stomach Cancers
Jonckheere and Van Seuningen J Transl Med (2018) 16:259 https://doi.org/10.1186/s12967-018-1632-2 Journal of Translational Medicine RESEARCH Open Access Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large‑scale genomic databases: MUC4/MUC16/ MUC20 signature is associated with poor survival in human carcinomas Nicolas Jonckheere* and Isabelle Van Seuningen* Abstract Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). Results: We identifed 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signal- ing. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with diferent localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adeno- carcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically signifcant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer. -
Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study Via Machine Learning
G C A T T A C G G C A T genes Article Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning Meeshanthini V. Dogan 1,2,3,*, Steven R. H. Beach 4, Ronald L. Simons 5, Amaury Lendasse 6,7, Brandan Penaluna 8 and Robert A. Philibert 1,2,3,8 1 Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA; [email protected] 2 Cardio Diagnostics LLC, 2500 Crosspark Road, Coralville, IA 52241, USA 3 Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA 4 Department of Psychology, University of Georgia, Athens, GA 30602, USA; [email protected] 5 Department of Sociology, University of Georgia, Athens, GA 30606, USA; [email protected] 6 Information and Logistics Technology Department, University of Houston, Houston, TX 77004, USA; [email protected] 7 Department of Business Management and Analytics, Arcada University of Applied Sciences, 00560 Helsinki, Finland 8 Behavioral Diagnostics LLC, 2500 Crosspark Road, Coralville, IA 52241, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-319-353-4986 Received: 15 November 2018; Accepted: 12 December 2018; Published: 18 December 2018 Abstract: An improved approach for predicting the risk for incident coronary heart disease (CHD) could lead to substantial improvements in cardiovascular health. Previously, we have shown that genetic and epigenetic loci could predict CHD status more sensitively than conventional risk factors. Herein, we examine whether similar machine learning approaches could be used to develop a similar panel for predicting incident CHD. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
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, -
Alpha;-Actinin-4 Promotes Metastasis in Gastric Cancer
Laboratory Investigation (2017) 97, 1084–1094 © 2017 USCAP, Inc All rights reserved 0023-6837/17 α-Actinin-4 promotes metastasis in gastric cancer Xin Liu and Kent-Man Chu Metastasis increases the mortality rate of gastric cancer, which is the third leading cause of cancer-associated deaths worldwide. This study aims to identify the genes promoting metastasis of gastric cancer (GC). A human cell motility PCR array was used to analyze a pair of tumor and non-tumor tissue samples from a patient with stage IV GC (T3N3M1). Expression of the dysregulated genes was then evaluated in GC tissue samples (n = 10) and cell lines (n =6) via qPCR. Expression of α-actinin-4 (ACTN4) was validated in a larger sample size (n = 47) by qPCR, western blot and immunohistochemistry. Knockdown of ACTN4 with specific siRNAs was performed in GC cells, and adhesion assays, transwell invasion assays and migration assays were used to evaluate the function of these cells. Expression of potential targets of ACTN4 were then evaluated by qPCR. Thirty upregulated genes (greater than twofold) were revealed by the PCR array. We focused on ACTN4 because it was upregulated in 6 out of 10 pairs of tissue samples and 5 out of 6 GC cell lines. Further study indicated that ACTN4 was upregulated in 22/32 pairs of tissue samples at stage III & IV (P = 0.0069). Knockdown of ACTN4 in GC cells showed no significant effect on cell proliferation, but significantly increased cell-matrix adhesion, as well as reduced migration and invasion of AGS, MKN7 and NCI-N87 cells. -
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int. -
Proteomic Expression Profile in Human Temporomandibular Joint
diagnostics Article Proteomic Expression Profile in Human Temporomandibular Joint Dysfunction Andrea Duarte Doetzer 1,*, Roberto Hirochi Herai 1 , Marília Afonso Rabelo Buzalaf 2 and Paula Cristina Trevilatto 1 1 Graduate Program in Health Sciences, School of Medicine, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba 80215-901, Brazil; [email protected] (R.H.H.); [email protected] (P.C.T.) 2 Department of Biological Sciences, Bauru School of Dentistry, University of São Paulo, Bauru 17012-901, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-41-991-864-747 Abstract: Temporomandibular joint dysfunction (TMD) is a multifactorial condition that impairs human’s health and quality of life. Its etiology is still a challenge due to its complex development and the great number of different conditions it comprises. One of the most common forms of TMD is anterior disc displacement without reduction (DDWoR) and other TMDs with distinct origins are condylar hyperplasia (CH) and mandibular dislocation (MD). Thus, the aim of this study is to identify the protein expression profile of synovial fluid and the temporomandibular joint disc of patients diagnosed with DDWoR, CH and MD. Synovial fluid and a fraction of the temporomandibular joint disc were collected from nine patients diagnosed with DDWoR (n = 3), CH (n = 4) and MD (n = 2). Samples were subjected to label-free nLC-MS/MS for proteomic data extraction, and then bioinformatics analysis were conducted for protein identification and functional annotation. The three Citation: Doetzer, A.D.; Herai, R.H.; TMD conditions showed different protein expression profiles, and novel proteins were identified Buzalaf, M.A.R.; Trevilatto, P.C. -
Tuberous Sclerosis Complex: Molecular Pathogenesis and Animal Models
Neurosurg Focus 20 (1):E4, 2006 Tuberous sclerosis complex: molecular pathogenesis and animal models LEANDRO R. PIEDIMONTE, M.D., IAN K. WAILES, M.D., AND HOWARD L. WEINER, M.D. Division of Pediatric Neurosurgery, Department of Neurosurgery, New York University School of Medicine, New York, New York Mutations in one of two genes, TSC1 and TSC2, result in a similar disease phenotype by disrupting the normal inter- action of their protein products, hamartin and tuberin, which form a functional signaling complex. Disruption of these genes in the brain results in abnormal cellular differentiation, migration, and proliferation, giving rise to the charac- teristic brain lesions of tuberous sclerosis complex (TSC) called cortical tubers. The most devastating complications of TSC affect the central nervous system and include epilepsy, mental retardation, autism, and glial tumors. Relevant animal models, including conventional and conditional knockout mice, are valuable tools for studying the normal func- tions of tuberin and hamartin and the way in which disruption of their expression gives rise to the variety of clinical features that characterize TSC. In the future, these animals will be invaluable preclinical models for the development of highly specific and efficacious treatments for children affected with TSC. KEY WORDS • tuberous sclerosis • pathogenesis • animal model Tuberous sclerosis complex is an autosomal-dominant zures, mental retardation, and autism.1,51 The number and tumor predisposition syndrome that affects approximately location of cortical tubers and the patient’s age at seizure 1 in 7500 individuals worldwide.19 The TSC is character- onset are highly correlated with neurological outcomes.1,30 ized by benign hamartomatous growths in multiple organs, Epilepsy occurs in approximately 80% of affected indi- including the kidney, skin, retina, lung, and brain.