Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia
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RT² Profiler PCR Array (384-Well Format) Human Apoptosis 384HT
RT² Profiler PCR Array (384-Well Format) Human Apoptosis 384HT Cat. no. 330231 PAHS-3012ZE For pathway expression analysis Format For use with the following real-time cyclers RT² Profiler PCR Array, Applied Biosystems® models 7900HT (384-well block), Format E ViiA™ 7 (384-well block); Bio-Rad CFX384™ RT² Profiler PCR Array, Roche® LightCycler® 480 (384-well block) Format G Description The Human Apoptosis RT² Profiler PCR Array profiles the expression of 370 key genes involved in apoptosis, or programmed cell death. The array includes the TNF ligands and their receptors; members of the bcl-2 family, BIR (baculoviral IAP repeat) domain proteins, CARD domain (caspase recruitment domain) proteins, death domain proteins, TRAF (TNF receptor-associated factor) domain proteins and caspases. These 370 genes are also grouped by their functional contribution to apoptosis, either anti-apoptosis or induction of apoptosis. Using real-time PCR, you can easily and reliably analyze expression of a focused panel of genes related to apoptosis with this array. For further details, consult the RT² Profiler PCR Array Handbook. Sample & Assay Technologies Shipping and storage RT² Profiler PCR Arrays in formats E and G are shipped at ambient temperature, on dry ice, or blue ice packs depending on destination and accompanying products. For long term storage, keep plates at –20°C. Note: Ensure that you have the correct RT² Profiler PCR Array format for your real-time cycler (see table above). Note: Open the package and store the products appropriately immediately -
Cytokine Nomenclature
RayBiotech, Inc. The protein array pioneer company Cytokine Nomenclature Cytokine Name Official Full Name Genbank Related Names Symbol 4-1BB TNFRSF Tumor necrosis factor NP_001552 CD137, ILA, 4-1BB ligand receptor 9 receptor superfamily .2. member 9 6Ckine CCL21 6-Cysteine Chemokine NM_002989 Small-inducible cytokine A21, Beta chemokine exodus-2, Secondary lymphoid-tissue chemokine, SLC, SCYA21 ACE ACE Angiotensin-converting NP_000780 CD143, DCP, DCP1 enzyme .1. NP_690043 .1. ACE-2 ACE2 Angiotensin-converting NP_068576 ACE-related carboxypeptidase, enzyme 2 .1 Angiotensin-converting enzyme homolog ACTH ACTH Adrenocorticotropic NP_000930 POMC, Pro-opiomelanocortin, hormone .1. Corticotropin-lipotropin, NPP, NP_001030 Melanotropin gamma, Gamma- 333.1 MSH, Potential peptide, Corticotropin, Melanotropin alpha, Alpha-MSH, Corticotropin-like intermediary peptide, CLIP, Lipotropin beta, Beta-LPH, Lipotropin gamma, Gamma-LPH, Melanotropin beta, Beta-MSH, Beta-endorphin, Met-enkephalin ACTHR ACTHR Adrenocorticotropic NP_000520 Melanocortin receptor 2, MC2-R hormone receptor .1 Activin A INHBA Activin A NM_002192 Activin beta-A chain, Erythroid differentiation protein, EDF, INHBA Activin B INHBB Activin B NM_002193 Inhibin beta B chain, Activin beta-B chain Activin C INHBC Activin C NM005538 Inhibin, beta C Activin RIA ACVR1 Activin receptor type-1 NM_001105 Activin receptor type I, ACTR-I, Serine/threonine-protein kinase receptor R1, SKR1, Activin receptor-like kinase 2, ALK-2, TGF-B superfamily receptor type I, TSR-I, ACVRLK2 Activin RIB ACVR1B -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Functional Analysis of Ectodysplasin-A Mutations in X-Linked Non-Syndromic Hypodontia and Possible Involvement of X-Chromosome Inactivation
Functional analysis of Ectodysplasin-A mutations in X-linked non-syndromic hypodontia and possible involvement of X-chromosome inactivation Yuhua Pan ( [email protected] ) Southern Medical University https://orcid.org/0000-0001-8039-1627 Ting Lu Southern Medical University Ling Peng Southern Medical University Qi Zeng Southern Medical University Xiangyu Huang Southern Medical University Buling Wu Southern Medical University Fu Xiong Southern Medical University Research Article Keywords: ectodysplasin-A, dental pulp stem cells, X chromosome inactivation, tooth agenesis, odontogenic differentiation Posted Date: August 13th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-602940/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/17 Abstract Background: Mutations of the Ectodysplasin-A (EDA) gene are generally associated with syndrome hypohidrotic ectodermal dysplasia or non-syndromic tooth agenesis. The inuence of EDA mutations on dentinogenesis and odontoblast differentiation have not been reported. The aim of this study was to identify genetic clues for the causes of familial non-syndromic oligodontia and explore the underlying mechanisms involved, while focusing on the role of human dental pulp stem cells (hDPSCs). Materials and Methods: Candidate gene sequences were obtained by PCR amplication and Sanger sequencing. Functional analysis was conducted, and the pathogenesis associated with EDA mutations in hDPSCs was investigated to explore the impact of the identied mutation on the phenotype. Capillary electrophoresis (CE) was used to detect X chromosome inactivation (XCI) in the blood of female carriers. Results: In this study, we identied an EDA mutation in a Chinese familythe missense mutation c.1013C>T (Thr338Met). -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
Molecular Effects of Isoflavone Supplementation Human Intervention Studies and Quantitative Models for Risk Assessment
Molecular effects of isoflavone supplementation Human intervention studies and quantitative models for risk assessment Vera van der Velpen Thesis committee Promotors Prof. Dr Pieter van ‘t Veer Professor of Nutritional Epidemiology Wageningen University Prof. Dr Evert G. Schouten Emeritus Professor of Epidemiology and Prevention Wageningen University Co-promotors Dr Anouk Geelen Assistant professor, Division of Human Nutrition Wageningen University Dr Lydia A. Afman Assistant professor, Division of Human Nutrition Wageningen University Other members Prof. Dr Jaap Keijer, Wageningen University Dr Hubert P.J.M. Noteborn, Netherlands Food en Consumer Product Safety Authority Prof. Dr Yvonne T. van der Schouw, UMC Utrecht Dr Wendy L. Hall, King’s College London This research was conducted under the auspices of the Graduate School VLAG (Advanced studies in Food Technology, Agrobiotechnology, Nutrition and Health Sciences). Molecular effects of isoflavone supplementation Human intervention studies and quantitative models for risk assessment Vera van der Velpen Thesis submitted in fulfilment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus Prof. Dr M.J. Kropff, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Friday 20 June 2014 at 13.30 p.m. in the Aula. Vera van der Velpen Molecular effects of isoflavone supplementation: Human intervention studies and quantitative models for risk assessment 154 pages PhD thesis, Wageningen University, Wageningen, NL (2014) With references, with summaries in Dutch and English ISBN: 978-94-6173-952-0 ABSTRact Background: Risk assessment can potentially be improved by closely linked experiments in the disciplines of epidemiology and toxicology. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Gene Expression Patterns of the Female Genital Tract and Immunomodulation by Lactobacillus Species
Gene expression patterns of the female genital tract and immunomodulation by Lactobacillus species Andrea Gillian Abrahams ABRAND006 Supervisor: Dr Lindi Masson Co-Supervisors: Dr Arghavan Alisoltani-Dehkordi Dr Heather Jaspan Presented for Master of Science Degree (MSc) in Medical Virology Division of Medical Virology Faculty of Health Sciences University of Cape Town 23 September 2020 The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or non- commercial research purposes only. Published by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University of Cape Town Plagiarism Declaration “This thesis/dissertation has been submitted to the Turnitin module (or equivalent similarity and originality checking software) and I confirm that my supervisor has seen my report and any concerns revealed by such have been resolved with my supervisor.” Name: Andrea Gillian Abrahams Student number: ABRAND006 Signature: Date: 23 September 2020 i Acknowledgements First and foremost, I thank God for His grace and strength that has sustained me during this study and the completion of this thesis. To my supervisor, Dr Lindi Masson, I cannot thank you enough for all the support and encouragement you’ve given me these past two years. Thank you for your understanding and kindness during every challenge I’ve faced. You have taught me so much in this field and I am very grateful to you. A special thanks to my co-supervisor Dr Arghavan Alisoltani-Dehkordi for assistance in microarray data analysis training. -
Generation of the Primary Hair Follicle Pattern
Generation of the primary hair follicle pattern Chunyan Mou*, Ben Jackson*, Pascal Schneider†, Paul A. Overbeek‡, and Denis J. Headon*§ *Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, United Kingdom; †Department of Biochemistry, BIL Biomedical Research Center, University of Lausanne, CH-1066 Epalinges, Switzerland; and ‡Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030 Edited by Kathryn V. Anderson, Sloan–Kettering Institute, New York, NY, and approved May 8, 2006 (received for review January 31, 2006) Hair follicles are spaced apart from one another at regular intervals between signaling pathways involved in this process are largely through the skin. Although follicles are predominantly epidermal unknown. This is a particularly important problem because it is structures, classical tissue recombination experiments indicated the interactions between molecules, rather than the intrinsic that the underlying dermis defines their location during develop- function of any individual gene product, that is responsible for ment. Although many molecules involved in hair follicle formation orchestrating pattern formation. One such signaling pathway, have been identified, the molecular interactions that determine the composed of the extracellular ligand ectodysplasin (Eda), its emergent property of pattern formation have remained elusive. receptor Edar, and its cytoplasmic signaling adapter Edar- We have used embryonic skin cultures to dissect signaling re- associated death domain (Edaradd), is required for development sponses and patterning outcomes as the skin spatially organizes of a specific subset of hair follicles. Mutation of any of these three itself. We find that ectodysplasin receptor (Edar)–bone morpho- genes, all of which are specifically expressed in the epidermis, genetic protein (BMP) signaling and transcriptional interactions are causes identical ectodermal dysplasia phenotypes in mouse and central to generation of the primary hair follicle pattern, with human (9–12).