DOCSLIB.ORG

TFG Ignacio M. Quintero FINAL

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
TFG Ignacio M. Quintero FINAL UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA AGRONÓMICA, ALIMENTARIA Y DE BIOSISTEMAS GRADO EN BIOTECNOLOGÍA DEPARTAMENTO DE PRODUCCIÓN AGRARIA Efecto de suplementar las dietas de pollos broiler con compuestos funcionales de orujo de oliva (Olea europaea) sobre la salud intestinal del animal Effects of supplementing broiler chicKen diets with bioactive olive pomace extracts (Olea europaea) on intestinal health TRABAJO FIN DE GRADO Autor: IGNACIO MANUEL QUINTERO MORÓN Tutor: DAVID MENOYO LUQUE Madrid, Julio de 2019 UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA AGRONÓMICA, ALIMENTARIA Y DE BIOSISTEMAS GRADO DE BIOTECNOLOGÍA Efecto de suplementar las dietas de pollos boiler con compuestos funcionales de orujo de oliva (Olea europaea) sobre la salud intestinal del animal - Effects of supplementing broiler chicken diets with bioactive olive pomace extracts (Olea europaea) on intestinal health TRABAJO FIN DE GRADO Ignacio Manuel Quintero Morón MADRID, 2019 Director: David Menoyo Luque Profesor Titular de Universidad Departamento de Producción Agraria II TITULO DEL TFG- Efecto de suplementar las dietas de pollos boiler con compuestos funcionales de orujo de oliva (Olea europaea) sobre la salud intestinal del animal - Effects of supplementing broiler chicken diets with bioactive olive pomace extracts (Olea europaea) on intestinal health Memoria presentada por Ignacio Manuel Quintero Morón para la obtención del título de Graduado en Biotecnología por la Universidad Politécnica de Madrid Fdo: Alumno VºBº Tutor y Director del TFG D. David Menoyo Luque Profesor Titular de Universidad Departamento de Producción Agraria ETSIAAB - Universidad Politécnica de Madrid Madrid, 10 de julio, 2019 III INDEX List of tables .................................................................................................................... V List of figures ................................................................................................................ VI List of Abbreviations ................................................................................................... VII SUMMARY .................................................................................................................. IX CHAPTER 1. INTRODUCTION AND OBJECTIVES .............................................. 1 1.1. Introduction .......................................................................................................... 1 1.2. Objectives .............................................................................................................. 3 CHAPTER 2. MATERIALS AND METHODS ........................................................... 4 2.1. Feeding trial and sampling .................................................................................. 4 2.2. RNA extraction ..................................................................................................... 5 2.3. Reverse transcription (RT) .................................................................................. 6 2.4. Quantitative Real-time Polymerase Chain Reaction (qPCR) .......................... 6 2.5. Statistical analysis ................................................................................................ 9 CHAPTER 3. RESULTS .............................................................................................. 10 CHAPTER 4. DISCUSSION AND CONCLUSION .................................................. 13 4.1. Discussion ............................................................................................................ 13 4.2. Conclusion ........................................................................................................... 14 BIBLIOGRAPHY ......................................................................................................... 15 IV List of tables Table 1. Ingredients and calculated values of starter diet (1 to 14 days of age). ............. 4 Table 2. Genes and their primer sequences (forward and reverse) used for the expression analysis .............................................................................................................. 7 Table 3. Genes with their standard curves’ slopes and efficiencies. ............................... 8 Table 4. Dietary effects on productive parameters of chickens from 0 to 14 days of age. ....................................................................................................................................... 10 V List of figures Figure 1. Differential gene expression according to mRNA relative abundance of every marker analyzed involved in the immune system of the ileum: Toll-Like Receptor 2B (TLR2B), Toll-Like Receptor 4 (TLR4), CXC-Family Chemokine (CXCLi2 / IL8), Interleukin 6 (IL6), Transforming Growth Factor- b4 (TGFb4), Chicken B-cell Marker/chB6 (Bu-1) and Cluster of Differentiation 3 – gamma glycoprotein (CD3g). Relative gene expression values are relative to the control diet with no additives (Control), which is set to be 1.00. Bars indicate the 95% confidence interval (n=8). ........................ 11 Figure 2. Differential gene expression according to mRNA relative abundance of both markers analyzed involved in the intestinal permeability: Claudin 1 and Claudin 3. Relative gene expression values are relative to the control diet with no additives (Control), which is set to be 1.00. Bars indicate the 95% confidence interval (n=8). ............................................................................... 12 VI List of Abbreviations ADFI Average daily feed intake ADG Average daily gain Bu-1 Chicken B-cell Marker CD3g Cluster of Differentiation 3 – Gamma glycoprotein cDNA Complementary Deoxyribonucleic acid CO2 Carbon dioxide Ct Cycle threshold CXCLi2 CXC-Family Chemokine 2 DNA Deoxyribonucleic acid E Efficiency EC European Commission FCR Feed conversion ratio FEDNA Fundación Española para el Desarrollo de la Nutrición Animal GALT Gut-associated lymphoid tissues IFNγ Interferon gamma Ig Immunoglobulin IL2 Interleukin 2 IL6 Interleukin 6 IL8 Interleukin 8 LPS Lipopolysaccharides ME Metabolizable Energy mRNA Messenger Ribonucleic acid n Number of samples ng Nanograms nm Nanometres NTC Non-template control OE Olive extract P P-value PCR Polymerase Chain Reaction PRR Pattern recognition receptor qPCR Quantitative Real-Time Polymerase Chain Reaction RNA Ribonucleic acid TGFb4 Transforming growth factor-b4 VII Th1 T helper 1 lymphocytes Th2 T helper 2 lymphocytes TLR2B Toll-like receptor 2B TLR4 Toll-like receptor 4 UB Ubiquitin WHO World Health Organization µl Microliters VIII SUMMARY The prohibition of antibiotics as growth promoters in animal nutrition has generated the need to find alternatives to enhance intestinal health and function. In this context, the use of plant extracts in animal feeds has been gaining interest because of their anti- inflammatory and anti-oxidant properties. The objective of the present study was to evaluate the effect of supplementing broiler chicken diets with an olive pomace extract from Olea europaea (OE) rich in triterpenic acids on productive parameters and the expression of intestinal health related genes. A total of 224 male broiler chickens (Cobb 500) were used in the study. A completely randomized design with 2 treatments was used, a diet with no additives as a Control and a diet supplemented with 1.000ppm (EO) of an olive pomace extract provided by the company Natac. S.A. Weight controls and feed intake were performed at 7 and 14 days to calculate the average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). After two weeks of feeding the experimental diets (14 days of age), ileum samples were taken to analyze the gene expression of different markers of the immune system and intestinal permeability by real-time PCR (qPCR): Toll-Like Receptor 2B (TLR2B), Toll-Like Receptor 4 (TLR4), CXC-Family Chemokine (CXCLi2), Interleukin 6 (IL6), Transforming Growth Factor-b4 (TGFb4), Chicken B-cell Marker (Bu-1), Cluster of Differentiation 3 - gamma glycoprotein (CD3g), Claudin 1 and Claudin 3. Chickens fed the OE tended (P = 0.06) to have better FCR during the first week of trial. However, this additive did not affect the following week or any of the other productive parameters. The expression of intestinal health related genes was not significantly affected by dietary means. However, broilers fed the OE tended to increase the expression the anti-inflammatory TGFb4 (P = 0.08) and that of CD3g (marker of T lymphocytes). In conclusion the supplementation of starter broiler diets with 1000 ppm of an OE rich in triterpenic acids does not affect productive performance or intestinal health benefits. The tendency of improving FCR during the first week and the expression of TGFb4 deserves future research. IX CHAPTER 1. INTRODUCTION AND OBJECTIVES 1.1. Introduction The use of antibiotics as growth promoters in animal production improves the rate of growth, the conversion of food and reduces mortality and morbidity (Cromwell, 2002). However, bacterial resistance has begun to be generated in animals due to the broad use of antibiotics at sub-therapeutic levels in animal feed. For this reason, the World Health Organization (WHO) suggested the prohibition and withdrawal of antibiotics worldwide for animal consumption (WHO, 2001). Following this regulation, the European Union published Regulation (EC) Nº 1831/2003 informing about changes in the legislation of additives for animal feed. The prohibition of the use of antibiotics,
Load more

Recommended publications
  • ENSG Gene Encodes Effector TCR Pathway Costimulation Inhibitory/Exhaustion Synapse/Adhesion Chemokines/Receptors
    ENSG Gene Encodes Effector TCR pathway Costimulation Inhibitory/exhaustion Synapse/adhesion Chemokines/receptors ENSG00000111537 IFNG IFNg x ENSG00000109471 IL2 IL-2 x ENSG00000232810 TNF TNFa x ENSG00000271503 CCL5 CCL5 x x ENSG00000139187 KLRG1 Klrg1 x ENSG00000117560 FASLG Fas ligand x ENSG00000121858 TNFSF10 TRAIL x ENSG00000134545 KLRC1 Klrc1 / NKG2A x ENSG00000213809 KLRK1 Klrk1 / NKG2D x ENSG00000188389 PDCD1 PD-1 x x ENSG00000117281 CD160 CD160 x x ENSG00000134460 IL2RA IL-2 receptor x subunit alpha ENSG00000110324 IL10RA IL-10 receptor x subunit alpha ENSG00000115604 IL18R1 IL-18 receptor 1 x ENSG00000115607 IL18RAP IL-18 receptor x accessory protein ENSG00000081985 IL12RB2 IL-12 receptor x beta 2 ENSG00000186810 CXCR3 CXCR3 x x ENSG00000005844 ITGAL CD11a x ENSG00000160255 ITGB2 CD18; Integrin x x beta-2 ENSG00000156886 ITGAD CD11d x ENSG00000140678 ITGAX; CD11c x x Integrin alpha-X ENSG00000115232 ITGA4 CD49d; Integrin x x alpha-4 ENSG00000169896 ITGAM CD11b; Integrin x x alpha-M ENSG00000138378 STAT4 Stat4 x ENSG00000115415 STAT1 Stat1 x ENSG00000170581 STAT2 Stat2 x ENSG00000126561 STAT5a Stat5a x ENSG00000162434 JAK1 Jak1 x ENSG00000100453 GZMB Granzyme B x ENSG00000145649 GZMA Granzyme A x ENSG00000180644 PRF1 Perforin 1 x ENSG00000115523 GNLY Granulysin x ENSG00000100450 GZMH Granzyme H x ENSG00000113088 GZMK Granzyme K x ENSG00000057657 PRDM1 Blimp-1 x ENSG00000073861 TBX21 T-bet x ENSG00000115738 ID2 ID2 x ENSG00000176083 ZNF683 Hobit x ENSG00000137265 IRF4 Interferon x regulatory factor 4 ENSG00000140968 IRF8 Interferon
    [Show full text]
  • Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
    Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only.
    [Show full text]
  • SUPPLEMENTARY DATA Supplementary Tables Table S1
    1 SUPPLEMENTARY DATA 2 3 Supplementary Tables 4 Table S1. Gene signatures Gene signature Genes Cytolytic GNLY, KLRK1, KLRB1, GZMH, GZMA, KLRD1, NKG7 Cytotoxic CD8+ T cells CD8A, GZMA, GZMB, IFNG, EOMES, PRF1 Activated CD4+ T cells CD4, IL2RA, CD69 CD4+ Treg cells FOXP3, CTLA4, ICOS Immune checkpoints—T cells BTLA, LAG3, HAVCR2, PDCD1, TIGIT, CTLA4 T cells CD2, CD3D, CD3E, CD3G, CD6, TRAT1, CD28, LCK B cells CD79A, MS4A1, CD19, STAP1, KIAA0125, POU2AF1, FCRL5 NK cells SLAMF7, KLRC3, KLRK1, KLRC2, KLRD1 Monocytes CD14, CD16, CD163, CSF1R, HLA-DR LAPTM5, LAIR1, CD4, CSF1R, CD163, ADAP2, CD68, MRC1, M2 macrophages PTPRC, SLA, SEPSECS, MSR1, FPR3, FCGR2A, FCGR3A, IDO1, RERE, ABL2, CD163L1, STAT3, SBNO2, CSF1, CSF2 FAP, FN1, MMP2, BGN, LOXL2, PDPN, PDGFRB, COL12a1, Active fibroblasts COL5A1, COL8A2, THY1, PALLD Antigen processing TAPBP, TAP1, TAP2, PSMB9, PSMB8 Immune checkpoints—APC CD274, PDCD1LG2, IDO1 Costimulatory ligands CD40, CD80, CD86, CD70, TNFRSF18 CD27, CD28, ICOS, TNFRSF4, TNFRSF14, TNFRSF18, Costimulatory receptors TNFSF14, CD226 Myeloid inflammation CCL2, IL1B, CXCL8, IL6, PTGS2 1 DERL1, DERL2, DNAJB11, DNAJB9, DNAJC10, DNAJC3, ER stress EDEM1, EDEM2, EDEM3, EIF2AK3, ERO1L, HERPUD1, PDIA3, PDIA6, SEC61A1, SERP1, SYVN1 RRM2, UBE2C, BIRC5, CEP55, CCNB1, NUF2, NDC80, Proliferation MKI67, CDC20, TYMS 5 ER, endoplasmic reticulum; NK, natural killer. 6 7 2 8 Table S2. Most common all-cause and treatment-related AEs Patients (N = 45) All cause Treatment related Eventa Any grade Grade 3/4 Any grade Grade 3/4 Any AE 45 (100.0) 20 (44.4)
    [Show full text]
  • CD200, Human Recombinant Recombinant Human CD200, : 408-493-1800 | Fax: 408-493-1801 408-493-1801 Fax: | 408-493-1800
    BioVision 1/14 For research use only CD200, human recombinant CATALOG #: 7309-50 50 µg ALTERNATE NAMES: CD200 molecule, MOX1, MOX2, MRC, OX-2 SOURCE: E. coli PURITY: > 90% by SDS-PAGE MOL. WEIGHT: 24.8 kDa (225 aa, 31-232 aa + His tag), confirmed by MALDI-TOF. ENDOTOXIN LEVEL: < 1.0 EU per 1 µg of protein FORM: Liquid FORMULATION: 1 mg/ml in 20 mM Tris-HCl buffer (pH 8.0) containing 0.4M Urea. CD200, human recombinant STORAGE CONDITIONS: Can be stored at 4°C short term (1-2 weeks). For long term storage, aliquot and store at -20°C or - 70°C. Avoid repeated freezing and thawing RELATED PRODUCTS: cycles. • CD1E, human recombinant (Cat. No. 7308-100) • CD226, human recombinant (Cat. No. 7310-100) DESCRIPTION : CD200 is a type-1 membrane glycoprotein, which contains two • CD274, mouse recombinant (Cat. No. 7311-100) immunoglobulin domains, and thus belongs to the immunoglobulin superfamily. Studies of • CD300C, human recombinant (Cat. No. 7312-100) the related genes in mouse and rat suggest that this gene may regulate myeloid cell • CD3G, human recombinant (Cat. No. 7313-100) activity and delivers an inhibitory signal for the macrophage lineage in diverse tissues. • CD46, human recombinant (Cat. No. 7314-100) Multiple alternatively spliced transcript variants that encode different isoforms have been • CD5, human recombinant (Cat. No. 7315-100) • CD7, human recombinant (Cat. No. 7316-100) found for this gene. Recombinant human CD200 protein, fused to His-tag at N-terminus, • CD74, human recombinant (Cat. No. 7317-100) was expressed in E.coli. • CD79B, human recombinant (Cat.
    [Show full text]
  • Transcriptional Control of Tissue-Resident Memory T Cell Generation
    Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells.
    [Show full text]
  • Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
    ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea.
    [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]
  • 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
    [Show full text]
  • Mutational Landscape Differences Between Young-Onset and Older-Onset Breast Cancer Patients Nicole E
    Mealey et al. BMC Cancer (2020) 20:212 https://doi.org/10.1186/s12885-020-6684-z RESEARCH ARTICLE Open Access Mutational landscape differences between young-onset and older-onset breast cancer patients Nicole E. Mealey1 , Dylan E. O’Sullivan2 , Joy Pader3 , Yibing Ruan3 , Edwin Wang4 , May Lynn Quan1,5,6 and Darren R. Brenner1,3,5* Abstract Background: The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inherited BRCA1 or BRCA2 mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours. Methods: In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages “deconstructSigs” and “SomaticSignatures” to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset. Results: Older patients had a higher proportion of mutations in PIK3CA, CDH1, and MAP3K1 genes, while young- onset patients had a higher proportion of mutations in GATA3 and CTNNB1. Mutational load was lower for young- onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database.
    [Show full text]
  • FOR RESEARCH USE ONLY 03/08V1 Recombinant Human
    Recombinant Human CD3G Datasheet Catalog Number: PR27294 Product Type: Recombinant Protein Source: Sf9, Insect Cells Amino Acid Sequence: ADPQSIKGNH LVKVYDYQED GSVLLTCDAE AKNITWFKDG KMIGFLTEDK KKWNLGSNAK DPRGMYQCKG SQNKSKPLQV YYRMCQNCIE LNAATISHHH HHH. Description/Molecular CD3-gamma polypeptide is encoded by CD3 Gamma (CD3G) which together with CD3-epsilon, -delta Mass: and -zeta, and the T-cell receptor alpha/beta and gamma/delta heterodimers, configures the T-cell receptor-CD3 complex. The T-cell receptor-CD3 complex takes part in coupling antigen recognition to numerous intracellular signal-transduction pathways. The genes encoding the epsilon, gamma and delta polypeptides are located in the same cluster on chromosome 11. Deficiencies in this gene are associated with T cell immunodeficiency. CD3G Human Recombinant produced in Sf9 Insect cells is a single, glycosylated polypeptide chain containing 103 amino acids (23-116 a.a.) and having a molecular mass of 11.8kDa (Molecular size on SDS-PAGE will appear at approximately 13.5-18kDa). CD3G is expressed with a 6 amino acids His tag at C-Terminus and purified by proprietary chromatographic techniques. Purity: Greater than 90.0% as determined by: (a) Analysis by SDS-PAGE. Format: CD3G protein solution (0.5mg/ml) contains phosphate buffered saline (pH7.4) and 10% glycerol. Storage: Store at 4°C if entire vial will be used within 2-4 weeks. Store, frozen at -20°C for longer periods of time. For long term storage, it is recommended to add a carrier protein (0.1% HSA or BSA). Avoid multiple freeze-thaw cycles. FOR RESEARCH USE ONLY NEUROMICS’ REAGENTS ARE FOR IN VITRO AND CERTAIN NON-HUMAN IN VIVO EXPERIMENTAL USE ONLY AND NOT INTENDED FOR USE IN ANY HUMAN CLINICAL INVESTIGATION, DIAGNOSIS, PROGNOSIS, OR TREATMENT.
    [Show full text]
  • ©Ferrata Storti Foundation
    Original Articles T-cell/histiocyte-rich large B-cell lymphoma shows transcriptional features suggestive of a tolerogenic host immune response Peter Van Loo,1,2,3 Thomas Tousseyn,4 Vera Vanhentenrijk,4 Daan Dierickx,5 Agnieszka Malecka,6 Isabelle Vanden Bempt,4 Gregor Verhoef,5 Jan Delabie,6 Peter Marynen,1,2 Patrick Matthys,7 and Chris De Wolf-Peeters4 1Department of Molecular and Developmental Genetics, VIB, Leuven, Belgium; 2Department of Human Genetics, K.U.Leuven, Leuven, Belgium; 3Bioinformatics Group, Department of Electrical Engineering, K.U.Leuven, Leuven, Belgium; 4Department of Pathology, University Hospitals K.U.Leuven, Leuven, Belgium; 5Department of Hematology, University Hospitals K.U.Leuven, Leuven, Belgium; 6Department of Pathology, The Norwegian Radium Hospital, University of Oslo, Oslo, Norway, and 7Department of Microbiology and Immunology, Rega Institute for Medical Research, K.U.Leuven, Leuven, Belgium Citation: Van Loo P, Tousseyn T, Vanhentenrijk V, Dierickx D, Malecka A, Vanden Bempt I, Verhoef G, Delabie J, Marynen P, Matthys P, and De Wolf-Peeters C. T-cell/histiocyte-rich large B-cell lymphoma shows transcriptional features suggestive of a tolero- genic host immune response. Haematologica. 2010;95:440-448. doi:10.3324/haematol.2009.009647 The Online Supplementary Tables S1-5 are in separate PDF files Supplementary Design and Methods One microgram of total RNA was reverse transcribed using random primers and SuperScript II (Invitrogen, Merelbeke, Validation of microarray results by real-time quantitative Belgium), as recommended by the manufacturer. Relative reverse transcriptase polymerase chain reaction quantification was subsequently performed using the compar- Ten genes measured by microarray gene expression profil- ative CT method (see User Bulletin #2: Relative Quantitation ing were validated by real-time quantitative reverse transcrip- of Gene Expression, Applied Biosystems).
    [Show full text]
  • Human CD Marker Chart Reviewed by HLDA1 Bdbiosciences.Com/Cdmarkers
    BD Biosciences Human CD Marker Chart Reviewed by HLDA1 bdbiosciences.com/cdmarkers 23-12399-01 CD Alternative Name Ligands & Associated Molecules T Cell B Cell Dendritic Cell NK Cell Stem Cell/Precursor Macrophage/Monocyte Granulocyte Platelet Erythrocyte Endothelial Cell Epithelial Cell CD Alternative Name Ligands & Associated Molecules T Cell B Cell Dendritic Cell NK Cell Stem Cell/Precursor Macrophage/Monocyte Granulocyte Platelet Erythrocyte Endothelial Cell Epithelial Cell CD Alternative Name Ligands & Associated Molecules T Cell B Cell Dendritic Cell NK Cell Stem Cell/Precursor Macrophage/Monocyte Granulocyte Platelet Erythrocyte Endothelial Cell Epithelial Cell CD1a R4, T6, Leu6, HTA1 b-2-Microglobulin, CD74 + + + – + – – – CD93 C1QR1,C1qRP, MXRA4, C1qR(P), Dj737e23.1, GR11 – – – – – + + – – + – CD220 Insulin receptor (INSR), IR Insulin, IGF-2 + + + + + + + + + Insulin-like growth factor 1 receptor (IGF1R), IGF-1R, type I IGF receptor (IGF-IR), CD1b R1, T6m Leu6 b-2-Microglobulin + + + – + – – – CD94 KLRD1, Kp43 HLA class I, NKG2-A, p39 + – + – – – – – – CD221 Insulin-like growth factor 1 (IGF-I), IGF-II, Insulin JTK13 + + + + + + + + + CD1c M241, R7, T6, Leu6, BDCA1 b-2-Microglobulin + + + – + – – – CD178, FASLG, APO-1, FAS, TNFRSF6, CD95L, APT1LG1, APT1, FAS1, FASTM, CD95 CD178 (Fas ligand) + + + + + – – IGF-II, TGF-b latency-associated peptide (LAP), Proliferin, Prorenin, Plasminogen, ALPS1A, TNFSF6, FASL Cation-independent mannose-6-phosphate receptor (M6P-R, CIM6PR, CIMPR, CI- CD1d R3G1, R3 b-2-Microglobulin, MHC II CD222 Leukemia
    [Show full text]