(12) Patent Application Publication (10) Pub. No.: US 2009/0232893 A1 Bader Et Al
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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. -
The Title of the Article
Mechanism-Anchored Profiling Derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia Xinan Yang, PhD1, Yong Huang, MD1, James L Chen, MD1, Jianming Xie, MSc2, Xiao Sun, PhD2, Yves A Lussier, MD1,3,4§ 1Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637 USA 2State Key Laboratory of Bioelectronics, Southeast University, 210096 Nanjing, P.R.China 3The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL 60637 USA 4The Institute for Genomics and Systems Biology, and the Computational Institute, The University of Chicago, Chicago, IL 60637 USA §Corresponding author Email addresses: XY: [email protected] YH: [email protected] JC: [email protected] JX: [email protected] XS: [email protected] YL: [email protected] - 1 - Abstract Background Current outcome predictors based on “molecular profiling” rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate “mechanism-anchored profiling”. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype. -
Tetraspanin CD53: an Overlooked Regulator of Immune Cell Function
Medical Microbiology and Immunology (2020) 209:545–552 https://doi.org/10.1007/s00430-020-00677-z REVIEW Tetraspanin CD53: an overlooked regulator of immune cell function V. E. Dunlock1 Received: 31 March 2020 / Accepted: 2 May 2020 / Published online: 21 May 2020 © The Author(s) 2020 Abstract Tetraspanins are membrane organizing proteins that play a role in organizing the cell surface through the formation of subcellular domains consisting of tetraspanins and their partner proteins. These complexes are referred to as tetraspanin enriched microdomains (TEMs) or the tetraspanin web. The formation of TEMs allows for the regulation of a variety of cellular processes such as adhesion, migration, signaling, and cell fusion. Tetraspanin CD53 is a member of the tetraspanin superfamily expressed exclusively within the immune compartment. Amongst others, B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and natural killer cells have all been found to express high levels of this protein on their sur- face. Almost three decades ago it was reported that patients who lacked CD53 sufered from an increased susceptibility to pathogens resulting in the clinical manifestation of recurrent viral, bacterial, and fungal infections. This clearly suggests a vital and non-redundant role for CD53 in immune function. Yet, despite this striking fnding, the specifc functional roles of CD53 within the immune system have remained elusive. This review aims to provide a concise overview of the published literature concerning CD53 and refect on the underappreciated role of this protein in immune cell regulation and function. Keywords Tetraspanins · Tetraspanin enriched microdomains · CD53 · Membrane organization · Immune cell signaling · Immune cell adhesion Introduction: tetraspanins in the immune surface or on intracellular membranes. -
Upregulation of Peroxisome Proliferator-Activated Receptor-Α And
Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer Chih-Yang Wang, Ying-Jui Chao, Yi-Ling Chen, Tzu-Wen Wang, Nam Nhut Phan, Hui-Ping Hsu, Yan-Shen Shan, Ming-Derg Lai 1 Supplementary Table 1. Demographics and clinical outcomes of five patients with ampullary cancer Time of Tumor Time to Age Differentia survival/ Sex Staging size Morphology Recurrence recurrence Condition (years) tion expired (cm) (months) (months) T2N0, 51 F 211 Polypoid Unknown No -- Survived 193 stage Ib T2N0, 2.41.5 58 F Mixed Good Yes 14 Expired 17 stage Ib 0.6 T3N0, 4.53.5 68 M Polypoid Good No -- Survived 162 stage IIA 1.2 T3N0, 66 M 110.8 Ulcerative Good Yes 64 Expired 227 stage IIA T3N0, 60 M 21.81 Mixed Moderate Yes 5.6 Expired 16.7 stage IIA 2 Supplementary Table 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of an ampullary cancer microarray using the Database for Annotation, Visualization and Integrated Discovery (DAVID). This table contains only pathways with p values that ranged 0.0001~0.05. KEGG Pathway p value Genes Pentose and 1.50E-04 UGT1A6, CRYL1, UGT1A8, AKR1B1, UGT2B11, UGT2A3, glucuronate UGT2B10, UGT2B7, XYLB interconversions Drug metabolism 1.63E-04 CYP3A4, XDH, UGT1A6, CYP3A5, CES2, CYP3A7, UGT1A8, NAT2, UGT2B11, DPYD, UGT2A3, UGT2B10, UGT2B7 Maturity-onset 2.43E-04 HNF1A, HNF4A, SLC2A2, PKLR, NEUROD1, HNF4G, diabetes of the PDX1, NR5A2, NKX2-2 young Starch and sucrose 6.03E-04 GBA3, UGT1A6, G6PC, UGT1A8, ENPP3, MGAM, SI, metabolism -
Mass Spectrometry-Based Proteomics Techniques and Their Application in Ovarian Cancer Research Agata Swiatly, Szymon Plewa, Jan Matysiak and Zenon J
Swiatly et al. Journal of Ovarian Research (2018) 11:88 https://doi.org/10.1186/s13048-018-0460-6 REVIEW Open Access Mass spectrometry-based proteomics techniques and their application in ovarian cancer research Agata Swiatly, Szymon Plewa, Jan Matysiak and Zenon J. Kokot* Abstract Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008–2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. -
Tumor-Associated Antigens Identified Early in Mouse Mammary Tumor Development Can Be Effective Vaccine Targets
Vaccine 37 (2019) 3552–3561 Contents lists available at ScienceDirect Vaccine journal homepage: www.elsevier.com/locate/vaccine Tumor-associated antigens identified early in mouse mammary tumor development can be effective vaccine targets ⇑ Sasha E. Stanton a, , Ekram Gad a, Lauren R. Corulli a, Hailing Lu a,1, Mary L. Disis a a Cancer Vaccine Institute, University of Washington, Seattle WA, 98109, USA article info abstract Article history: Breast cancer vaccines composed of antigens identified by serological analysis of cDNA expression Received 25 June 2018 libraries (SEREX) induce antigen specific immune responses in patients but have had disappointing clin- Received in revised form 5 April 2019 ical benefits. While many attempts to modify the adjuvants and vaccine method have been tried, one Accepted 9 May 2019 issue not addressed was whether the SEREX tumor-associated antigens identified from late stages of dis- Available online 21 May 2019 ease were ideal targets. We questioned in the transgenic TgMMTV-neu mouse model whether the antigen repertoire is distinct between early and late stage breast cancer and whether the antigens identified via Keywords: SEREX from transgenic mice with early or late stage tumors would elicit differential anti-tumor effects to Breast cancer prevention address this question. Vaccine antigens Th1 Three early stage antigens, Pdhx, Stk39, and Otud6B, were identified from a SEREX screen of mice prior DNA vaccines to development of palpable lesions. Formulated into a vaccine, each early antigen inhibited tumor growth Mouse mammary tumor models (p < 0.0001). The antigens identified from mice with late stage tumors (Swap70, Gsn, and Arhgef2) were unable to inhibit tumor growth when used as vaccines (for example Gsn p = 0.26). -
Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
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. -
Studies on the Proteome of Human Hair - Identifcation of Histones and Deamidated Keratins Received: 15 August 2017 Sunil S
www.nature.com/scientificreports OPEN Studies on the Proteome of Human Hair - Identifcation of Histones and Deamidated Keratins Received: 15 August 2017 Sunil S. Adav 1, Roopa S. Subbaiaih2, Swat Kim Kerk 2, Amelia Yilin Lee 2,3, Hui Ying Lai3,4, Accepted: 12 January 2018 Kee Woei Ng3,4,7, Siu Kwan Sze 1 & Artur Schmidtchen2,5,6 Published: xx xx xxxx Human hair is laminar-fbrous tissue and an evolutionarily old keratinization product of follicle trichocytes. Studies on the hair proteome can give new insights into hair function and lead to the development of novel biomarkers for hair in health and disease. Human hair proteins were extracted by detergent and detergent-free techniques. We adopted a shotgun proteomics approach, which demonstrated a large extractability and variety of hair proteins after detergent extraction. We found an enrichment of keratin, keratin-associated proteins (KAPs), and intermediate flament proteins, which were part of protein networks associated with response to stress, innate immunity, epidermis development, and the hair cycle. Our analysis also revealed a signifcant deamidation of keratin type I and II, and KAPs. The hair shafts were found to contain several types of histones, which are well known to exert antimicrobial activity. Analysis of the hair proteome, particularly its composition, protein abundances, deamidated hair proteins, and modifcation sites, may ofer a novel approach to explore potential biomarkers of hair health quality, hair diseases, and aging. Hair is an important and evolutionarily conserved structure. It originates from hair follicles deep within the der- mis and is mainly composed of hair keratins and KAPs, which form a complex network that contributes to the rigidity and mechanical properties. -
Table SD1. Patient Characteristicsa
Table SD1. Patient characteristicsa Patient Sex Age Esophageal Treatment Maximum Cell Maximum Maximum Genotype Food SPT/Ab SPT/F b RAST b Rhinitisc Atopicc Asthmac Alternative diagnosis Dated (year) Disease eosinophils thickness in mast cells lymphocytes anaphylaxis (positive dermatitis /hpf basal layer /hpf /hpf reaction) 1 M 11 NL None 0 3 5 3 Unk No ND ND ND Yes No No Recurrent croup December 2 M 11 NL LTRA 0 3 4 3 Unk No ND ND ND No No Yes Functional abdominal pain May 3 F 9 NL None 0 3 4 3 Unk Unk ND ND ND Unk Unk Unk Functional abdominal pain March 4 M 14 NL None 0 2 6 4 Unk No ND ND ND No No No Vomiting/diarrhea Febuary 5 F 7 NL LTRA 0 3 5 6 Unk Yes 1 3 ND Unk Unk Yes Functional abdominal pain March 6 F 13 NL None 0 2 4 2 Unk No 0 4 ND No No No Functional abdominal pain August 7 M 17 CE PPI 0 4 7 12 Unk Yes 17 4 ND No No Yes None November 8 M 6 CE PPI 0 4 6 10 Unk Unk ND ND ND Unk Unk Unk None June 9 F 16 CE LTRA 3 4 6 8 Unk No ND ND ND No No Yes None January 10 F 13 CE LTRA+PPI 3 5 4 8 Unk No 0 0 ND No No No None August 11 F 11 CE LTRA+PPI 6 4 6 9 Unk Unk ND ND ND Unk Unk Unk None May 12 M 11 EE PPI 24 6 6 12 TT No 2 5 3 Yes No Yes None November 13 F 4 EE PPI 25 6 15 15 TT No 0 0 ND No No No None November 14 M 15 EE None 30 6 24 6 TG Unk 1 1 ND Unk Unk Yes None February 15 M 15 EE None 31 6 15 11 TT No 8 2 ND Yes No Yes None March 16 M 13 EE PPI 32 6 10 25 TG No 0 0 ND No No No None June 17 M 6 EE PPI 40 7 10 21 TT Yes 5 3 0 Unk Unk No None November 18 M 13 EE LTRA 42 7 10 5 TT No 4 5 2 Yes Yes Yes None November 19 F 16 EE LTRA+PPI -
Longitudinal Peripheral Blood Transcriptional Analysis of COVID-19 Patients
medRxiv preprint doi: https://doi.org/10.1101/2020.05.05.20091355; this version posted May 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. 1 Longitudinal peripheral blood transcriptional analysis of COVID-19 patients 2 captures disease progression and reveals potential biomarkers 3 Qihong Yan1,5,†, Pingchao Li1,†, Xianmiao Ye1,†, Xiaohan Huang1,5,†, Xiaoneng Mo2, 4 Qian Wang1, Yudi Zhang1, Kun Luo1, Zhaoming Chen1, Jia Luo1, Xuefeng Niu3, Ying 5 Feng3, Tianxing Ji3, Bo Feng3, Jinlin Wang2, Feng Li2, Fuchun Zhang2, Fang Li2, 6 Jianhua Wang1, Liqiang Feng1, Zhilong Chen4,*, Chunliang Lei2,*, Linbing Qu1,*, Ling 7 Chen1,2,3,4,* 8 1Guangzhou Regenerative Medicine and Health-Guangdong Laboratory 9 (GRMH-GDL), Guangdong Laboratory of Computational Biomedicine, Guangzhou 10 Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 11 China 12 2Guangzhou Institute of Infectious Disease, Guangzhou Eighth People’s Hospital, 13 Guangzhou Medical University, Guangzhou, China 14 3State Key Laboratory of Respiratory Disease, National Clinical Research Center for 15 Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated 16 Hospital of Guangzhou Medical University, Guangzhou, China 17 4School of Medicine, Huaqiao University, Xiamen, China 18 5University of Chinese Academy of Science, Beijing, China 19 †These authors contributed equally to this work. 20 *To whom correspondence should be addressed: Ling Chen ([email protected]), 21 Linbing Qu ([email protected]), Chunliang Lei ([email protected]), Zhilong 22 Chen ([email protected]) NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. -
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