Interleukins in Cancer: from Biology to Therapy
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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 -
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. -
Type I Interferons and the Development of Impaired Vascular Function and Repair in Human and Murine Lupus
Type I Interferons and the Development of Impaired Vascular Function and Repair in Human and Murine Lupus by Seth G Thacker A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Immunology) in The University of Michigan 2011 Doctoral Committee: Associate Professor Mariana J. Kaplan, Chair Professor David A. Fox Professor Alisa E. Koch Professor Matthias Kretzler Professor Nicholas W. Lukacs Associate Professor Daniel T. Eitzman © Seth G Thacker 2011 Sharon, this work is dedicated to you. This achievement is as much yours as it is mine. Your support through all six years of this Ph.D. process has been incredible. You put up with my countless miscalculations on when I would finish experiments, and still managed to make me and our kids feel loved and special. Without you this would have no meaning. Sharon, you are the safe harbor in my life. ii Acknowledgments I have been exceptionally fortunate in my time here at the University of Michigan. I have been able to interact with so many supportive people over the years. I would like to express my thanks and admiration for my mentor. Mariana has taught me so much about writing, experimental design and being a successful scientist in general. I could never have made it here without her help. I would also like to thank Mike Denny. He had a hand in the beginning of all of my projects in one way or another, and was always quick and eager to help in whatever way he could. He really made my first year in the lab successful. -
Tools for Cell Therapy and Immunoregulation
RnDSy-lu-2945 Tools for Cell Therapy and Immunoregulation Target Cell TIM-4 SLAM/CD150 BTNL8 PD-L2/B7-DC B7-H1/PD-L1 (Human) Unknown PD-1 B7-1/CD80 TIM-1 SLAM/CD150 Receptor TIM Family SLAM Family Butyrophilins B7/CD28 Families T Cell Multiple Co-Signaling Molecules Co-stimulatory Co-inhibitory Ig Superfamily Regulate T Cell Activation Target Cell T Cell Target Cell T Cell B7-1/CD80 B7-H1/PD-L1 T cell activation requires two signals: 1) recognition of the antigenic peptide/ B7-1/CD80 B7-2/CD86 CTLA-4 major histocompatibility complex (MHC) by the T cell receptor (TCR) and 2) CD28 antigen-independent co-stimulation induced by interactions between B7-2/CD86 B7-H1/PD-L1 B7-1/CD80 co-signaling molecules expressed on target cells, such as antigen-presenting PD-L2/B7-DC PD-1 ICOS cells (APCs), and their T cell-expressed receptors. Engagement of the TCR in B7-H2/ICOS L 2Ig B7-H3 (Mouse) the absence of this second co-stimulatory signal typically results in T cell B7-H1/PD-L1 B7/CD28 Families 4Ig B7-H3 (Human) anergy or apoptosis. In addition, T cell activation can be negatively regulated Unknown Receptors by co-inhibitory molecules present on APCs. Therefore, integration of the 2Ig B7-H3 Unknown B7-H4 (Mouse) Receptors signals transduced by co-stimulatory and co-inhibitory molecules following TCR B7-H5 4Ig B7-H3 engagement directs the outcome and magnitude of a T cell response Unknown Ligand (Human) B7-H5 including the enhancement or suppression of T cell proliferation, B7-H7 Unknown Receptor differentiation, and/or cytokine secretion. -
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 -
Targeting Tgfβ Signal Transduction for Cancer Therapy
Signal Transduction and Targeted Therapy www.nature.com/sigtrans REVIEW ARTICLE OPEN Targeting TGFβ signal transduction for cancer therapy Sijia Liu1, Jiang Ren1 and Peter ten Dijke1 Transforming growth factor-β (TGFβ) family members are structurally and functionally related cytokines that have diverse effects on the regulation of cell fate during embryonic development and in the maintenance of adult tissue homeostasis. Dysregulation of TGFβ family signaling can lead to a plethora of developmental disorders and diseases, including cancer, immune dysfunction, and fibrosis. In this review, we focus on TGFβ, a well-characterized family member that has a dichotomous role in cancer progression, acting in early stages as a tumor suppressor and in late stages as a tumor promoter. The functions of TGFβ are not limited to the regulation of proliferation, differentiation, apoptosis, epithelial–mesenchymal transition, and metastasis of cancer cells. Recent reports have related TGFβ to effects on cells that are present in the tumor microenvironment through the stimulation of extracellular matrix deposition, promotion of angiogenesis, and suppression of the anti-tumor immune reaction. The pro-oncogenic roles of TGFβ have attracted considerable attention because their intervention provides a therapeutic approach for cancer patients. However, the critical function of TGFβ in maintaining tissue homeostasis makes targeting TGFβ a challenge. Here, we review the pleiotropic functions of TGFβ in cancer initiation and progression, summarize the recent clinical advancements regarding TGFβ signaling interventions for cancer treatment, and discuss the remaining challenges and opportunities related to targeting this pathway. We provide a perspective on synergistic therapies that combine anti-TGFβ therapy with cytotoxic chemotherapy, targeted therapy, radiotherapy, or immunotherapy. -
United States Atopic Dermatitis (AD) Market Report 2021
Source: Research and Markets July 28, 2021 04:08 ET United States Atopic Dermatitis (AD) Market Report 2021 Dublin, July 28, 2021 (GLOBE NEWSWIRE) -- The "Atopic Dermatitis (AD) - US Market Insight, Epidemiology and Market Forecast - 2030" report has been added to ResearchAndMarkets.com's offering. This 'Atopic Dermatitis (AD) - Market Insights, Epidemiology and Market Forecast- 2030' report delivers an in- depth understanding of the Atopic Dermatitis (AD), historical and forecasted epidemiology as well as the Atopic Dermatitis (AD) market trends in the United States. Key Findings The total prevalent population of AD in the United States was estimated to be 32,197,083 in 2020. The total diagnosed prevalent population of AD in the United States was estimated to be 25,091,967 in 2020. The prevalent population of AD in the United States is expected to increase at a CAGR of 0.58% during the study period 2018-2030. In the United States, the total number of adult cases of AD comprised of 5,684,566 males and 9,421,907 females in 2020. The total number of cases of mild AD were 9,065,394 in the United States, in 2020, as compared to the cases of moderate and severe AD with 4,365,771 and 1,661,712 cases respectively, in adults. In children, the total number of cases of mild AD were 6,690,281, in 2020, as compared to the cases of moderate and severe AD with 2,596,228 and 698,985 cases respectively, in the United States. Report Highlights In the coming years, Atopic Dermatitis (AD) market is set to change due to the rising awareness of the disease, and incremental healthcare spending across the world; which would expand the size of the market to enable the drug manufacturers to penetrate more into the market. -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
Genetic Determinants of Circulating Interleukin-1 Receptor Antagonist Levels and Their Association with Glycemic Traits
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Trepo - Institutional Repository of Tampere University GENETIC DETERMINANTS OF CIRCULATING INTERLEUKIN-1 RECEPTOR ANTAGONIST LEVELS AND THEIR ASSOCIATION WITH GLYCEMIC TRAITS Marja-Liisa Nuotio Syventävien opintojen kirjallinen työ Tampereen yliopisto Lääketieteen yksikkö Tammikuu 2015 Tampereen yliopisto Lääketieteen yksikkö NUOTIO MARJA-LIISA: GENETIC DETERMINANTS OF CIRCULATING INTERLEUKIN-1 RECEPTOR ANTAGONIST LEVELS AND THEIR ASSOCIATION WITH GLYCEMIC TRAITS Kirjallinen työ, 57 s. Ohjaaja: professori Mika Kähönen Tammikuu 2015 Avainsanat: sytokiinit, insuliiniresistenssi, tyypin 2 diabetes, tulehdus, glukoosimetabolia, genominlaajuinen assosiaatioanalyysi (GWAS) Tulehdusta välittäviin sytokiineihin kuuluvan interleukiini 1β (IL-1β):n kohonneen systeemisen pitoisuuden on arveltu edesauttavan insuliiniresistenssin kehittymistä ja johtavan haiman β-solujen toimintahäiriöihin. IL-1β:n sisäsyntyisellä vastavaikuttajalla, interleukiini 1 reseptoriantagonistilla (IL-1RA), on puolestaan esitetty olevan suojaava rooli mainittujen fenotyyppien kehittymisessä päinvastaisten vaikutustensa ansiosta. IL-1RA:n suojaavan roolin havainnollistamiseksi työssä Genetic determinants of circulating interleukin-1 receptor antagonist levels and their association with glycemic traits tunnistettiin veren IL-1RA- pitoisuuteen assosioituvia geneettisiä variantteja, minkä jälkeen selvitettiin näiden yhteyttä glukoosi- ja insuliinimetaboliaan liittyvien muuttujien-, sekä -
Inflammation-Dependent IL18 Signaling Restricts Hepatocellular Carcinoma Growth by Enhancing the Accumulation and Activity of Tumor-Infiltrating Lymphocytes
Published OnlineFirst February 18, 2016; DOI: 10.1158/0008-5472.CAN-15-1548 Cancer Tumor and Stem Cell Biology Research Inflammation-Dependent IL18 Signaling Restricts Hepatocellular Carcinoma Growth by Enhancing the Accumulation and Activity of Tumor- Infiltrating Lymphocytes Geoffrey J. Markowitz1, Pengyuan Yang1,2,3, Jing Fu3, Gregory A. Michelotti4, Rui Chen1, Jianhua Sui5, Bin Yang2, Wen-Hao Qin3, Zheng Zhang6, Fu-Sheng Wang6, Anna Mae Diehl4, Qi-Jing Li7, Hongyang Wang3, and Xiao-Fan Wang1 Abstract Chronic inflammation in liver tissue is an underlying cause of IL18R1 deletion increased tumor burden. Mechanistically, we hepatocellular carcinoma. High levels of inflammatory cytokine foundthatIL18exertedinflammation-dependent tumor-sup- IL18 in the circulation of patients with hepatocellular carcinoma pressive effects largely by promoting the differentiation, activ- correlates with poor prognosis. However, conflicting results have ity, and survival of tumor-infiltrating T cells. Finally, differences been reported for IL18 in hepatocellular carcinoma development in the expression of IL18 in tumor tissue versus nontumor and progression. In this study, we used tissue specimens from tissueweremorepredictiveofpatientoutcomethanoverall hepatocellular carcinoma patients and clinically relevant mouse tissue expression. Taken together, our findings resolve a long- models of hepatocellular carcinoma to evaluate IL18 expression standing contradiction regarding a tumor-suppressive role for and function. In a mouse model of liver fibrosis that recapitulates IL18 in established hepatocellular carcinoma and provide a a tumor-promoting microenvironment, global deletion of the mechanistic explanation for the complex relationship between IL18 receptor IL18R1 enhanced tumor growth and burden. Sim- its expression pattern and hepatocellular carcinoma prognosis. ilarly, in a carcinogen-induced model of liver tumorigenesis, Cancer Res; 76(8); 1–12. -
Regulation of Osteoblast Differentiation by Cytokine Networks
International Journal of Molecular Sciences Review Regulation of Osteoblast Differentiation by Cytokine Networks Dulshara Sachini Amarasekara 1, Sumi Kim 2 and Jaerang Rho 2,* 1 Department of Zoology and Environment Sciences, Faculty of Science, University of Colombo, Colombo 00300, Sri Lanka; [email protected] 2 Department of Microbiology and Molecular Biology, Chungnam National University, Daejeon 34134, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-42-821-6420; Fax: +82-42-822-7367 Abstract: Osteoblasts, which are bone-forming cells, play pivotal roles in bone modeling and remod- eling. Osteoblast differentiation, also known as osteoblastogenesis, is orchestrated by transcription factors, such as runt-related transcription factor 1/2, osterix, activating transcription factor 4, special AT-rich sequence-binding protein 2 and activator protein-1. Osteoblastogenesis is regulated by a network of cytokines under physiological and pathophysiological conditions. Osteoblastogenic cytokines, such as interleukin-10 (IL-10), IL-11, IL-18, interferon-γ (IFN-γ), cardiotrophin-1 and oncostatin M, promote osteoblastogenesis, whereas anti-osteoblastogenic cytokines, such as tumor necrosis factor-α (TNF-α), TNF-β, IL-1α, IL-4, IL-7, IL-12, IL-13, IL-23, IFN-α, IFN-β, leukemia inhibitory factor, cardiotrophin-like cytokine, and ciliary neurotrophic factor, downregulate os- teoblastogenesis. Although there are gaps in the body of knowledge regarding the interplay of cytokine networks in osteoblastogenesis, cytokines appear to be potential therapeutic targets in bone- related diseases. Thus, in this study, we review and discuss our osteoblast, osteoblast differentiation, osteoblastogenesis, cytokines, signaling pathway of cytokine networks in osteoblastogenesis. Keywords: osteoblast; osteoblast differentiation; osteoblastogenesis; cytokine; signaling pathway Citation: Amarasekara, D.S.; Kim, S.; Rho, J.