Receptor Signaling Through Osteoclast-Associated Monocyte
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Acute Lymphoblastic Leukemia Patient with Variant ATF7IP
ISSN 1941-5923 © Am J Case Rep, 2017; 18: 1204-1208 DOI: 10.12659/AJCR.906300 Received: 2017.07.20 Accepted: 2017.08.07 Acute Lymphoblastic Leukemia Patient with Published: 2017.11.14 Variant ATF7IP/PDGFRB Fusion and Favorable Response to Tyrosine Kinase Inhibitor Treatment: A Case Report Authors’ Contribution: BCDF 1,2 Ge Zhang* 1 Department of Pediatrics, Key Laboratory of Birth Defects and Related Diseases Study Design A BCD 1 Yanle Zhang* of Women and Children, Ministry of Health, West China Second University Data Collection B Hospital, Sichuan University, Chengdu, Sichuan, P.R. China Statistical Analysis C BD 1 Jianrong Wu 2 Department of Laboratory Medicine, West China Second University Hospital, Data Interpretation D AE 3 Yan Chen Sichuan University, Chengdu, Sichuan, P.R. China Manuscript Preparation E AE 1 Zhigui Ma 3 Department of Pediatrics, Affiliated Hospital of Zunyi Medical College, Zunyi, Literature Search F Guizhou, P.R. China Funds Collection G * Ge Zhang and Yanle Zhang contributed equally to this work Corresponding Authors: Zhigui Ma, e-mail: [email protected]; Yan Chen, e-mail: [email protected] Conflict of interest: None declared Patient: Female, 14-month-old Final Diagnosis: Acute lymphoblastic leukemia Symptoms: Fever Medication: — Clinical Procedure: — Specialty: Hematology Objective: Rare disease Background: Chromosomal translocations involving the PDGFRB gene have been reported in a broad spectrum of hemato- logical malignancies. An ATF7IP/PDGFRB fusion was recently identified in a Philadelphia chromosome-like (Ph- like) B-progenitor acute lymphoblastic leukemia (B-ALL) patient. Here we report on a special case of a Ph-like ALL patient who had a variant ATF7IP/PDGFRB fusion. -
Cytogenomic SNP Microarray - Fetal ARUP Test Code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells
Patient Report |FINAL Client: Example Client ABC123 Patient: Patient, Example 123 Test Drive Salt Lake City, UT 84108 DOB 2/13/1987 UNITED STATES Gender: Female Patient Identifiers: 01234567890ABCD, 012345 Physician: Doctor, Example Visit Number (FIN): 01234567890ABCD Collection Date: 00/00/0000 00:00 Cytogenomic SNP Microarray - Fetal ARUP test code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells Single fetal genotype present; no maternal cells present. Fetal and maternal samples were tested using STR markers to rule out maternal cell contamination. This result has been reviewed and approved by Maternal Specimen Yes Cytogenomic SNP Microarray - Fetal Abnormal * (Ref Interval: Normal) Test Performed: Cytogenomic SNP Microarray- Fetal (ARRAY FE) Specimen Type: Direct (uncultured) villi Indication for Testing: Patient with 46,XX,t(4;13)(p16.3;q12) (Quest: EN935475D) ----------------------------------------------------------------- ----- RESULT SUMMARY Abnormal Microarray Result (Male) Unbalanced Translocation Involving Chromosomes 4 and 13 Classification: Pathogenic 4p Terminal Deletion (Wolf-Hirschhorn syndrome) Copy number change: 4p16.3p16.2 loss Size: 5.1 Mb 13q Proximal Region Deletion Copy number change: 13q11q12.12 loss Size: 6.1 Mb ----------------------------------------------------------------- ----- RESULT DESCRIPTION This analysis showed a terminal deletion (1 copy present) involving chromosome 4 within 4p16.3p16.2 and a proximal interstitial deletion (1 copy present) involving chromosome 13 within 13q11q12.12. This -
Expression Level and Prognostic Potential of MAP3K8 in Human Cancers Based on Data Mining
Expression Level and Prognostic Potential of MAP3K8 in Human Cancers Based on Data Mining Jiatao Hao Xi'an Jiaotong University Medical College First Aliated Hospital Yumeng Cao China Medical University Hui Yu Xi'an Jiaotong University Medical College First Aliated Hospital Lu Zong Xi'an Jiaotong University Medical College First Aliated Hospital Ruifang An Xi'an Jiaotong University Medical College First Aliated Hospital Yan Xue ( [email protected] ) Xi'an Jiaotong University Medical College First Aliated Hospital https://orcid.org/0000-0002-6061- 1706 Primary research Keywords: MAPK kinase kinase 8 (MAP3K8), expression, methylation, prognosis, protein-protein interaction (PPI), functional enrichment analysis Posted Date: March 9th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-276370/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/22 Abstract Background: MAPK kinase kinase 8 (MAP3K8) is a member of the MAP3K family with a major role in the regulation of the MAPK pathway and immune response. Differential expression of MAP3K8 is closely correlated with tumorigenesis. In this study, we used bioinformatics tools to explore expression level, prognostic values, and interactive networks of MAP3K8 in human cancers. Methods: Expression prole of MAP3K8 was analyzed using the Oncomine Platform, the Gene Expression Proling Interactive Analysis (GEPIA), and UALCAN. Survival analysis was evaluated via UALCAN, GEPIA, and DriverDBv3 databases. Then, MAP3K8 related functional networks were explored within GeneMANIA and Cytoscape. Moreover, Metascape was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: We found that MAP3K8 was down-expressed in most cancer samples compared with paired normal tissues. -
Supplemental Table S1
Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1 -
Induction of Antitumor Immunity by Transduction of CD40 Ligand Gene
D2001 Nature Publishing Group 0929-1903/01/$17.00/+0 www.nature.com/cgt Induction of antitumor immunity by transduction of CD40 ligand gene and interferon- gene into lung cancer Masahiro Noguchi,1 Kazuyoshi Imaizumi,1 Tsutomu Kawabe,1 Hisashi Wakayama,1 Yoshitsugu Horio,1 Yoshitaka Sekido,2 Toru Hara,1 Naozumi Hashimoto,1 Masahide Takahashi,3 Kaoru Shimokata,2 and Yoshinori Hasegawa1 1First Department of Internal Medicine, Nagoya University School of Medicine, Nagoya, Japan; Departments of 2Clinical Preventive Medicine and 3Pathology, Nagoya University School of Medicine, Nagoya, Japan. CD40±CD40 ligand (CD40L) interaction is an important costimulatory signaling pathway in the crosstalk between T cells and antigen-presenting cells. This receptor±ligand system is known to be essential in eliciting strong cellular immunity. Here we demonstrate that murine lung cancer cells (3LLSA) transduced with the CD40L gene (3LLSA-CD40L) were rejected in syngeneic C57BL/6 mice, but grew in CD40-deficient mice to the same extent as control tumor cells. Immunohistochemical study showed that inflammatory cells, including CD4+, CD8+ T cells and NK cells, infiltrated into the inoculated 3LLSA-CD40L tumor tissue. Inoculation of 3LLSA-CD40L cells into mice resulted in the induction of 3LLSA-specific cytotoxic T-cell immunity, and the growth of parental 3LLSA tumors was inhibited when 3LLSA cells were inoculated into C57BL/6 mice mixed with 3LLSA-CD40L cells or when they were rechallenged 4 weeks after 3LLSA-CD40L cells were rejected. Furthermore, co-inoculation of interferon (IFN)- ± transduced cells (3LLSA-IFN ) with 3LLSA-CD40L cells enhanced the antitumor immunity efficiently in vivo. These results indicate that the in vivo priming with CD40L- and IFN- gene±transduced lung cancer cells is a promising strategy for inducing antitumor immunity in the treatment of lung cancer. -
Laboratory Mouse Models for the Human Genome-Wide Associations
Laboratory Mouse Models for the Human Genome-Wide Associations The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Kitsios, Georgios D., Navdeep Tangri, Peter J. Castaldi, and John P. A. Ioannidis. 2010. Laboratory mouse models for the human genome-wide associations. PLoS ONE 5(11): e13782. Published Version doi:10.1371/journal.pone.0013782 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:8592157 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Laboratory Mouse Models for the Human Genome-Wide Associations Georgios D. Kitsios1,4, Navdeep Tangri1,6, Peter J. Castaldi1,2,4,5, John P. A. Ioannidis1,2,3,4,5,7,8* 1 Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, United States of America, 2 Tufts University School of Medicine, Boston, Massachusetts, United States of America, 3 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine and Biomedical Research Institute, Foundation for Research and Technology-Hellas, Ioannina, Greece, 4 Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, United States of America, 5 Department of Medicine, Center for Genetic Epidemiology and Modeling, Tufts Medical Center, Tufts University -
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. -
Pro-Inflammatory Tnfα and IL-1Β Differentially Regulate the Inflammatory Phenotype of Brain Microvascular Endothelial Cells Simon J
O’Carroll et al. Journal of Neuroinflammation (2015) 12:131 JOURNAL OF DOI 10.1186/s12974-015-0346-0 NEUROINFLAMMATION RESEARCH Open Access Pro-inflammatory TNFα and IL-1β differentially regulate the inflammatory phenotype of brain microvascular endothelial cells Simon J. O’Carroll1,2, Dan Ting Kho1,3, Rachael Wiltshire1, Vicky Nelson1,3, Odunayo Rotimi1,2, Rebecca Johnson1,3, Catherine E. Angel4 and E. Scott Graham1,3* Abstract Background: The vasculature of the brain is composed of endothelial cells, pericytes and astrocytic processes. The endothelial cells are the critical interface between the blood and the CNS parenchyma and are a critical component of the blood-brain barrier (BBB). These cells are innately programmed to respond to a myriad of inflammatory cytokines or other danger signals. IL-1β and TNFα are well recognised pro-inflammatory mediators, and here, we provide compelling evidence that they regulate the function and immune response profile of human cerebral microvascular endothelial cells (hCMVECs) differentially. Methods: We used xCELLigence biosensor technology, which revealed global differences in the endothelial response between IL-1β and TNFα. xCELLigence is a label-free impedance-based biosensor, which is ideal for acute or long-term comparison of drug effects on cell behaviour. In addition, flow cytometry and multiplex cytokine arrays were used to show differences in the inflammatory responses from the endothelial cells. Results: Extensive cytokine-secretion profiling and cell-surface immune phenotyping confirmed that the immune response of the hCMVEC to IL-1β was different to that of TNFα. Interestingly, of the 38 cytokines, chemokines and growth factors measured by cytometric bead array, the endothelial cells secreted only 13. -
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, -
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 -
CD29 Identifies IFN-Γ–Producing Human CD8+ T Cells With
+ CD29 identifies IFN-γ–producing human CD8 T cells with an increased cytotoxic potential Benoît P. Nicoleta,b, Aurélie Guislaina,b, Floris P. J. van Alphenc, Raquel Gomez-Eerlandd, Ton N. M. Schumacherd, Maartje van den Biggelaarc,e, and Monika C. Wolkersa,b,1 aDepartment of Hematopoiesis, Sanquin Research, 1066 CX Amsterdam, The Netherlands; bLandsteiner Laboratory, Oncode Institute, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; cDepartment of Research Facilities, Sanquin Research, 1066 CX Amsterdam, The Netherlands; dDivision of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands; and eDepartment of Molecular and Cellular Haemostasis, Sanquin Research, 1066 CX Amsterdam, The Netherlands Edited by Anjana Rao, La Jolla Institute for Allergy and Immunology, La Jolla, CA, and approved February 12, 2020 (received for review August 12, 2019) Cytotoxic CD8+ T cells can effectively kill target cells by producing therefore developed a protocol that allowed for efficient iso- cytokines, chemokines, and granzymes. Expression of these effector lation of RNA and protein from fluorescence-activated cell molecules is however highly divergent, and tools that identify and sorting (FACS)-sorted fixed T cells after intracellular cytokine + preselect CD8 T cells with a cytotoxic expression profile are lacking. staining. With this top-down approach, we performed an un- + Human CD8 T cells can be divided into IFN-γ– and IL-2–producing biased RNA-sequencing (RNA-seq) and mass spectrometry cells. Unbiased transcriptomics and proteomics analysis on cytokine- γ– – + + (MS) analyses on IFN- and IL-2 producing primary human producing fixed CD8 T cells revealed that IL-2 cells produce helper + + + CD8 Tcells. -
Gene Expression Analysis of Angioimmunoblastic Lymphoma Indicates Derivation from T Follicular Helper Cells and Vascular Endothelial Growth Factor Deregulation
Research Article Gene Expression Analysis of Angioimmunoblastic Lymphoma Indicates Derivation from T Follicular Helper Cells and Vascular Endothelial Growth Factor Deregulation Pier Paolo Piccaluga,1,2 Claudio Agostinelli,1 Andrea Califano,3 Antonino Carbone,4 Luca Fantoni,6 Sergio Ferrari,5 Anna Gazzola,1 Annunziata Gloghini,6 Simona Righi,1 Maura Rossi,1 Enrico Tagliafico,5 Pier Luigi Zinzani,1 Simonetta Zupo,7 Michele Baccarani,1 and Stefano A. Pileri1 1Institute of Hematology and Medical Oncology ‘‘L. and A. Sera`gnoli,’’ S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy; 2Institute for Cancer Genetics and 3Center for Computational Biology and Biochemistry, Columbia University, New York, New York; 4Department of Pathology, Istituto Nazionale Tumori, Milan, Italy; 5Department of Biomedical Sciences, University of Modena and Reggio Emilia, Modena, Italy; 6Division of Experimental Oncology, Centro di Riferimento Oncologico, Aviano, Italy; and 7S.S.D. Diagnostica Malattie Linfoproliferative, Istituto Nazionale per la Ricerca sul Cancro, Genova, Italy Abstract Introduction Angioimmunoblastic lymphoma (AILT) is the second most Peripheral T-cell lymphomas (PTCL) represent 10% to 15% of common subtype of peripheral T-cell lymphoma (PTCL) and all lymphoid neoplasms (1). They are a heterogeneous group of is characterized by dismal prognosis. Thus far, only a fewstu- tumors that in the Revised European-American Lymphoma dies have dealt with its molecular pathogenesis. We performed (REAL)/WHO classification are subdivided into