SUPPLEMENTARY MATERIALS the Inflammatory Cytokine IL-3
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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. -
United States Patent 19 11 Patent Number: 5,780,253 Subramanian Et Al
III USOO5780253A United States Patent 19 11 Patent Number: 5,780,253 Subramanian et al. (45) Date of Patent: Jul. 14, 1998 54 SCREENING METHOD FOR DETECTION OF 4.433.999 2/1984 Hyzak ....................................... 71.03 HERBCDES 4.6–552 2/1987 Anoti et al. if O3. 4,802,912 2/1989 Baker ........................................ 7/103 Inventors: Wenkiteswaran Subramanian Danville: Anne G. Toschi. Burlingame. OTHERTHER PPUBLICATION CATIONS both of Calif. Heim et al. Pesticide Biochem & Physiol; vol. 53, pp. 138-145 (1995). 73) Assignee: Sandoz Ltd., Basel. Switzerland Hatch. MD.: Phytochem. vol. 6... pp. 115 to 119, (1967). Haworth et al. J. Agric. Food Chem, vol. 38, pp. 1271-1273. 21 Appl. No.:752.990 1990. Nishimura et al: Phytochem: vol. 34, pp. 613-615. (1993). 22 Filed: Nov. 21, 1996 Primary Examiner-Louise N. Leary Related U.S. Application Data Attorney, Agent, or Firm-Lynn Marcus-Wyner: Michael P. Morris 63 Continuation of Ser. No. 434.826, May 4, 1995, abandoned. 6 57 ABSTRACT 51 Int. Cl. ............................... C12Q 1/48: C12Q 1/32: C12Q 1/37; C12O 1/00 This invention relates to novel screening methods for iden 52 U.S. Cl. ................................. 435/15:435/18: 435/26: tifying compounds that specifically inhibit a biosynthetic 435/23: 435/4, 536/23.6:536/23.2:536/24.3 pathway in plants. Enzymes which are specifically affected 536/26.11:536/26.12:536/26.13 by the novel screening method include plant purine biosyn 58 Field of Search .................................. 435/15, 8, 26, thetic pathway enzymes and particularly the enzymes 435/23 4: 536/23.6, 23.2, 24.3, 26.1, involved in the conversion of inosine monophosphate to 26.12, 26.13 adenosine monophosphate and inosine monophosphate to guanosine monophosphate. -
View of HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-Negative Breast Resistance to Systemic Therapy in Patients with Breast Cancer
Wen et al. Cancer Cell Int (2018) 18:128 https://doi.org/10.1186/s12935-018-0625-9 Cancer Cell International PRIMARY RESEARCH Open Access Sulbactam‑enhanced cytotoxicity of doxorubicin in breast cancer cells Shao‑hsuan Wen1†, Shey‑chiang Su2†, Bo‑huang Liou3, Cheng‑hao Lin1 and Kuan‑rong Lee1* Abstract Background: Multidrug resistance (MDR) is a major obstacle in breast cancer treatment. The predominant mecha‑ nism underlying MDR is an increase in the activity of adenosine triphosphate (ATP)-dependent drug efux trans‑ porters. Sulbactam, a β-lactamase inhibitor, is generally combined with β-lactam antibiotics for treating bacterial infections. However, sulbactam alone can be used to treat Acinetobacter baumannii infections because it inhibits the expression of ATP-binding cassette (ABC) transporter proteins. This is the frst study to report the efects of sulbactam on mammalian cells. Methods: We used the breast cancer cell lines as a model system to determine whether sulbactam afects cancer cells. The cell viabilities in the present of doxorubicin with or without sulbactam were measured by MTT assay. Protein identities and the changes in protein expression levels in the cells after sulbactam and doxorubicin treatment were determined using LC–MS/MS. Real-time reverse transcription polymerase chain reaction (real-time RT-PCR) was used to analyze the change in mRNA expression levels of ABC transporters after treatment of doxorubicin with or without sulbactam. The efux of doxorubicin was measures by the doxorubicin efux assay. Results: MTT assay revealed that sulbactam enhanced the cytotoxicity of doxorubicin in breast cancer cells. The results of proteomics showed that ABC transporter proteins and proteins associated with the process of transcription and initiation of translation were reduced. -
Characterization of FLT3-Itdmut Acute Myeloid Leukemia
Travaglini et al. Blood Cancer Journal (2020) 10:85 https://doi.org/10.1038/s41408-020-00352-9 Blood Cancer Journal ARTICLE Open Access Characterization of FLT3-ITDmut acute myeloid leukemia: molecular profiling of leukemic precursor cells Serena Travaglini1, Daniela Francesca Angelini 2, Valentina Alfonso1, Gisella Guerrera2,SerenaLavorgna1, Mariadomenica Divona1, Anna Maria Nardozza1, Maria Irno Consalvo1, Emiliano Fabiani1,MarcoDeBardi 2, Benedetta Neri3,FabioForghieri4, Francesco Marchesi5, Giovangiacinto Paterno1, Raffaella Cerretti1, Eva Barragan 6, Valentina Fiori7,SabrinaDominici7, Maria Ilaria Del Principe1, Adriano Venditti 1, Luca Battistini2, William Arcese1, Francesco Lo-Coco1, Maria Teresa Voso 1,2 and Tiziana Ottone1,2 Abstract Acute myeloid leukemia (AML) with FLT3-ITD mutations (FLT3-ITDmut) remains a therapeutic challenge, with a still high relapse rate, despite targeted treatment with tyrosine kinase inhibitors. In this disease, the CD34/CD123/CD25/CD99+ leukemic precursor cells (LPCs) phenotype predicts for FLT3-ITD-positivity. The aim of this study was to characterize the distribution of FLT3-ITD mutation in different progenitor cell subsets to shed light on the subclonal architecture of FLT3-ITDmut AML. Using high-speed cell sorting, we sequentially purified LPCs and CD34+ progenitors in samples from patients with FLT3-ITDmut AML (n = 12). A higher FLT3-ITDmut load was observed within CD34/CD123/CD25/CD99+ LPCs, as compared to CD34+ progenitors (CD123+/−,CD25−,CD99low/−)(p = 0.0005) and mononuclear cells (MNCs) (p < 0.0001). This was associated with significantly increased CD99 mean fluorescence intensity in LPCs. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Significantly higher FLT3-ITDmut burden was also observed in LPCs of AML patients with a small FLT3-ITDmut clones at diagnosis. -
List of Genes Used in Cell Type Enrichment Analysis
List of genes used in cell type enrichment analysis Metagene Cell type Immunity ADAM28 Activated B cell Adaptive CD180 Activated B cell Adaptive CD79B Activated B cell Adaptive BLK Activated B cell Adaptive CD19 Activated B cell Adaptive MS4A1 Activated B cell Adaptive TNFRSF17 Activated B cell Adaptive IGHM Activated B cell Adaptive GNG7 Activated B cell Adaptive MICAL3 Activated B cell Adaptive SPIB Activated B cell Adaptive HLA-DOB Activated B cell Adaptive IGKC Activated B cell Adaptive PNOC Activated B cell Adaptive FCRL2 Activated B cell Adaptive BACH2 Activated B cell Adaptive CR2 Activated B cell Adaptive TCL1A Activated B cell Adaptive AKNA Activated B cell Adaptive ARHGAP25 Activated B cell Adaptive CCL21 Activated B cell Adaptive CD27 Activated B cell Adaptive CD38 Activated B cell Adaptive CLEC17A Activated B cell Adaptive CLEC9A Activated B cell Adaptive CLECL1 Activated B cell Adaptive AIM2 Activated CD4 T cell Adaptive BIRC3 Activated CD4 T cell Adaptive BRIP1 Activated CD4 T cell Adaptive CCL20 Activated CD4 T cell Adaptive CCL4 Activated CD4 T cell Adaptive CCL5 Activated CD4 T cell Adaptive CCNB1 Activated CD4 T cell Adaptive CCR7 Activated CD4 T cell Adaptive DUSP2 Activated CD4 T cell Adaptive ESCO2 Activated CD4 T cell Adaptive ETS1 Activated CD4 T cell Adaptive EXO1 Activated CD4 T cell Adaptive EXOC6 Activated CD4 T cell Adaptive IARS Activated CD4 T cell Adaptive ITK Activated CD4 T cell Adaptive KIF11 Activated CD4 T cell Adaptive KNTC1 Activated CD4 T cell Adaptive NUF2 Activated CD4 T cell Adaptive PRC1 Activated -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Transcriptomic Analysis of the Aquaporin (AQP) Gene Family
Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples. -
35 Disorders of Purine and Pyrimidine Metabolism
35 Disorders of Purine and Pyrimidine Metabolism Georges van den Berghe, M.- Françoise Vincent, Sandrine Marie 35.1 Inborn Errors of Purine Metabolism – 435 35.1.1 Phosphoribosyl Pyrophosphate Synthetase Superactivity – 435 35.1.2 Adenylosuccinase Deficiency – 436 35.1.3 AICA-Ribosiduria – 437 35.1.4 Muscle AMP Deaminase Deficiency – 437 35.1.5 Adenosine Deaminase Deficiency – 438 35.1.6 Adenosine Deaminase Superactivity – 439 35.1.7 Purine Nucleoside Phosphorylase Deficiency – 440 35.1.8 Xanthine Oxidase Deficiency – 440 35.1.9 Hypoxanthine-Guanine Phosphoribosyltransferase Deficiency – 441 35.1.10 Adenine Phosphoribosyltransferase Deficiency – 442 35.1.11 Deoxyguanosine Kinase Deficiency – 442 35.2 Inborn Errors of Pyrimidine Metabolism – 445 35.2.1 UMP Synthase Deficiency (Hereditary Orotic Aciduria) – 445 35.2.2 Dihydropyrimidine Dehydrogenase Deficiency – 445 35.2.3 Dihydropyrimidinase Deficiency – 446 35.2.4 Ureidopropionase Deficiency – 446 35.2.5 Pyrimidine 5’-Nucleotidase Deficiency – 446 35.2.6 Cytosolic 5’-Nucleotidase Superactivity – 447 35.2.7 Thymidine Phosphorylase Deficiency – 447 35.2.8 Thymidine Kinase Deficiency – 447 References – 447 434 Chapter 35 · Disorders of Purine and Pyrimidine Metabolism Purine Metabolism Purine nucleotides are essential cellular constituents 4 The catabolic pathway starts from GMP, IMP and which intervene in energy transfer, metabolic regula- AMP, and produces uric acid, a poorly soluble tion, and synthesis of DNA and RNA. Purine metabo- compound, which tends to crystallize once its lism can be divided into three pathways: plasma concentration surpasses 6.5–7 mg/dl (0.38– 4 The biosynthetic pathway, often termed de novo, 0.47 mmol/l). starts with the formation of phosphoribosyl pyro- 4 The salvage pathway utilizes the purine bases, gua- phosphate (PRPP) and leads to the synthesis of nine, hypoxanthine and adenine, which are pro- inosine monophosphate (IMP). -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
Genomic Aberrations Associated with Erlotinib Resistance in Non-Small Cell Lung Cancer Cells
ANTICANCER RESEARCH 33: 5223-5234 (2013) Genomic Aberrations Associated with Erlotinib Resistance in Non-small Cell Lung Cancer Cells MASAKUNI SERIZAWA1, TOSHIAKI TAKAHASHI2, NOBUYUKI YAMAMOTO2,3 and YASUHIRO KOH1 1Drug Discovery and Development Division, Shizuoka Cancer Center Research Institute, Sunto-gun, Shizuoka, Japan; 2Division of Thoracic Oncology, Shizuoka Cancer Center Hospital, Sunto-gun, Shizuoka, Japan; 3Third Department of Internal Medicine, Wakayama Medical University, Kimiidera, Wakayama, Japan Abstract. Background/Aim: Mechanisms of resistance to mutations develop resistance, usually within one year of epidermal growth factor receptor (EGFR)-tyrosine kinase treatment. Therefore, there is an urgent need to elucidate the inhibitors (TKIs) in non-small cell lung cancer (NSCLC) underlying mechanisms of resistance in such tumors to are not fully-understood. In this study we aimed to overcome this obstacle (11-14, 17, 24). Recent studies elucidate remaining unknown mechanisms using erlotinib- suggest that mechanisms of acquired resistance to EGFR- resistant NSCLC cells. Materials and Methods: We TKIs can be categorized into three groups: occurrence of performed array comparative genomic hybridization genetic alterations, activation of downstream pathways via (aCGH) to identify genomic aberrations associated with bypass signaling, and phenotypic transformation (15, 16, 21); EGFR-TKI resistance in erlotinib-resistant PC-9ER cells. therapeutic strategies to overcome these resistance Real-time polymerase chain reaction (PCR) and mechanisms are under development. However, although the immunoblot analyses were performed to confirm the results causes of acquired resistance to EGFR-TKIs have been of aCGH. Results: Among the five regions with copy investigated, in more than 30% of patients with acquired number gain detected in PC-9ER cells, we focused on resistance to EGFR-TKI treatment, the mechanisms remain 22q11.2-q12.1 including v-crk avian sarcoma virus CT10 unknown (15). -
AP1B1 Rabbit Polyclonal Antibody – TA323221 | Origene
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TA323221 AP1B1 Rabbit Polyclonal Antibody Product data: Product Type: Primary Antibodies Applications: IHC Recommended Dilution: ELISA: 1:1000-5000, IHC: 1:25-100 Reactivity: Human, Mouse, Rat Host: Rabbit Isotype: IgG Clonality: Polyclonal Immunogen: Synthetic peptide corresponding to a region derived from 18-30 amino acids of Human Adapter-related protein complex 1 subunit beta-1 Formulation: PBS pH7.3, 0.05% NaN3, 50% glycerol Concentration: lot specific Purification: Antigen affinity purification Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Gene Name: adaptor related protein complex 1 beta 1 subunit Database Link: NP_001118 Entrez Gene 11764 MouseEntrez Gene 29663 RatEntrez Gene 162 Human Q10567 Background: Adaptor protein complex 1 is found at the cytoplasmic face of coated vesicles located at the Golgi complex; where it mediates both the recruitment of clathrin to the membrane and the recognition of sorting signals within the cytosolic tails of transmembrane receptors. This complex is a heterotetramer composed of two large; one medium; and one small adaptin subunit. The protein encoded by this gene serves as one of the large subunits of this complex and is a member of the adaptin protein family. This gene is a candidate meningioma gene. Alternative splicing results in multiple transcript variants. Synonyms: ADTB1; AP105A; BAM22; CLAPB2 Protein Pathways: Lysosome This product is to be used for laboratory only. -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented.