Supplementary Table 1
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Original Article Upregulation of HOXA13 As a Potential Tumorigenesis and Progression Promoter of LUSC Based on Qrt-PCR and Bioinformatics
Int J Clin Exp Pathol 2017;10(10):10650-10665 www.ijcep.com /ISSN:1936-2625/IJCEP0065149 Original Article Upregulation of HOXA13 as a potential tumorigenesis and progression promoter of LUSC based on qRT-PCR and bioinformatics Rui Zhang1*, Yun Deng1*, Yu Zhang1, Gao-Qiang Zhai1, Rong-Quan He2, Xiao-Hua Hu2, Dan-Ming Wei1, Zhen-Bo Feng1, Gang Chen1 Departments of 1Pathology, 2Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China. *Equal contributors. Received September 7, 2017; Accepted September 29, 2017; Epub October 1, 2017; Published October 15, 2017 Abstract: In this study, we investigated the levels of homeobox A13 (HOXA13) and the mechanisms underlying the co-expressed genes of HOXA13 in lung squamous cancer (LUSC), the signaling pathways in which the co-ex- pressed genes of HOXA13 are involved and their functional roles in LUSC. The clinical significance of 23 paired LUSC tissues and adjacent non-tumor tissues were gathered. HOXA13 levels in LUSC were detected by quantita- tive real-time polymerase chain reaction (qRT-PCR). HOXA13 levels in LUSC from The Cancer Genome Atlas (TCGA) and Oncomine were analyzed. We performed receiver operator characteristic (ROC) curves of various clinicopath- ological features of LUSC. Co-expressed of HOXA13 were collected from MEM, cBioPortal and GEPIA. The func- tions and pathways of the most reliable overlapped genes were achieved from the Gene Otology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. The protein-protein interaction (PPI) net- works were mapped using STRING. HOXA13 in LUSC were markedly upregulated compared with those in the non- cancerous controls as demonstrated by qRT-PCR (LUSC: 0.330±0.360; CONTROLS: 0.155±0.142; P=0.021). -
Small Cell Ovarian Carcinoma: Genomic Stability and Responsiveness to Therapeutics
Gamwell et al. Orphanet Journal of Rare Diseases 2013, 8:33 http://www.ojrd.com/content/8/1/33 RESEARCH Open Access Small cell ovarian carcinoma: genomic stability and responsiveness to therapeutics Lisa F Gamwell1,2, Karen Gambaro3, Maria Merziotis2, Colleen Crane2, Suzanna L Arcand4, Valerie Bourada1,2, Christopher Davis2, Jeremy A Squire6, David G Huntsman7,8, Patricia N Tonin3,4,5 and Barbara C Vanderhyden1,2* Abstract Background: The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Method: The tumourigenic potential of BIN-67 cells was determined and the tumours formed in a xenograft model was compared to human SCCOHT. DNA sequencing, spectral karyotyping and high density SNP array analysis was performed. The sensitivity of the BIN-67 cells to standard chemotherapeutic agents and to vesicular stomatitis virus (VSV) and the JX-594 vaccinia virus was tested. Results: BIN-67 cells were capable of forming spheroids in hanging drop cultures. When xenografted into immunodeficient mice, BIN-67 cells developed into tumours that reflected the hypercalcemia and histology of human SCCOHT, notably intense expression of WT-1 and vimentin, and lack of expression of inhibin. Somatic mutations in TP53 and the most common activating mutations in KRAS and BRAF were not found in BIN-67 cells by DNA sequencing. -
Human ADAM12 Quantikine ELISA
Quantikine® ELISA Human ADAM12 Immunoassay Catalog Number DAD120 For the quantitative determination of A Disintegrin And Metalloproteinase domain- containing protein 12 (ADAM12) concentrations in cell culture supernates, serum, plasma, and urine. This package insert must be read in its entirety before using this product. For research use only. Not for use in diagnostic procedures. TABLE OF CONTENTS SECTION PAGE INTRODUCTION .....................................................................................................................................................................1 PRINCIPLE OF THE ASSAY ...................................................................................................................................................2 LIMITATIONS OF THE PROCEDURE .................................................................................................................................2 TECHNICAL HINTS .................................................................................................................................................................2 MATERIALS PROVIDED & STORAGE CONDITIONS ...................................................................................................3 OTHER SUPPLIES REQUIRED .............................................................................................................................................3 PRECAUTIONS .........................................................................................................................................................................4 -
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. -
Β-Catenin Confers Resistance to PI3K and AKT Inhibitors and Subverts Foxo3a to Promote Metastasis in Colon Cancer
β-catenin Confers Resistance to PI3K and AKT inhibitors and Subverts FOXO3a to Promote Metastasis in Colon Cancer Stephan P. Tenbaum1§, Paloma Ordóñez-Morán2§#, Isabel Puig1§, Irene Chicote1, Oriol Arqués1, Stefania Landolfi3, Yolanda Fernández4, José Raúl Herance5, Juan D. Gispert5, Leire Mendizabal6, Susana Aguilar7, Santiago Ramón y Cajal3, Simó Schwartz Jr4, Ana Vivancos6, Eloy Espín8, Santiago Rojas5, José Baselga9, Josep Tabernero10, Alberto Muñoz2, Héctor G. Palmer1* 1 Vall d’Hebrón Institut d´Oncología (VHIO). Stem Cells and Cancer Laboratory. Barcelona, Spain. 2 Instituto de Investigaciones Biomédicas "Alberto Sols", Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid, Madrid, Spain. 3 Department of Pathology, Hospital Universitari Vall d'Hebrón, Universitat Autònoma de Barcelona, Barcelona, Spain. 4 Group of Drug Delivery and Targeting, CIBBIM-Nanomedicine and Networking Biomedical Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Hospital Universitari Vall d’Hebrón, Institut de Recerca Vall d’Hebrón, Universitat Autònoma de Barcelona, Barcelona, Spain. 5 Parc de Recerca Biomèdica de Barcelona (PRBB), Centre d´Imatge Molecular (CRC) Corporació Sanitària, Barcelona, Spain. 6 Vall d’Hebrón Institut d´Oncología (VHIO). Genomics Cancer Group. Barcelona, Spain. 7 Centre for Respiratory Research, Rayne Institute, University College London, London, United Kingdom, Hematopoietic Stem Cell Laboratory, London Research Institute, Cancer Research UK, London, United Kingdom. 8 General Surgery Service, Hospital Universitari Vall d'Hebrón, Barcelona, Spain. 9 Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown, USA; Howard Hughes Medical Institute, Chevy Chase, USA. 10 Medical Oncology Department, Hospital Universitari Vall d'Hebrón, Barcelona, Spain. # Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. -
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. -
Circular RNA Hsa Circ 0005114‑Mir‑142‑3P/Mir‑590‑5P‑ Adenomatous
ONCOLOGY LETTERS 21: 58, 2021 Circular RNA hsa_circ_0005114‑miR‑142‑3p/miR‑590‑5p‑ adenomatous polyposis coli protein axis as a potential target for treatment of glioma BO WEI1*, LE WANG2* and JINGWEI ZHAO1 1Department of Neurosurgery, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033; 2Department of Ophthalmology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin 130021, P.R. China Received September 12, 2019; Accepted October 22, 2020 DOI: 10.3892/ol.2020.12320 Abstract. Glioma is the most common type of brain tumor APC expression with a good overall survival rate. UALCAN and is associated with a high mortality rate. Despite recent analysis using TCGA data of glioblastoma multiforme and the advances in treatment options, the overall prognosis in patients GSE25632 and GSE103229 microarray datasets showed that with glioma remains poor. Studies have suggested that circular hsa‑miR‑142‑3p/hsa‑miR‑590‑5p was upregulated and APC (circ)RNAs serve important roles in the development and was downregulated. Thus, hsa‑miR‑142‑3p/hsa‑miR‑590‑5p‑ progression of glioma and may have potential as therapeutic APC‑related circ/ceRNA axes may be important in glioma, targets. However, the expression profiles of circRNAs and their and hsa_circ_0005114 interacted with both of these miRNAs. functions in glioma have rarely been studied. The present study Functional analysis showed that hsa_circ_0005114 was aimed to screen differentially expressed circRNAs (DECs) involved in insulin secretion, while APC was associated with between glioma and normal brain tissues using sequencing the Wnt signaling pathway. In conclusion, hsa_circ_0005114‑ data collected from the Gene Expression Omnibus database miR‑142‑3p/miR‑590‑5p‑APC ceRNA axes may be potential (GSE86202 and GSE92322 datasets) and explain their mecha‑ targets for the treatment of glioma. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Discovery of an Alternate Metabolic Pathway for Urea Synthesis in Adult Aedes Aegypti Mosquitoes
Discovery of an alternate metabolic pathway for urea synthesis in adult Aedes aegypti mosquitoes Patricia Y. Scaraffia*†‡, Guanhong Tan§, Jun Isoe*†, Vicki H. Wysocki*§, Michael A. Wells*†, and Roger L. Miesfeld*† Departments of §Chemistry and *Biochemistry and Molecular Biophysics and †Center for Insect Science, University of Arizona, Tucson, AZ 85721-0088 Edited by Anthony A. James, University of California, Irvine, CA, and approved December 4, 2007 (received for review August 27, 2007) We demonstrate the presence of an alternate metabolic pathway We previously reported that mosquitoes dispose of toxic for urea synthesis in Aedes aegypti mosquitoes that converts uric ammonia through glutamine (Gln) and proline (Pro) synthesis, acid to urea via an amphibian-like uricolytic pathway. For these along with excretion of ammonia, uric acid, and urea (20). By studies, female mosquitoes were fed a sucrose solution containing using labeled isotopes and mass spectrometry techniques (21), 15 15 15 15 15 NH4Cl, [5- N]-glutamine, [ N]-proline, allantoin, or allantoic we have recently determined how the N from NH4Cl is acid. At 24 h after feeding, the feces were collected and analyzed incorporated into the amide side chain of Gln, and then into Pro, in a mass spectrometer. Specific enzyme inhibitors confirmed that in Ae. aegypti (22). In the present article we demonstrate that the 15 15 15 mosquitoes incorporate N from NH4Cl into [5- N]-glutamine nitrogen of the amide group of Gln contributes to uric acid and use the 15N of the amide group of glutamine to produce synthesis in mosquitoes and, surprisingly, that uric acid can be 15 labeled uric acid. -
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, -
Transcriptome Sequencing and Genome-Wide Association Analyses Reveal Lysosomal Function and Actin Cytoskeleton Remodeling in Schizophrenia and Bipolar Disorder
Molecular Psychiatry (2015) 20, 563–572 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp ORIGINAL ARTICLE Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder Z Zhao1,6,JXu2,6, J Chen3,6, S Kim4, M Reimers3, S-A Bacanu3,HYu1, C Liu5, J Sun1, Q Wang1, P Jia1,FXu2, Y Zhang2, KS Kendler3, Z Peng2 and X Chen3 Schizophrenia (SCZ) and bipolar disorder (BPD) are severe mental disorders with high heritability. Clinicians have long noticed the similarities of clinic symptoms between these disorders. In recent years, accumulating evidence indicates some shared genetic liabilities. However, what is shared remains elusive. In this study, we conducted whole transcriptome analysis of post-mortem brain tissues (cingulate cortex) from SCZ, BPD and control subjects, and identified differentially expressed genes in these disorders. We found 105 and 153 genes differentially expressed in SCZ and BPD, respectively. By comparing the t-test scores, we found that many of the genes differentially expressed in SCZ and BPD are concordant in their expression level (q ⩽ 0.01, 53 genes; q ⩽ 0.05, 213 genes; q ⩽ 0.1, 885 genes). Using genome-wide association data from the Psychiatric Genomics Consortium, we found that these differentially and concordantly expressed genes were enriched in association signals for both SCZ (Po10 − 7) and BPD (P = 0.029). To our knowledge, this is the first time that a substantially large number of genes show concordant expression and association for both SCZ and BPD. Pathway analyses of these genes indicated that they are involved in the lysosome, Fc gamma receptor-mediated phagocytosis, regulation of actin cytoskeleton pathways, along with several cancer pathways. -
Genome-Wide Association and Transcriptome Studies Identify Candidate Genes and Pathways for Feed Conversion Ratio in Pigs
Miao et al. BMC Genomics (2021) 22:294 https://doi.org/10.1186/s12864-021-07570-w RESEARCH ARTICLE Open Access Genome-wide association and transcriptome studies identify candidate genes and pathways for feed conversion ratio in pigs Yuanxin Miao1,2,3, Quanshun Mei1,2, Chuanke Fu1,2, Mingxing Liao1,2,4, Yan Liu1,2, Xuewen Xu1,2, Xinyun Li1,2, Shuhong Zhao1,2 and Tao Xiang1,2* Abstract Background: The feed conversion ratio (FCR) is an important productive trait that greatly affects profits in the pig industry. Elucidating the genetic mechanisms underpinning FCR may promote more efficient improvement of FCR through artificial selection. In this study, we integrated a genome-wide association study (GWAS) with transcriptome analyses of different tissues in Yorkshire pigs (YY) with the aim of identifying key genes and signalling pathways associated with FCR. Results: A total of 61 significant single nucleotide polymorphisms (SNPs) were detected by GWAS in YY. All of these SNPs were located on porcine chromosome (SSC) 5, and the covered region was considered a quantitative trait locus (QTL) region for FCR. Some genes distributed around these significant SNPs were considered as candidates for regulating FCR, including TPH2, FAR2, IRAK3, YARS2, GRIP1, FRS2, CNOT2 and TRHDE. According to transcriptome analyses in the hypothalamus, TPH2 exhibits the potential to regulate intestinal motility through serotonergic synapse and oxytocin signalling pathways. In addition, GRIP1 may be involved in glutamatergic and GABAergic signalling pathways, which regulate FCR by affecting appetite in pigs. Moreover, GRIP1, FRS2, CNOT2,andTRHDE may regulate metabolism in various tissues through a thyroid hormone signalling pathway.