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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Isyte: Integrated Systems Tool for Eye Gene Discovery
Lens iSyTE: Integrated Systems Tool for Eye Gene Discovery Salil A. Lachke,1,2,3,4 Joshua W. K. Ho,1,4,5 Gregory V. Kryukov,1,4,6 Daniel J. O’Connell,1 Anton Aboukhalil,1,7 Martha L. Bulyk,1,8,9 Peter J. Park,1,5,10 and Richard L. Maas1 PURPOSE. To facilitate the identification of genes associated ther investigation. Extension of this approach to other ocular with cataract and other ocular defects, the authors developed tissue components will facilitate eye disease gene discovery. and validated a computational tool termed iSyTE (integrated (Invest Ophthalmol Vis Sci. 2012;53:1617–1627) DOI: Systems Tool for Eye gene discovery; http://bioinformatics. 10.1167/iovs.11-8839 udel.edu/Research/iSyTE). iSyTE uses a mouse embryonic lens gene expression data set as a bioinformatics filter to select candidate genes from human or mouse genomic regions impli- ven with the advent of high-throughput sequencing, the cated in disease and to prioritize them for further mutational Ediscovery of genes associated with congenital birth defects and functional analyses. such as eye defects remains a challenge. We sought to develop METHODS. Microarray gene expression profiles were obtained a straightforward experimental approach that could facilitate for microdissected embryonic mouse lens at three key devel- the identification of candidate genes for developmental disor- opmental time points in the transition from the embryonic day ders, and, as proof-of-principle, we chose defects involving the (E)10.5 stage of lens placode invagination to E12.5 lens primary ocular lens. Opacification of the lens results in cataract, a leading cause of blindness that affects 77 million persons and fiber cell differentiation. -
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. -
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. -
Expression Gene Network Analyses Reveal Molecular Mechanisms And
www.nature.com/scientificreports OPEN Diferential expression and co- expression gene network analyses reveal molecular mechanisms and candidate biomarkers involved in breast muscle myopathies in chicken Eva Pampouille1,2, Christelle Hennequet-Antier1, Christophe Praud1, Amélie Juanchich1, Aurélien Brionne1, Estelle Godet1, Thierry Bordeau1, Fréderic Fagnoul2, Elisabeth Le Bihan-Duval1 & Cécile Berri1* The broiler industry is facing an increasing prevalence of breast myopathies, such as white striping (WS) and wooden breast (WB), and the precise aetiology of these occurrences remains poorly understood. To progress our understanding of the structural changes and molecular pathways involved in these myopathies, a transcriptomic analysis was performed using an 8 × 60 K Agilent chicken microarray and histological study. The study used pectoralis major muscles from three groups: slow-growing animals (n = 8), fast-growing animals visually free from defects (n = 8), or severely afected by both WS and WB (n = 8). In addition, a weighted correlation network analysis was performed to investigate the relationship between modules of co-expressed genes and histological traits. Functional analysis suggested that selection for fast growing and breast meat yield has progressively led to conditions favouring metabolic shifts towards alternative catabolic pathways to produce energy, leading to an adaptive response to oxidative stress and the frst signs of infammatory, regeneration and fbrosis processes. All these processes are intensifed in muscles afected by severe myopathies, in which new mechanisms related to cellular defences and remodelling seem also activated. Furthermore, our study opens new perspectives for myopathy diagnosis by highlighting fne histological phenotypes and genes whose expression was strongly correlated with defects. Te poultry industry relies on the production of fast-growing chickens, which are slaughtered at high weights and intended for cutting and processing. -
Cd8a (Ly-2) Microbeads Mouse
CD8a (Ly-2) MicroBeads mouse Order no. 130-049-401 Contents 1.2 Background information 1. Description Mouse CD8a (Ly-2) MicroBeads were developed for positive + 1.1 Principle of the MACS® Separation selection or depletion of mouse CD8a T cells from single-cell suspensions of lymphoid and non-lymphoid tissues or from 1.2 Background information peripheral blood. The CD8a antigen is expressed on most 1.3 Applications thymocytes, almost all cytotoxic T cells and on subpopulations of dendritic cells. CD8a functions as an accessory molecule in 1.4 Reagent and instrument requirements the recognition of MHC class I/peptide complexes by the TCR 2. Protocol heterodimer on cytotoxic CD8a+ T cells. 2.1 Sample preparation 1.3 Applications 2.2 Magnetic labeling + 2.3 Magnetic separation ● Positive selection or depletion of CD8a T cells from lymphoid organs, non-lymphoid tissue, peripheral blood, or in vitro 2.4 Cell separation with the autoMACS® Pro Separator cultured cells. 3. Example of a separation using the CD8a (Ly-2) MicroBeads + ● Isolation of purified CD8 cells for in vitro and in vivo studies 1,2 4. References on protective immune responses against parasites or allergens3, and for adoptive transfer into immunodeficient4,5 and virus infected mice6. + Warnings ● Isolation of highly pure CD8 T cells from CNS of MHV infected 7 Reagents contain sodium azide. Under acidic conditions sodium mice for evaluation of their chemokine expression pattern. azide yields hydrazoic acid, which is extremely toxic. Azide compounds should be diluted with running water before discarding. 1.4 Reagent and instrument requirements These precautions are recommended to avoid deposits in plumbing ● Buffer: Prepare a solution containing phosphate-buffered where explosive conditions may develop. -
Propranolol-Mediated Attenuation of MMP-9 Excretion in Infants with Hemangiomas
Supplementary Online Content Thaivalappil S, Bauman N, Saieg A, Movius E, Brown KJ, Preciado D. Propranolol-mediated attenuation of MMP-9 excretion in infants with hemangiomas. JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2013.4773 eTable. List of All of the Proteins Identified by Proteomics This supplementary material has been provided by the authors to give readers additional information about their work. © 2013 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable. List of All of the Proteins Identified by Proteomics Protein Name Prop 12 mo/4 Pred 12 mo/4 Δ Prop to Pred mo mo Myeloperoxidase OS=Homo sapiens GN=MPO 26.00 143.00 ‐117.00 Lactotransferrin OS=Homo sapiens GN=LTF 114.00 205.50 ‐91.50 Matrix metalloproteinase‐9 OS=Homo sapiens GN=MMP9 5.00 36.00 ‐31.00 Neutrophil elastase OS=Homo sapiens GN=ELANE 24.00 48.00 ‐24.00 Bleomycin hydrolase OS=Homo sapiens GN=BLMH 3.00 25.00 ‐22.00 CAP7_HUMAN Azurocidin OS=Homo sapiens GN=AZU1 PE=1 SV=3 4.00 26.00 ‐22.00 S10A8_HUMAN Protein S100‐A8 OS=Homo sapiens GN=S100A8 PE=1 14.67 30.50 ‐15.83 SV=1 IL1F9_HUMAN Interleukin‐1 family member 9 OS=Homo sapiens 1.00 15.00 ‐14.00 GN=IL1F9 PE=1 SV=1 MUC5B_HUMAN Mucin‐5B OS=Homo sapiens GN=MUC5B PE=1 SV=3 2.00 14.00 ‐12.00 MUC4_HUMAN Mucin‐4 OS=Homo sapiens GN=MUC4 PE=1 SV=3 1.00 12.00 ‐11.00 HRG_HUMAN Histidine‐rich glycoprotein OS=Homo sapiens GN=HRG 1.00 12.00 ‐11.00 PE=1 SV=1 TKT_HUMAN Transketolase OS=Homo sapiens GN=TKT PE=1 SV=3 17.00 28.00 ‐11.00 CATG_HUMAN Cathepsin G OS=Homo -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
Product Data Sheet
Product Data Sheet ExProfileTM Human AMPK Signaling Related Gene qPCR Array For focused group profiling of human AMPK signaling genes expression Cat. No. QG004-A (4 x 96-well plate, Format A) Cat. No. QG004-B (4 x 96-well plate, Format B) Cat. No. QG004-C (4 x 96-well plate, Format C) Cat. No. QG004-D (4 x 96-well plate, Format D) Cat. No. QG004-E (4 x 96-well plate, Format E) Plates available individually or as a set of 6. Each set contains 336 unique gene primer pairs deposited in one 96-well plate. Introduction The ExProfile human AMPK signaling related gene qPCR array profiles the expression of 336 human genes related to AMPK-mediated signal transduction. These genes are carefully chosen for their close pathway correlation based on a thorough literature search of peer-reviewed publications, mainly including genes that encode AMP-activated protein kinase complex,its regulators and targets involved in many important biological processes, such as glucose uptake, β-oxidation of fatty acids and modulation of insulin secretion. This array allows researchers to study the pathway-related genes to gain understanding of their roles in the different biological processes. QG004 plate 01: 84 unique gene PCR primer pairs QG004 plate 02: 84 unique gene PCR primer pairs QG004 plate 03: 84 unique gene PCR primer pairs QG004 plate 04: 84 unique gene PCR primer pairs Shipping and storage condition Shipped at room temperate Stable for at least 6 months when stored at -20°C Array format GeneCopoeia provides five qPCR array formats (A, B, C, D, and E) suitable for use with the following real- time cyclers. -
Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers
Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers. by Kelly Kennaley A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Molecular and Cellular Pathology) in the University of Michigan 2019 Doctoral Committee: Associate Professor Zaneta Nikolovska-Coleska, Co-Chair Adjunct Associate Professor Scott A. Tomlins, Co-Chair Associate Professor Eric R. Fearon Associate Professor Alexey I. Nesvizhskii Kelly R. Kennaley [email protected] ORCID iD: 0000-0003-2439-9020 © Kelly R. Kennaley 2019 Acknowledgements I have immeasurable gratitude for the unwavering support and guidance I received throughout my dissertation. First and foremost, I would like to thank my thesis advisor and mentor Dr. Scott Tomlins for entrusting me with a challenging, interesting, and impactful project. He taught me how to drive a project forward through set-backs, ask the important questions, and always consider the impact of my work. I’m truly appreciative for his commitment to ensuring that I would get the most from my graduate education. I am also grateful to the many members of the Tomlins lab that made it the supportive, collaborative, and educational environment that it was. I would like to give special thanks to those I’ve worked closely with on this project, particularly Dr. Moloy Goswami for his mentorship, Lei Lucy Wang, Dr. Sumin Han, and undergraduate students Bhavneet Singh, Travis Weiss, and Myles Barlow. I am also grateful for the support of my thesis committee, Dr. Eric Fearon, Dr. Alexey Nesvizhskii, and my co-mentor Dr. Zaneta Nikolovska-Coleska, who have offered guidance and critical evaluation since project inception. -
Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics
GBE Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics Marisa C.W. Lim1,*, Christopher C. Witt2, Catherine H. Graham1,3,andLilianaM.Davalos 1,4 1Department of Ecology and Evolution, Stony Brook University 2 Museum of Southwestern Biology and Department of Biology, University of New Mexico Downloaded from https://academic.oup.com/gbe/article-abstract/11/6/1552/5494706 by guest on 08 June 2019 3Swiss Federal Research Institute (WSL), Birmensdorf, Switzerland 4Consortium for Inter-Disciplinary Environmental Research, Stony Brook University *Corresponding author: E-mail: [email protected]. Accepted: May 12, 2019 Data deposition: The raw read data have been deposited in the NCBI Sequence Read Archive under BioProject: PRJNA543673, BioSample: SAMN11774663-SAMN11774674, SRA Study: SRP198856. All scripts used for analyses are available on Dryad: doi:10.5061/dryad.v961mb4. Abstract High-elevation organisms experience shared environmental challenges that include low oxygen availability, cold temperatures, and intense ultraviolet radiation. Consequently, repeated evolution of the same genetic mechanisms may occur across high-elevation taxa. To test this prediction, we investigated the extent to which the same biochemical pathways, genes, or sites were subject to parallel molecular evolution for 12 Andean hummingbird species (family: Trochilidae) representing several independent transitions to high elevation across the phylogeny. Across high-elevation species, we discovered parallel evolution for several pathways and genes with evidence of positive selection. In particular, positively selected genes were frequently part of cellular respiration, metabolism, or cell death pathways. To further examine the role of elevation in our analyses, we compared results for low- and high-elevation species and tested different thresholds for defining elevation categories. -
Tepzz¥ 6Z54za T
(19) TZZ¥ ZZ_T (11) EP 3 260 540 A1 (12) EUROPEAN PATENT APPLICATION (43) Date of publication: (51) Int Cl.: 27.12.2017 Bulletin 2017/52 C12N 15/113 (2010.01) A61K 9/127 (2006.01) A61K 31/713 (2006.01) C12Q 1/68 (2006.01) (21) Application number: 17000579.7 (22) Date of filing: 12.11.2011 (84) Designated Contracting States: • Sarma, Kavitha AL AT BE BG CH CY CZ DE DK EE ES FI FR GB Philadelphia, PA 19146 (US) GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO • Borowsky, Mark PL PT RO RS SE SI SK SM TR Needham, MA 02494 (US) • Ohsumi, Toshiro Kendrick (30) Priority: 12.11.2010 US 412862 P Cambridge, MA 02141 (US) 20.12.2010 US 201061425174 P 28.07.2011 US 201161512754 P (74) Representative: Clegg, Richard Ian et al Mewburn Ellis LLP (62) Document number(s) of the earlier application(s) in City Tower accordance with Art. 76 EPC: 40 Basinghall Street 11840099.3 / 2 638 163 London EC2V 5DE (GB) (71) Applicant: The General Hospital Corporation Remarks: Boston, MA 02114 (US) •Thecomplete document including Reference Tables and the Sequence Listing can be downloaded from (72) Inventors: the EPO website • Lee, Jeannie T •This application was filed on 05-04-2017 as a Boston, MA 02114 (US) divisional application to the application mentioned • Zhao, Jing under INID code 62. San Diego, CA 92122 (US) •Claims filed after the date of receipt of the divisional application (Rule 68(4) EPC). (54) POLYCOMB-ASSOCIATED NON-CODING RNAS (57) This invention relates to long non-coding RNAs (IncRNAs), libraries of those ncRNAs that bind chromatin modifiers, such as Polycomb Repressive Complex 2, inhibitory nucleic acids and methods and compositions for targeting IncRNAs.