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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Computational Analysis of DNA Methylation and Gene Expression Patterns in Prostate Cancer
Ieva Rauluševičiūtė Computational Analysis of DNA Methylation and Gene Expression Patterns in Prostate Cancer Master’s thesis in Molecular Medicine Trondheim, June 2018 Supervisors: dr. Morten Beck Rye and prof. Finn Drabløs Norwegian University of Science and Technology Faculty of Medicine and Health Sciences Department of Clinical and Molecular Medicine ABSTRACT DNA methylation is an important contributor for prostate cancer development and progression. It has been studied experimentally for years, but, lately, high-throughput technologies are able to produce genome-wide DNA methylation data that can be analyzed using various computational approaches. Thus, this study aims to bioinformatically investigate different DNA methylation and gene expression patterns in prostate cancer. DNA methylation data from three datasets (TCGA, Absher and Kirby) was correlated with gene expression data in order to distinguish different regulation patterns. Classically, increased DNA methylation in promoter regions is being associated with gene expression downregulation. Although, results of the present project demonstrate another robust pattern, where DNA hypermethylation in promoter regions of 1,058 common genes is accompanied by upregulated expression. After analyzing expression and methylation values in the same samples from TCGA dataset, expression overcompensation in a dataset as an explanation for upregulation was excluded. Further reasons behind the pattern were investigated using TCGA DNA methylation data with extended list of probes and includes the presence of methylated positions in CpG islands, distance to transcription start sites and alternative TSSs. As compared with the downregulated genes in the classical pattern, upregulated genes were shown to have more positions in CpG islands and closer to TSSs. Moreover, the presence of alternative TSS in prostate was demonstrated, also disclosing the limitations of methylation detection systems. -
Rule Mining on Microrna Expression Profiles For
Faculty of Engineering and Information Technology University of Technology, Sydney Rule Mining on MicroRNA Expression Profiles for Human Disease Understanding A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Renhua Song July 2016 CERTIFICATE OF AUTHORSHIP/ORIGINALITY I certify that the work in this thesis has not previously been submitted for a degree nor has it been submitted as part of requirements for a degree except as fully acknowledged within the text. I also certify that the thesis has been written by me. Any help that I have received in my research work and the preparation of the thesis itself has been acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. Signature of Candidate i Acknowledgments First, and foremost, I would like to express my gratitude to my chief supervisor, Assoc. Prof. Jinyan Li, and to my co-supervisors Assoc. Prof. Paul Kennedy and Assoc. Prof. Daniel Catchpoole. I am extremely grateful for all the advice and guidance so unselfishly given to me over the last three and half years by these three distinguished academics. This research would not have been possible without their high order supervision, support, assistance and leadership. The wonderful support and assistance provided by many people during this research is very much appreciated by me and my family. I am very grateful for the help that I received from all of these people but there are some special individuals that I must thank by name. A very sincere thank you is definitely owed to my loving husband Jing Liu, not only for his tremendous support during the last three and half years, but also his unfailing and often sorely tested patience. -
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. -
Systems Analysis Implicates WAVE2&Nbsp
JACC: BASIC TO TRANSLATIONAL SCIENCE VOL.5,NO.4,2020 ª 2020 THE AUTHORS. PUBLISHED BY ELSEVIER ON BEHALF OF THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY-NC-ND LICENSE (http://creativecommons.org/licenses/by-nc-nd/4.0/). PRECLINICAL RESEARCH Systems Analysis Implicates WAVE2 Complex in the Pathogenesis of Developmental Left-Sided Obstructive Heart Defects a b b b Jonathan J. Edwards, MD, Andrew D. Rouillard, PHD, Nicolas F. Fernandez, PHD, Zichen Wang, PHD, b c d d Alexander Lachmann, PHD, Sunita S. Shankaran, PHD, Brent W. Bisgrove, PHD, Bradley Demarest, MS, e f g h Nahid Turan, PHD, Deepak Srivastava, MD, Daniel Bernstein, MD, John Deanfield, MD, h i j k Alessandro Giardini, MD, PHD, George Porter, MD, PHD, Richard Kim, MD, Amy E. Roberts, MD, k l m m,n Jane W. Newburger, MD, MPH, Elizabeth Goldmuntz, MD, Martina Brueckner, MD, Richard P. Lifton, MD, PHD, o,p,q r,s t d Christine E. Seidman, MD, Wendy K. Chung, MD, PHD, Martin Tristani-Firouzi, MD, H. Joseph Yost, PHD, b u,v Avi Ma’ayan, PHD, Bruce D. Gelb, MD VISUAL ABSTRACT Edwards, J.J. et al. J Am Coll Cardiol Basic Trans Science. 2020;5(4):376–86. ISSN 2452-302X https://doi.org/10.1016/j.jacbts.2020.01.012 JACC: BASIC TO TRANSLATIONALSCIENCEVOL.5,NO.4,2020 Edwards et al. 377 APRIL 2020:376– 86 WAVE2 Complex in LVOTO HIGHLIGHTS ABBREVIATIONS AND ACRONYMS Combining CHD phenotype–driven gene set enrichment and CRISPR knockdown screening in zebrafish is an effective approach to identifying novel CHD genes. -
(BCLAF1) ELISA Kit Catalogue No.:Abx503645
Datasheet Version: 1.0.0 Revision date: 14 Jan 2021 Human Bcl-2-associated transcription factor 1 (BCLAF1) ELISA Kit Catalogue No.:abx503645 Human Bcl-2-associated transcription factor 1 (BCLAF1) ELISA Kit is an ELISA Kit for the in vitro quantitative measurement of Human Bcl-2-associated transcription factor 1 (BCLAF1) concentrations in tissue homogenates, cell lysates and other biological fluids. Target: Bcl-2-associated transcription factor 1 (BCLAF1) Reactivity: Human Tested Applications: ELISA Recommended dilutions: Optimal dilutions/concentrations should be determined by the end user. Storage: Shipped at 4 °C. Upon receipt, store the kit according to the storage instruction in the kit's manual. Validity: The validity for this kit is 6 months. Stability: The stability of the kit is determined by the rate of activity loss. The loss rate is less than 5% within the expiration date under appropriate storage conditions. To minimize performance fluctuations, operation procedures and lab conditions should be strictly controlled. It is also strongly suggested that the whole assay is performed by the same user throughout. UniProt Primary AC: Q9NYF8 (UniProt, ExPASy) Gene Symbol: ForBCLAF1 Reference Only KEGG: hsa:9774 Test Range: 0.156 ng/ml - 10 ng/ml Standard Form: Lyophilized ELISA Detection: Colorimetric v1.0.0 Abbexa Ltd, Cambridge, UK · Phone: +44 1223 755950 · Fax: +44 1223 755951 1 Abbexa LLC, Houston, TX, USA · Phone: +1 832 327 7413 www.abbexa.com · Email: [email protected] Datasheet Version: 1.0.0 Revision date: 14 Jan 2021 ELISA Data: Quantitative Sample Type: Tissue homogenates, cell lysates and other biological fluids. Note: This product is for research use only. -
RT² Profiler PCR Array (96-Well Format and 384-Well [4 X 96] Format)
RT² Profiler PCR Array (96-Well Format and 384-Well [4 x 96] Format) Human Mitochondria Cat. no. 330231 PAHS-087ZA For pathway expression analysis Format For use with the following real-time cyclers RT² Profiler PCR Array, Applied Biosystems® models 5700, 7000, 7300, 7500, Format A 7700, 7900HT, ViiA™ 7 (96-well block); Bio-Rad® models iCycler®, iQ™5, MyiQ™, MyiQ2; Bio-Rad/MJ Research Chromo4™; Eppendorf® Mastercycler® ep realplex models 2, 2s, 4, 4s; Stratagene® models Mx3005P®, Mx3000P®; Takara TP-800 RT² Profiler PCR Array, Applied Biosystems models 7500 (Fast block), 7900HT (Fast Format C block), StepOnePlus™, ViiA 7 (Fast block) RT² Profiler PCR Array, Bio-Rad CFX96™; Bio-Rad/MJ Research models DNA Format D Engine Opticon®, DNA Engine Opticon 2; Stratagene Mx4000® RT² Profiler PCR Array, Applied Biosystems models 7900HT (384-well block), ViiA 7 Format E (384-well block); Bio-Rad CFX384™ RT² Profiler PCR Array, Roche® LightCycler® 480 (96-well block) Format F RT² Profiler PCR Array, Roche LightCycler 480 (384-well block) Format G RT² Profiler PCR Array, Fluidigm® BioMark™ Format H Sample & Assay Technologies Description The Human Mitochondria RT² Profiler PCR Array profiles the expression of 84 genes involved in the biogenesis and function of the cell's powerhouse organelle. The genes monitored by this array include regulators and mediators of mitochondrial molecular transport of not only the metabolites needed for the electron transport chain and oxidative phosphorylation, but also the ions required for maintaining the mitochondrial membrane polarization and potential important for ATP synthesis. Metabolism and energy production are not the only functions of mitochondria. -
Actin Binding LIM 1 (Ablim1) Negatively Controls Osteoclastogenesis by Regulating Cell Migration and Fusion
Received: 14 September 2017 | Accepted: 16 March 2018 DOI: 10.1002/jcp.26605 ORIGINAL RESEARCH ARTICLE Actin binding LIM 1 (abLIM1) negatively controls osteoclastogenesis by regulating cell migration and fusion Haruna Narahara1,2 | Eiko Sakai1 | Yu Yamaguchi1 | Shun Narahara1 | Mayumi Iwatake1,* | Kuniaki Okamoto1,† | Noriaki Yoshida2 | Takayuki Tsukuba1 1 Department of Dental Pharmacology, Graduate School of Biomedical Sciences, Actin binding LIM 1 (abLIM1) is a cytoskeletal actin-binding protein that has been Nagasaki University, Nagasaki, Japan implicated in interactions between actin filaments and cytoplasmic targets. Previous 2 Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Biomedical biochemical and cytochemical studies have shown that abLIM1 interacts and co- Sciences, Nagasaki University, Nagasaki, Japan localizes with F-actin in the retina and muscle. However, whether abLIM1 regulates Correspondence osteoclast differentiation has not yet been elucidated. In this study, we examined the Takayuki Tsukuba, Department of Dental role of abLIM1 in osteoclast differentiation and function. We found that abLIM1 Pharmacology, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852- expression was upregulated during receptor activator of nuclear factor kappa-B ligand 8588, Japan. (RANKL)-induced osteoclast differentiation, and that a novel transcript of abLIM1 was Email: [email protected] exclusively expressed in osteoclasts. Overexpression of abLIM1 in the murine Funding information monocytic cell line, RAW-D suppressed osteoclast differentiation and decreased JSPS KAKENHI, Grant numbers: 15H05298, 16K15790, 17H04379 expression of several osteoclast-marker genes. By contrast, small interfering RNA-induced knockdown of abLIM1 enhanced the formation of multinucleated osteoclasts and markedly increased the expression of the osteoclast-marker genes. Mechanistically, abLIM1 regulated the localization of tubulin, migration, and fusion in osteoclasts. -
Analysis of the Dynamics of Limb Transcriptomes During Mouse Development Istvan Gyurján1, Bernhard Sonderegger1, Felix Naef1,2 and Denis Duboule1,3*
Gyurján et al. BMC Developmental Biology 2011, 11:47 http://www.biomedcentral.com/1471-213X/11/47 RESEARCH ARTICLE Open Access Analysis of the dynamics of limb transcriptomes during mouse development Istvan Gyurján1, Bernhard Sonderegger1, Felix Naef1,2 and Denis Duboule1,3* Abstract Background: The development of vertebrate limbs has been a traditional system to study fundamental processes at work during ontogenesis, such as the establishment of spatial cellular coordinates, the effect of diffusible morphogenetic molecules or the translation between gene activity and morphogenesis. In addition, limbs are amongst the first targets of malformations in human and they display a huge realm of evolutionary variations within tetrapods, which make them a paradigm to study the regulatory genome. Results: As a reference resource for future biochemical and genetic analyses, we used genome-wide tiling arrays to establish the transcriptomes of mouse limb buds at three different stages, during which major developmental events take place. We compare the three time-points and discuss some aspects of these datasets, for instance related to transcriptome dynamics or to the potential association between active genes and the distribution of intergenic transcriptional activity. Conclusions: These datasets provide a valuable resource, either for research projects involving gene expression and regulation in developing mouse limbs, or as examples of tissue-specific, genome-wide transcriptional activities. Background regulators of the dorso-ventral (DV) patterning are Limb development has fascinated biologists for a cen- Wnt7a, expressed in dorsal ectoderm, and Engrailed,in tury, mostly because of the importance of these struc- ventral ectoderm, as well as Lmxb1,agenetranscribed tures in the evolution of land vertebrates and due to in dorsal mesenchyme. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Mechanism of Completion of Peptidyltransferase Centre
RESEARCH ARTICLE Mechanism of completion of peptidyltransferase centre assembly in eukaryotes Vasileios Kargas1,2,3†, Pablo Castro-Hartmann1,2,3†, Norberto Escudero-Urquijo1,2,3, Kyle Dent1,2,3, Christine Hilcenko1,2,3, Carolin Sailer4, Gertrude Zisser5, Maria J Marques-Carvalho1,2,3, Simone Pellegrino1,2,3, Leszek Wawio´ rka1,2,3,6, Stefan MV Freund7, Jane L Wagstaff7, Antonina Andreeva7, Alexandre Faille1,2,3, Edwin Chen8, Florian Stengel4, Helmut Bergler5, Alan John Warren1,2,3* 1Cambridge Institute for Medical Research, Cambridge, United Kingdom; 2Department of Haematology, University of Cambridge, Cambridge, United Kingdom; 3Wellcome Trust–Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom; 4Department of Biology, University of Konstanz, Konstanz, Germany; 5Institute of Molecular Biosciences, University of Graz, Graz, Austria; 6Department of Molecular Biology, Maria Curie-Skłodowska University, Lublin, Poland; 7MRC Laboratory of Molecular Biology, Cambridge, United Kingdom; 8Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom Abstract During their final maturation in the cytoplasm, pre-60S ribosomal particles are converted to translation-competent large ribosomal subunits. Here, we present the mechanism of peptidyltransferase centre (PTC) completion that explains how integration of the last ribosomal *For correspondence: [email protected] proteins is coupled to release of the nuclear export adaptor Nmd3. Single-particle cryo-EM reveals that eL40 recruitment stabilises helix 89 to form the uL16 binding site. The loading of uL16 unhooks † These authors contributed helix 38 from Nmd3 to adopt its mature conformation. In turn, partial retraction of the L1 stalk is equally to this work coupled to a conformational switch in Nmd3 that allows the uL16 P-site loop to fully accommodate Competing interests: The into the PTC where it competes with Nmd3 for an overlapping binding site (base A2971). -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.