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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Id4, a New Candidate Gene for Senile Osteoporosis, Acts As a Molecular Switch Promoting Osteoblast Differentiation
Id4, a New Candidate Gene for Senile Osteoporosis, Acts as a Molecular Switch Promoting Osteoblast Differentiation Yoshimi Tokuzawa1., Ken Yagi1., Yzumi Yamashita1, Yutaka Nakachi1, Itoshi Nikaido1, Hidemasa Bono1, Yuichi Ninomiya1, Yukiko Kanesaki-Yatsuka1, Masumi Akita2, Hiromi Motegi3, Shigeharu Wakana3, Tetsuo Noda3,4, Fred Sablitzky5, Shigeki Arai6, Riki Kurokawa6, Toru Fukuda7, Takenobu Katagiri7, Christian Scho¨ nbach8,9, Tatsuo Suda1, Yosuke Mizuno1, Yasushi Okazaki1* 1 Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama, Japan, 2 Division of Morphological Science, Biomedical Research Center, Saitama Medical University, Iruma-gun, Saitama, Japan, 3 RIKEN BioResource Center, Tsukuba, Ibaraki, Japan, 4 The Cancer Institute of the Japanese Foundation for Cancer Research, Koto-ward, Tokyo, Japan, 5 Developmental Genetics and Gene Control, Institute of Genetics, University of Nottingham, Queen’s Medical Center, Nottingham, United Kingdom, 6 Division of Gene Structure and Function, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama, Japan, 7 Division of Pathophysiology, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama, Japan, 8 Division of Genomics and Genetics, Nanyang Technological University School of Biological Sciences, Singapore, Singapore, 9 Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan Abstract Excessive accumulation of bone marrow adipocytes observed in senile osteoporosis or age-related osteopenia is caused by the unbalanced differentiation of MSCs into bone marrow adipocytes or osteoblasts. Several transcription factors are known to regulate the balance between adipocyte and osteoblast differentiation. However, the molecular mechanisms that regulate the balance between adipocyte and osteoblast differentiation in the bone marrow have yet to be elucidated. -
Multifactorial Erβ and NOTCH1 Control of Squamous Differentiation and Cancer
Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks, … , Karine Lefort, G. Paolo Dotto J Clin Invest. 2014;124(5):2260-2276. https://doi.org/10.1172/JCI72718. Research Article Oncology Downmodulation or loss-of-function mutations of the gene encoding NOTCH1 are associated with dysfunctional squamous cell differentiation and development of squamous cell carcinoma (SCC) in skin and internal organs. While NOTCH1 receptor activation has been well characterized, little is known about how NOTCH1 gene transcription is regulated. Using bioinformatics and functional screening approaches, we identified several regulators of the NOTCH1 gene in keratinocytes, with the transcription factors DLX5 and EGR3 and estrogen receptor β (ERβ) directly controlling its expression in differentiation. DLX5 and ERG3 are required for RNA polymerase II (PolII) recruitment to the NOTCH1 locus, while ERβ controls NOTCH1 transcription through RNA PolII pause release. Expression of several identified NOTCH1 regulators, including ERβ, is frequently compromised in skin, head and neck, and lung SCCs and SCC-derived cell lines. Furthermore, a keratinocyte ERβ–dependent program of gene expression is subverted in SCCs from various body sites, and there are consistent differences in mutation and gene-expression signatures of head and neck and lung SCCs in female versus male patients. Experimentally increased ERβ expression or treatment with ERβ agonists inhibited proliferation of SCC cells and promoted NOTCH1 expression and squamous differentiation both in vitro and in mouse xenotransplants. Our data identify a link between transcriptional control of NOTCH1 expression and the estrogen response in keratinocytes, with implications for differentiation therapy of squamous cancer. Find the latest version: https://jci.me/72718/pdf Research article Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks,1,2 Paola Ostano,3 Seung-Hee Jo,1,2 Jun Dai,1,2 Spiro Getsios,4 Piotr Dziunycz,5 Günther F.L. -
A Core Transcriptional Network Composed of Pax2/8, Gata3 and Lim1 Regulates Key Players of Pro/Mesonephros Morphogenesis
Developmental Biology 382 (2013) 555–566 Contents lists available at ScienceDirect Developmental Biology journal homepage: www.elsevier.com/locate/developmentalbiology Genomes and Developmental Control A core transcriptional network composed of Pax2/8, Gata3 and Lim1 regulates key players of pro/mesonephros morphogenesis Sami Kamel Boualia a, Yaned Gaitan a, Mathieu Tremblay a, Richa Sharma a, Julie Cardin b, Artur Kania b, Maxime Bouchard a,n a Goodman Cancer Research Centre and Department of Biochemistry, McGill University, 1160 Pine Ave. W., Montreal, Quebec, Canada H3A 1A3 b Institut de Recherches Cliniques de Montréal, Montréal, Québec, Canada H2W 1R7, Department of Anatomy and Cell Biology, Division of Experimental Medicine, McGill University, Montréal, Quebec, Canada, H3A 2B2 and Faculté de médecine, Université de Montréal, Montréal, Quebec, Canada, H3C 3J7. article info abstract Article history: Translating the developmental program encoded in the genome into cellular and morphogenetic Received 23 January 2013 functions requires the deployment of elaborate gene regulatory networks (GRNs). GRNs are especially Received in revised form crucial at the onset of organ development where a few regulatory signals establish the different 27 July 2013 programs required for tissue organization. In the renal system primordium (the pro/mesonephros), Accepted 30 July 2013 important regulators have been identified but their hierarchical and regulatory organization is still Available online 3 August 2013 elusive. Here, we have performed a detailed analysis of the GRN underlying mouse pro/mesonephros Keywords: development. We find that a core regulatory subcircuit composed of Pax2/8, Gata3 and Lim1 turns on a Kidney development deeper layer of transcriptional regulators while activating effector genes responsible for cell signaling Transcription and tissue organization. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
Supplementary Materials
Supplementary Materials: Supplemental Table 1 Abbreviations FMDV Foot and Mouth Disease Virus FMD Foot and Mouth Disease NC Non-treated Control DEGs Differentially Expressed Genes RNA-seq High-throughput Sequencing of Mrna RT-qPCR Quantitative Real-time Reverse Transcriptase PCR TCID50 50% Tissue Culture Infective Doses CPE Cytopathic Effect MOI Multiplicity of Infection DMEM Dulbecco's Modified Eagle Medium FBS Fetal Bovine Serum PBS Phosphate Buffer Saline QC Quality Control FPKM Fragments per Kilo bases per Million fragments method GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes R Pearson Correlation Coefficient NFKBIA NF-kappa-B Inhibitor alpha IL6 Interleukin 6 CCL4 C-C motif Chemokine 4 CXCL2 C-X-C motif Chemokine 2 TNF Tumor Necrosis Factor VEGFA Vascular Endothelial Growth Gactor A CCL20 C-C motif Chemokine 20 CSF2 Macrophage Colony-Stimulating Factor 2 GADD45B Growth Arrest and DNA Damage Inducible 45 beta MYC Myc proto-oncogene protein FOS Proto-oncogene c-Fos MCL1 Induced myeloid leukemia cell differentiation protein Mcl-1 MAP3K14 Mitogen-activated protein kinase kinase kinase 14 IRF1 Interferon regulatory factor 1 CCL5 C-C motif chemokine 5 ZBTB3 Zinc finger and BTB domain containing 3 OTX1 Orthodenticle homeobox 1 TXNIP Thioredoxin-interacting protein ZNF180 Znc Finger Protein 180 ZNF36 Znc Finger Protein 36 ZNF182 Zinc finger protein 182 GINS3 GINS complex subunit 3 KLF15 Kruppel-like factor 15 Supplemental Table 2 Primers for Verification of RNA-seq-detected DEGs with RT-qPCR TNF F: CGACTCAGTGCCGAGATCAA R: -
Pluripotency Factors Regulate Definitive Endoderm Specification Through Eomesodermin
Downloaded from genesdev.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Pluripotency factors regulate definitive endoderm specification through eomesodermin Adrian Kee Keong Teo,1,2 Sebastian J. Arnold,3 Matthew W.B. Trotter,1 Stephanie Brown,1 Lay Teng Ang,1 Zhenzhi Chng,1,2 Elizabeth J. Robertson,4 N. Ray Dunn,2,5 and Ludovic Vallier1,5,6 1Laboratory for Regenerative Medicine, University of Cambridge, Cambridge CB2 0SZ, United Kingdom; 2Institute of Medical Biology, A*STAR (Agency for Science, Technology, and Research), Singapore 138648; 3Renal Department, Centre for Clinical Research, University Medical Centre, 79106 Freiburg, Germany; 4Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, United Kingdom Understanding the molecular mechanisms controlling early cell fate decisions in mammals is a major objective toward the development of robust methods for the differentiation of human pluripotent stem cells into clinically relevant cell types. Here, we used human embryonic stem cells and mouse epiblast stem cells to study specification of definitive endoderm in vitro. Using a combination of whole-genome expression and chromatin immunoprecipitation (ChIP) deep sequencing (ChIP-seq) analyses, we established an hierarchy of transcription factors regulating endoderm specification. Importantly, the pluripotency factors NANOG, OCT4, and SOX2 have an essential function in this network by actively directing differentiation. Indeed, these transcription factors control the expression of EOMESODERMIN (EOMES), which marks the onset of endoderm specification. In turn, EOMES interacts with SMAD2/3 to initiate the transcriptional network governing endoderm formation. Together, these results provide for the first time a comprehensive molecular model connecting the transition from pluripotency to endoderm specification during mammalian development. -
Mediator of DNA Damage Checkpoint 1 (MDC1) Is a Novel Estrogen Receptor Co-Regulator in Invasive 6 Lobular Carcinoma of the Breast 7 8 Evelyn K
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423142; this version posted December 16, 2020. 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 4.0 International license. 1 Running Title: MDC1 co-regulates ER in ILC 2 3 Research article 4 5 Mediator of DNA damage checkpoint 1 (MDC1) is a novel estrogen receptor co-regulator in invasive 6 lobular carcinoma of the breast 7 8 Evelyn K. Bordeaux1+, Joseph L. Sottnik1+, Sanjana Mehrotra1, Sarah E. Ferrara2, Andrew E. Goodspeed2,3, James 9 C. Costello2,3, Matthew J. Sikora1 10 11 +EKB and JLS contributed equally to this project. 12 13 Affiliations 14 1Dept. of Pathology, University of Colorado Anschutz Medical Campus 15 2Biostatistics and Bioinformatics Shared Resource, University of Colorado Comprehensive Cancer Center 16 3Dept. of Pharmacology, University of Colorado Anschutz Medical Campus 17 18 Corresponding author 19 Matthew J. Sikora, PhD.; Mail Stop 8104, Research Complex 1 South, Room 5117, 12801 E. 17th Ave.; Aurora, 20 CO 80045. Tel: (303)724-4301; Fax: (303)724-3712; email: [email protected]. Twitter: 21 @mjsikora 22 23 Authors' contributions 24 MJS conceived of the project. MJS, EKB, and JLS designed and performed experiments. JLS developed models 25 for the project. EKB, JLS, SM, and AEG contributed to data analysis and interpretation. SEF, AEG, and JCC 26 developed and performed informatics analyses. MJS wrote the draft manuscript; all authors read and revised the 27 manuscript and have read and approved of this version of the manuscript. -
Watsonjn2018.Pdf (1.780Mb)
UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support. -
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. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7