Supplemental Material
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
The Prognostic Utility and Clinical Outcomes of MNX1-AS1 Expression in Cancers: a Systematic Review and Meta-Analysis
The prognostic utility and clinical outcomes of MNX1-AS1 expression in cancers: a systematic review and meta-analysis Juan Li The rst aliated hospital, college of medicine, zhejiang university https://orcid.org/0000-0002-0121-7098 Wen Jin The rst aliated hospital, college of medicine, zhejiang university Zhengyu Zhang The rst aliated hospital, college of medicine, zhejiang university Jingjing Chu The rst aliated hospital, college of medicine, zhejiang university Hui Yang The rst aliated hospital, college of meicine, zhejiang university Chang Li the rst aliated hospital, college of medicine, zhejiang university Ruiyin Dong The rst aliated hospital, college of medicine, zhejiang university Cailian Zhao ( [email protected] ) https://orcid.org/0000-0001-8337-0610 Primary research Keywords: Long non-coding RNA, MNX1-AS1, Cancer, Prognosis Posted Date: March 25th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-19089/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/9 Abstract Background: Recently, emerging studies have identied that MNX1-AS1 highly expressed among variety of cancers and related with worse prognosis of cancer patients. The purpose of this study was to evaluate the relationship between MNX1-AS1 expression with clinical features and prognosis in different cancers. Methods: In this study, we searched the Web of Science, PubMed, CNKI, and Wanfang databases to nd relevant studies of MNX1-AS1. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% condence intervals (CIs) were applied to explore the prognostic and clinical signicance of MNX1-AS1. Results: A total of 9 literatures were included in this study, including 882 cancer patients. -
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. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
CSE642 Final Version
Eindhoven University of Technology MASTER Dimensionality reduction of gene expression data Arts, S. Award date: 2018 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Eindhoven University of Technology MASTER THESIS Dimensionality Reduction of Gene Expression Data Author: S. (Sako) Arts Daily Supervisor: dr. V. (Vlado) Menkovski Graduation Committee: dr. V. (Vlado) Menkovski dr. D.C. (Decebal) Mocanu dr. N. (Nikolay) Yakovets May 16, 2018 v1.0 Abstract The focus of this thesis is dimensionality reduction of gene expression data. I propose and test a framework that deploys linear prediction algorithms resulting in a reduced set of selected genes relevant to a specified case. Abstract In cancer research there is a large need to automate parts of the process of diagnosis, this is mainly to reduce cost, make it faster and more accurate. -
Supplementary Materials
Supplementary material. S1. Images from automatic imaging reader Cytation™ 1. A, A’, A’’ present the same field of view. Cells migrating from the upper compartment of the inserts are stained with DID (red color), Cell nuclei are stained with DAPI (blue color). A’ and A’’ demonstrate the method of analysis (A’ – nuclei numbering, A’’ – migrating cells numbering), scale bar – 300 µm. ASC BM-MSC Y BM-MSC A mean SEM mean SEM mean SEM CXCL6 2.240 0.727 4.158 0.677 2.149 1.816 CXCL16 4.277 0.248 0.763 0.405 1.130 0.346 CXCL12 -2.855 0.483 -4.528 0.226 -3.616 0.318 SMAD3 -0.511 0.191 -1.522 0.188 -1.332 0.215 TGFB2 3.742 0.533 1.238 0.210 0.990 0.568 COL14A 1.532 0.357 -0.397 0.736 -1.522 0.469 MHX 1.397 0.414 1.145 0.412 0.642 0.613 SCX 2.984 0.301 2.062 0.320 2.031 0.249 RUNX2 3.290 0.359 2.319 0.388 2.546 0.529 PPRAG 1.720 0.303 2.926 0.423 2.912 0.215 Supplementary material S3. The table provides the mean Δct and SEM values for all genes and all groups analyzed in this study (n=8 in each group). Supplementary material S2. The table provides the list of genes which displayed significantly different expression between hASCs and hBM-MSCs A based on microarray nr Exp p-value Exp Fold Change ID Symbol Entrez Gene Name 1 2.84E-05 16.896 16960355 TM4SF1 transmembrane 4 L six family member 1 2 9.10E-09 12.238 16684080 IFI6 interferon alpha inducible protein 6 3 3.93E-08 11.851 16667702 VCAM1 vascular cell adhesion molecule 1 4 1.54E-05 9.944 16840113 CXCL16 C-X-C motif chemokine ligand 16 5 2.01E-06 9.123 16852463 RAB27B RAB27B, member RAS oncogene -
Identification of RNA Bound to the TDP-43 Ribonucleoprotein Complex in the Adult Mouse Brain
Identification of RNA bound to the TDP-43 ribonucleoprotein complex in the adult mouse brain Running Title: Identification of RNA bound to TDP-43 Ramesh K. Narayanan1, Marie Mangelsdorf1, Ajay Panwar1, Tim J. Butler1, Peter G. Noakes1,2, Robyn H. Wallace1,3* 1Queensland Brain Institute, 2School of Biomedical Sciences, 3School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia *Corresponding author: [email protected] Objectives. Cytoplasmic inclusions containing TDP-43 are a pathological hallmark of several neurodegenerative disorders, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. TDP-43 is an RNA binding protein involved in gene regulation through control of RNA transcription, splicing and transport. However, the function of TDP-43 in the nervous system is largely unknown and its role in the pathogenesis of ALS is unclear. The aim of this study was to identify genes in the central nervous system that are regulated by TDP-43. Methods. RNA-immunoprecipitation with anti-TDP-43 antibody, followed by microarray analysis (RIP- chip), was used to isolate and identify RNA bound to TDP-43 protein from mouse brain. Results. This analysis produced a list of 1,839 potential TDP-43 gene targets, many of which overlap with previous studies and whose functions include RNA processing and synaptic function. Immunohistochemistry demonstrated that the TDP-43 protein could be found at the presynaptic membrane of axon terminals in the neuromuscular junction in mice. Conclusions. The finding that TDP-43 binds to RNA that codes for genes related to synaptic function, together with the localisation of TDP-43 protein at axon terminals, suggest a role for TDP-43 in the transport of synaptic mRNAs into distal processes. -
Spatial and Temporal Specification of Neural Fates by Transcription Factor Codes François Guillemot
REVIEW 3771 Development 134, 3771-3780 (2007) doi:10.1242/dev.006379 Spatial and temporal specification of neural fates by transcription factor codes François Guillemot The vertebrate central nervous system contains a great diversity Box 1. Neurons and glial cells of neurons and glial cells, which are generated in the embryonic neural tube at specific times and positions. Several classes of transcription factors have been shown to control various steps in the differentiation of progenitor cells in the neural tube and to determine the identity of the cells produced. Recent evidence indicates that combinations of transcription factors of the homeodomain and basic helix-loop-helix families establish molecular codes that determine both where and when the different kinds of neurons and glial cells are generated. Introduction Neuron Oligodendrocyte Astrocyte A multitude of neurons of different types, as well as oligodendrocytes and astrocytes (see Box 1), are generated as the vertebrate central The vertebrate central nervous system comprises three primary cell nervous system develops. These different neural cells are generated types, including neurons and two types of glial cells. Neurons are at defined times and positions by multipotent progenitors located in electrically excitable cells that process and transmit information via the walls of the embryonic neural tube. Progenitors located in the the release of neurotransmitters at synapses. Different subtypes of ventral neural tube at spinal cord level first produce motor neurons, neurons can be distinguished by the morphology of their cell body which innervate skeletal muscles and later produce oligodendrocytes and dendritic tree, the type of cells they connect with via their axon, the type of neurotransmitter used, etc. -
Aberrant Development of Pancreatic Beta Cells Derived from Human Ipscs with FOXA2 Deficiency Ahmed K
Elsayed et al. Cell Death and Disease (2021) 12:103 https://doi.org/10.1038/s41419-021-03390-8 Cell Death & Disease ARTICLE Open Access Aberrant development of pancreatic beta cells derived from human iPSCs with FOXA2 deficiency Ahmed K. Elsayed1, Ihab Younis2, Gowher Ali1, Khalid Hussain3 and Essam M. Abdelalim 1,4 Abstract FOXA2 has been identified as an essential factor for pancreas development and emerging evidence supports an association between FOXA2 and diabetes. Although the role of FOXA2 during pancreatic development is well-studied in animal models, its role during human islet cell development remains unclear. Here, we generated induced pluripotent stem cells (iPSCs) from a patient with FOXA2 haploinsufficiency (FOXA2+/− iPSCs) followed by beta-cell differentiation to understand the role of FOXA2 during pancreatic beta-cell development. Our results showed that FOXA2 haploinsufficiency resulted in aberrant expression of genes essential for the differentiation and proper functioning of beta cells. At pancreatic progenitor (PP2) and endocrine progenitor (EPs) stages, transcriptome analysis showed downregulation in genes associated with pancreatic development and diabetes and upregulation in genes associated with nervous system development and WNT signaling pathway. Knockout of FOXA2 in control iPSCs (FOXA2−/− iPSCs) led to severe phenotypes in EPs and beta-cell stages. The expression of NGN3 and its downstream targets at EPs as well as INSUILIN and GLUCAGON at the beta-cell stage, were almost absent in the cells derived from FOXA2−/− iPSCs. These findings indicate that FOXA2 is crucial for human pancreatic endocrine development and its defect may lead to diabetes based on FOXA2 dosage. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Introduction TFs lead to neonatal diabetes, which can be associated During human development, early endodermal tissue with pancreatic hypoplasia/agenesis in some mutations4. -
(HLXB9) in Infant Acute Myeloid Leukemia
EDITORIALS Novel insights into the role of aberrantly expressed MNX1 (HLXB9) in infant acute myeloid leukemia Juerg Schwaller University Children’s Hospital beider Basel (UKBB), Department of Biomedicine, University of Basel Childhood Leukemia Group ZLF, Switzerland. E-mail: [email protected] doi:10.3324/haematol.2018.205971 lmost two decades ago, the molecular characteriza- TP53 and its target the cyclin-dependent kinase inhibitor 1A tion of a t(7;12)(q36;p13) chromosomal translocation (CDKN1A, aka p21WAF1/CIP1). As oncogene-induced senescence Ain very young children with acute myeloid leukemia is a hallmark of early malignant transformation of solid (AML) and poor outcome identified a fusion mRNA poten- tumors, this finding suggests that MNX1 overexpression may tially encoding for a chimeric protein that contains the point- result in a pre-cancerous state.16 However, one has to keep in ed (PNT) and ETS domains of the ETS variant 6 (ETV6) gene, mind that both of the models used are immortalized solid also known as TEL1 (Translocating E26 transforming-specific cancer cell lines that may carry potent oncogenes such as leukemia 1) on 12p13, joined to the regulatory sequences and mutated NRASQ61K present in HT-1080 (https://portals.broadinsti- first exons of the HLXB9 homeobox gene.1 Previous work tute.org/ccle/page?cell_line= HT1080_SOFT_TISSUE). reported a series of infant AML patients with t(7;12)(q36;p13) Nevertheless, previous work has shown that overexpression with blasts carrying a potential ETV6 translocation (revealed of well-characterized AML-associated fusion oncogenes (e.g. by a split FISH signal).2,3 In fact, the entire HLBX9 gene seems PML-RARA, RUNX1-ETO, CBFB-MYH11) induces DNA to be transferred onto the der(12) without disruption of the damage, and activates a CDKN1A-dependent cell cycle arrest gene itself. -
Figure S1. Basic Information of RNA-Seq Results. (A) Bar Plot of Reads Component for Each Sample
Figure S1. Basic information of RNA-seq results. (A) Bar plot of reads component for each sample. (B) Dot plot shows the principal component analysis (PCA) of each sample. (C) Venn diagram of DEGs for three time points, the overlap part of the circles represents common differentially expressed genes between combinations. Figure S2. Scatter plot of DEGs for each time point. The X and Y axes represent the logarithmic value of gene expression. Red represents up-regulated DEG, blue represents down-regulated DEG, and gray represents non-DEG. Table S1. Primers used for quantitative real-time PCR analysis of DEGs. Gene Primer Sequence Forward 5’-CTACGAGTGGATGGTCAAGAGC-3’ FOXO1 Reverse 5’-CCAGTTCCTTCATTCTGCACACG-3’ Forward 5’-GACGTCCGGCATCAGAGAAA-3’ IRS2 Reverse 5’-TCCACGGCTAATCGTCACAG-3’ Forward 5’-CACAACCAGGACCTCACACC-3’ IRS1 Reverse 5’-CTTGGCACGATAGAGAGCGT-3’ Forward 5’-AGGATACCACTCCCAACAGACCT-3’ IL6 Reverse 5’-CAAGTGCATCATCGTTGTTCATAC-3’ Forward 5’-TCACGTTGTACGCAGCTACC-3’ CCL5 Reverse 5’-CAGTCCTCTTACAGCCTTTGG-3’ Forward 5’-CTGTGCAGCCGCAGTGCCTACC-3’ BMP7 Reverse 5’-ATCCCTCCCCACCCCACCATCT-3’ Forward 5’-CTCTCCCCCTCGACTTCTGA-3’ BCL2 Reverse 5’-AGTCACGCGGAACACTTGAT-3’ Forward 5’-CTGTCGAACACAGTGGTACCTG-3’ FGF7 Reverse 5’-CCAACTGCCACTGTCCTGATTTC-3’ Forward 5’-GGGAGCCAAAAGGGTCATCA-3’ GAPDH Reverse 5’-CGTGGACTGTGGTCATGAGT-3’ Supplementary material: Differentially expressed genes log2(SADS-CoV_12h/ Qvalue (SADS-CoV _12h/ Gene Symbol Control_12h) Control_12h) PTGER4 -1.03693 6.79E-04 TMEM72 -3.08132 3.66E-04 IFIT2 -1.02918 2.11E-07 FRAT2 -1.09282 4.66E-05 -
Human Social Genomics in the Multi-Ethnic Study of Atherosclerosis
Getting “Under the Skin”: Human Social Genomics in the Multi-Ethnic Study of Atherosclerosis by Kristen Monét Brown A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Epidemiological Science) in the University of Michigan 2017 Doctoral Committee: Professor Ana V. Diez-Roux, Co-Chair, Drexel University Professor Sharon R. Kardia, Co-Chair Professor Bhramar Mukherjee Assistant Professor Belinda Needham Assistant Professor Jennifer A. Smith © Kristen Monét Brown, 2017 [email protected] ORCID iD: 0000-0002-9955-0568 Dedication I dedicate this dissertation to my grandmother, Gertrude Delores Hampton. Nanny, no one wanted to see me become “Dr. Brown” more than you. I know that you are standing over the bannister of heaven smiling and beaming with pride. I love you more than my words could ever fully express. ii Acknowledgements First, I give honor to God, who is the head of my life. Truly, without Him, none of this would be possible. Countless times throughout this doctoral journey I have relied my favorite scripture, “And we know that all things work together for good, to them that love God, to them who are called according to His purpose (Romans 8:28).” Secondly, I acknowledge my parents, James and Marilyn Brown. From an early age, you two instilled in me the value of education and have been my biggest cheerleaders throughout my entire life. I thank you for your unconditional love, encouragement, sacrifices, and support. I would not be here today without you. I truly thank God that out of the all of the people in the world that He could have chosen to be my parents, that He chose the two of you.