Subsets of Very Low Risk Wilms Tumor Show Distinctive Gene Expression, Histologic, and Clinical Features Simone T
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Detection of Interacting Transcription Factors in Human Tissues Using
Myšičková and Vingron BMC Genomics 2012, 13(Suppl 1):S2 http://www.biomedcentral.com/1471-2164/13/S1/S2 PROCEEDINGS Open Access Detection of interacting transcription factors in human tissues using predicted DNA binding affinity Alena Myšičková*, Martin Vingron From The Tenth Asia Pacific Bioinformatics Conference (APBC 2012) Melbourne, Australia. 17-19 January 2012 Abstract Background: Tissue-specific gene expression is generally regulated by combinatorial interactions among transcription factors (TFs) which bind to the DNA. Despite this known fact, previous discoveries of the mechanism that controls gene expression usually consider only a single TF. Results: We provide a prediction of interacting TFs in 22 human tissues based on their DNA-binding affinity in promoter regions. We analyze all possible pairs of 130 vertebrate TFs from the JASPAR database. First, all human promoter regions are scanned for single TF-DNA binding affinities with TRAP and for each TF a ranked list of all promoters ordered by the binding affinity is created. We then study the similarity of the ranked lists and detect candidates for TF-TF interaction by applying a partial independence test for multiway contingency tables. Our candidates are validated by both known protein-protein interactions (PPIs) and known gene regulation mechanisms in the selected tissue. We find that the known PPIs are significantly enriched in the groups of our predicted TF-TF interactions (2 and 7 times more common than expected by chance). In addition, the predicted interacting TFs for studied tissues (liver, muscle, hematopoietic stem cell) are supported in literature to be active regulators or to be expressed in the corresponding tissue. -
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. -
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). -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Immunohistochemistry Stain Offerings
immunohistochemistry stain offerings TRUSTED PATHOLOGISTS. INVALUABLE ANSWERS.™ MARCHMAY 20172021 www.aruplab.com/ap-ihcaruplab.com/ap-ihc InformationInformation in this brochurein this brochure is current is current as of as May of March 2021. 2017. All content All content is subject is subject to tochange. change. Please contactPlease ARUPcontact ClientARUP Services Client Services at 800-522-2787 at (800) 522-2787 with any with questions any questions or concerns.or concerns. ARUP LABORATORIES As a nonprofit, academic institution of the University of Utah and its Department We believe in of Pathology, ARUP believes in collaborating, sharing and contributing to laboratory science in ways that benefit our clients and their patients. collaborating, Our test menu is one of the broadest in the industry, encompassing more sharing and than 3,000 tests, including highly specialized and esoteric assays. We offer comprehensive testing in the areas of genetics, molecular oncology, pediatrics, contributing pain management, and more. to laboratory ARUP’s clients include many of the nation’s university teaching hospitals and children’s hospitals, as well as multihospital groups, major commercial science in ways laboratories, and group purchasing organizations. We believe that healthcare should be delivered as close to the patient as possible, which is why we support that provide our clients’ efforts to be the principal healthcare provider in the communities they serve by offering highly complex assays and accompanying consultative support. the best value Offering analytics, consulting, and decision support services, ARUP provides for the patient. clients with the utilization management tools necessary to prosper in this time of value-based care. -
Congenital Eye Disorders Gene Panel
Congenital eye disorders gene panel Contact details Introduction Regional Genetics Service Ocular conditions are highly heterogeneous and show considerable phenotypic overlap. 1 in Levels 4-6, Barclay House 2,500 children in the UK are diagnosed as blind or severely visually impaired by the time they 37 Queen Square reach one year old. As many as half of these cases are likely to be inherited and remain undiagnosed due to the vast number of genes involved in these conditions. Many congenital London, WC1N 3BH eye disorders causing visual impairment or blindness at birth or progressive visual impairment T +44 (0) 20 7762 6888 also include syndromic conditions involving additional metabolic, developmental, physical or F +44 (0) 20 7813 8578 sensory abnormalities. Gene panels offer the enhanced probability of diagnosis as a very large number of genes can be interrogated. Samples required Ocular birth defects include all inheritance modalities. Autosomal dominant and recessive 5ml venous blood in plastic EDTA diseases as well as X-linked dominant and recessive diseases are seen. These conditions can bottles (>1ml from neonates) also be caused by de novo variants. Prenatal testing must be arranged Referrals in advance, through a Clinical Genetics department if possible. Patients presenting with a phenotype appropriate for the requested sub-panel Amniotic fluid or CV samples Referrals will be accepted from clinical geneticists and consultants in ophthalmology. should be sent to Cytogenetics for Prenatal testing dissecting and culturing, with instructions to forward the sample Prenatal diagnosis may be offered as appropriate where pathogenic variants have been to the Regional Molecular Genetics identified in accordance with expected inheritance pattern and where appropriate parental laboratory for analysis testing and counselling has been conducted. -
A Dissertation Entitled the Androgen Receptor
A Dissertation entitled The Androgen Receptor as a Transcriptional Co-activator: Implications in the Growth and Progression of Prostate Cancer By Mesfin Gonit Submitted to the Graduate Faculty as partial fulfillment of the requirements for the PhD Degree in Biomedical science Dr. Manohar Ratnam, Committee Chair Dr. Lirim Shemshedini, Committee Member Dr. Robert Trumbly, Committee Member Dr. Edwin Sanchez, Committee Member Dr. Beata Lecka -Czernik, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo August 2011 Copyright 2011, Mesfin Gonit This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of The Androgen Receptor as a Transcriptional Co-activator: Implications in the Growth and Progression of Prostate Cancer By Mesfin Gonit As partial fulfillment of the requirements for the PhD Degree in Biomedical science The University of Toledo August 2011 Prostate cancer depends on the androgen receptor (AR) for growth and survival even in the absence of androgen. In the classical models of gene activation by AR, ligand activated AR signals through binding to the androgen response elements (AREs) in the target gene promoter/enhancer. In the present study the role of AREs in the androgen- independent transcriptional signaling was investigated using LP50 cells, derived from parental LNCaP cells through extended passage in vitro. LP50 cells reflected the signature gene overexpression profile of advanced clinical prostate tumors. The growth of LP50 cells was profoundly dependent on nuclear localized AR but was independent of androgen. Nevertheless, in these cells AR was unable to bind to AREs in the absence of androgen. -
Notch Signalling Regulates Epibranchial Placode Patterning
Development | Peer review history Notch signalling regulates epibranchial placode patterning and segregation Li Wang, Junjie Xie, Haoran Zhang, Long Hin Tsang, Sze Lan Tsang, Eike-Benjamin Braune, Urban Lendahl and Mai Har Sham DOI: 10.1242/dev.183665 Editor: Francois Guillemot Review timeline Original submission: 16 August 2019 Editorial decision: 11 September 2019 First revision received: 24 December 2019 Accepted: 14 January 2020 Original submission First decision letter MS ID#: DEVELOP/2019/183665 MS TITLE: Notch signaling regulates epibranchial placode patterning, segregation and differentiation AUTHORS: Li Wang, Junjie Xie, Haoran Zhang, Long Hin Tsang, Sze Lan Tsang, Urban Lendahl, and Mai Har Sham I have now received the reports of three referees on your manuscript and I have reached a decision. The reports are appended below and you can access them online: please go to BenchPressand click on the 'Manuscripts with Decisions' queue in the Author Area. As you will see, all the referees express great interest in your work, but they also have some significant criticisms and recommend a substantial revision of your manuscript before we can consider publication. If you are able to revise the manuscript along the lines suggested, which may involve further experiments, I will be happy to receive a revised version of the manuscript. The identification of the Notch targets mentioned by referee 2 would be of course interesting but seems beyond the scope of the study unless you already have some evidence in that direction. The request of referee 3 that you analyse the effect of manipulations of Notch signalling at other stages than E9.5 will also require a significant amount of work without being necessarily informative. -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in -
Evolving Principles Underlying Neural Lineage Conversion
F1000Research 2019, 8(F1000 Faculty Rev):1548 Last updated: 16 SEP 2019 REVIEW Evolving principles underlying neural lineage conversion and their relevance for biomedical translation [version 1; peer review: 4 approved] Lea Jessica Flitsch, Oliver Brüstle Institute of Reconstructive Neurobiology, University of Bonn School of Medicine & University Hospital Bonn, Bonn, North Rhine Wesphalia, 53127, Germany First published: 30 Aug 2019, 8(F1000 Faculty Rev):1548 ( Open Peer Review v1 https://doi.org/10.12688/f1000research.18926.1) Latest published: 30 Aug 2019, 8(F1000 Faculty Rev):1548 ( https://doi.org/10.12688/f1000research.18926.1) Reviewer Status Abstract Invited Reviewers Scientific and technological advances of the past decade have shed light 1 2 3 4 on the mechanisms underlying cell fate acquisition, including its transcriptional and epigenetic regulation during embryonic development. version 1 This knowledge has enabled us to purposefully engineer cell fates in vitro published by manipulating expression levels of lineage-instructing transcription 30 Aug 2019 factors. Here, we review the state of the art in the cell programming field with a focus on the derivation of neural cells. We reflect on what we know about the mechanisms underlying fate changes in general and on the F1000 Faculty Reviews are written by members of degree of epigenetic remodeling conveyed by the distinct reprogramming the prestigious F1000 Faculty. They are and direct conversion strategies available. Moreover, we discuss the commissioned and are peer reviewed before implications of residual epigenetic memory for biomedical applications such publication to ensure that the final, published version as disease modeling and neuroregeneration. Finally, we cover recent developments approaching cell fate conversion in the living brain and is comprehensive and accessible. -
Supplementary File 1
Supplementary Materials Figure S1. RNA sequencing quality control report. The upper panel showed per base sequence quality of (a) miRNA from 786-O and ACHN, and (b) RNA from 786-O and ACHN; the lower panel showed the per sequence quality scores of (c) miRNA from 786-O and ACHN, and (d) RNA from 786-O and ACHN. The quality score from 15 to 99 bp fell into the green background, indicating good quality calls. The majority read > 35 indicated good sequencing quality. Table S1. The list of miRNA with significant changes (ACHN vs 786-O) #miRNA Fold Change #miRNA Fold Change #miRNA Fold Change hsa-let-7a-2-3p -490.00 hsa-miR-192-5p 21.37 hsa-miR-323a-3p 135.76 hsa-let-7c-3p -10.30 hsa-miR-194-3p 31.18 hsa-miR-323b-3p 53.75 hsa-miR-100-3p -26.39 hsa-miR-194-5p 21.87 hsa-miR-326 -16.46 hsa-miR-100-5p -63.93 hsa-miR-195-5p 8.31 hsa-miR-329-3p 101.81 hsa-miR-105-5p -288.00 hsa-miR-196a-3p 5.28 hsa-miR-329-5p 436 hsa-miR-1185-1-3p 32.08 hsa-miR-199b-5p -9.75 hsa-miR-335-3p 73.67 hsa-miR-1185-2-3p 37.22 hsa-miR-203a-3p 222.25 hsa-miR-335-5p 71.58 hsa-miR-1185-5p 91.13 hsa-miR-205-5p -5.12 hsa-miR-337-3p 1696 hsa-miR-1197 7.38 hsa-miR-20b-5p 5.28 hsa-miR-337-5p 12141 hsa-miR-125b-1-3p -301.58 hsa-miR-211-5p 118 hsa-miR-34b-3p 1110.62 hsa-miR-125b-2-3p 5.91 hsa-miR-215-5p 18.88 hsa-miR-34b-5p 796.38 hsa-miR-1262 16.28 hsa-miR-218-5p 14.37 hsa-miR-34c-3p 10751 hsa-miR-1266-5p 6.62 hsa-miR-224-5p 118 hsa-miR-34c-5p 836.81 hsa-miR-127-3p 1723.39 hsa-miR-2277-3p 6.65 hsa-miR-3614-5p 141 hsa-miR-127-5p 4334.00 hsa-miR-2682-3p 6.63 hsa-miR-3617-5p -140 hsa-miR-1271-5p