The Signature of Maternal Rearing in the Methylome in Rhesus Macaque Prefrontal Cortex and T Cells
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In Vivo Studies Using the Classical Mouse Diversity Panel
The Mouse Diversity Panel Predicts Clinical Drug Toxicity Risk Where Classical Models Fail Alison Harrill, Ph.D The Hamner-UNC Institute for Drug Safety Sciences 0 The Importance of Predicting Clinical Adverse Drug Reactions (ADR) Figure: Cath O’Driscoll Nature Publishing 2004 Risk ID PGx Testing 1 People Respond Differently to Drugs Pharmacogenetic Markers Identified by Genome-Wide Association Drug Adverse Drug Risk Allele Reaction (ADR) Abacavir Hypersensitivity HLA-B*5701 Flucloxacillin Hepatotoxicity Allopurinol Cutaneous ADR HLA-B*5801 Carbamazepine Stevens-Johnson HLA-B*1502 Syndrome Augmentin Hepatotoxicity DRB1*1501 Ximelagatran Hepatotoxicity DRB1*0701 Ticlopidine Hepatotoxicity HLA-A*3303 Average preclinical populations and human hepatocytes lack the diversity to detect incidence of adverse events that occur only in 1/10,000 people. Current Rodent Models of Risk Assessment The Challenge “At a time of extraordinary scientific progress, methods have hardly changed in several decades ([FDA] 2004)… Toxicologists face a major challenge in the twenty-first century. They need to embrace the new “omics” techniques and ensure that they are using the most appropriate animals if their discipline is to become a more effective tool in drug development.” -Dr. Michael Festing Quantitative geneticist Toxicol Pathol. 2010;38(5):681-90 Rodent Models as a Strategy for Hazard Characterization and Pharmacogenetics Genetically defined rodent models may provide ability to: 1. Improve preclinical prediction of drugs that carry a human safety risk 2. -
Systems and Chemical Biology Approaches to Study Cell Function and Response to Toxins
Dissertation submitted to the Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences Presented by MSc. Yingying Jiang born in Shandong, China Oral-examination: Systems and chemical biology approaches to study cell function and response to toxins Referees: Prof. Dr. Rob Russell Prof. Dr. Stefan Wölfl CONTRIBUTIONS The chapter III of this thesis was submitted for publishing under the title “Drug mechanism predominates over toxicity mechanisms in drug induced gene expression” by Yingying Jiang, Tobias C. Fuchs, Kristina Erdeljan, Bojana Lazerevic, Philip Hewitt, Gordana Apic & Robert B. Russell. For chapter III, text phrases, selected tables, figures are based on this submitted manuscript that has been originally written by myself. i ABSTRACT Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragment- chemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. -
TFEB Regulates Murine Liver Cell Fate During Development and Regeneration ✉ Nunzia Pastore 1,2 , Tuong Huynh1,2, Niculin J
ARTICLE https://doi.org/10.1038/s41467-020-16300-x OPEN TFEB regulates murine liver cell fate during development and regeneration ✉ Nunzia Pastore 1,2 , Tuong Huynh1,2, Niculin J. Herz1,2, Alessia Calcagni’ 1,2, Tiemo J. Klisch1,2, Lorenzo Brunetti 3,4, Kangho Ho Kim5, Marco De Giorgi6, Ayrea Hurley6, Annamaria Carissimo7, Margherita Mutarelli7, Niya Aleksieva 8, Luca D’Orsi7, William R. Lagor6, David D. Moore 5, ✉ Carmine Settembre7,9, Milton J. Finegold10, Stuart J. Forbes8 & Andrea Ballabio 1,2,7,9 1234567890():,; It is well established that pluripotent stem cells in fetal and postnatal liver (LPCs) can differentiate into both hepatocytes and cholangiocytes. However, the signaling pathways implicated in the differentiation of LPCs are still incompletely understood. Transcription Factor EB (TFEB), a master regulator of lysosomal biogenesis and autophagy, is known to be involved in osteoblast and myeloid differentiation, but its role in lineage commitment in the liver has not been investigated. Here we show that during development and upon regen- eration TFEB drives the differentiation status of murine LPCs into the progenitor/cho- langiocyte lineage while inhibiting hepatocyte differentiation. Genetic interaction studies show that Sox9, a marker of precursor and biliary cells, is a direct transcriptional target of TFEB and a primary mediator of its effects on liver cell fate. In summary, our findings identify an unexplored pathway that controls liver cell lineage commitment and whose dysregulation may play a role in biliary cancer. 1 Jan and Dan Duncan Neurological Research Institute, Texas Children Hospital, Houston, TX 77030, USA. 2 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. -
Characterization of BRCA1-Deficient Premalignant Tissues and Cancers Identifies Plekha5 As a Tumor Metastasis Suppressor
ARTICLE https://doi.org/10.1038/s41467-020-18637-9 OPEN Characterization of BRCA1-deficient premalignant tissues and cancers identifies Plekha5 as a tumor metastasis suppressor Jianlin Liu1,2, Ragini Adhav1,2, Kai Miao1,2, Sek Man Su1,2, Lihua Mo1,2, Un In Chan1,2, Xin Zhang1,2, Jun Xu1,2, Jianjie Li1,2, Xiaodong Shu1,2, Jianming Zeng 1,2, Xu Zhang1,2, Xueying Lyu1,2, Lakhansing Pardeshi1,3, ✉ ✉ Kaeling Tan1,3, Heng Sun1,2, Koon Ho Wong 1,3, Chuxia Deng 1,2 & Xiaoling Xu 1,2 1234567890():,; Single-cell whole-exome sequencing (scWES) is a powerful approach for deciphering intra- tumor heterogeneity and identifying cancer drivers. So far, however, simultaneous analysis of single nucleotide variants (SNVs) and copy number variations (CNVs) of a single cell has been challenging. By analyzing SNVs and CNVs simultaneously in bulk and single cells of premalignant tissues and tumors from mouse and human BRCA1-associated breast cancers, we discover an evolution process through which the tumors initiate from cells with SNVs affecting driver genes in the premalignant stage and malignantly progress later via CNVs acquired in chromosome regions with cancer driver genes. These events occur randomly and hit many putative cancer drivers besides p53 to generate unique genetic and pathological features for each tumor. Upon this, we finally identify a tumor metastasis suppressor Plekha5, whose deficiency promotes cancer metastasis to the liver and/or lung. 1 Cancer Centre, Faculty of Health Sciences, University of Macau, Macau, SAR, China. 2 Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau, SAR, China. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Genome-Wide Association Analysis on Coronary Artery Disease in Type 1 Diabetes Suggests Beta-Defensin 127 As a Novel Risk Locus
Genome-wide association analysis on coronary artery disease in type 1 diabetes suggests beta-defensin 127 as a novel risk locus Antikainen, A., Sandholm, N., Tregouet, D-A., Charmet, R., McKnight, A., Ahluwalia, T. V. S., Syreeni, A., Valo, E., Forsblom, C., Gordin, D., Harjutsalo, V., Hadjadj, S., Maxwell, P., Rossing, P., & Groop, P-H. (2020). Genome-wide association analysis on coronary artery disease in type 1 diabetes suggests beta-defensin 127 as a novel risk locus. Cardiovascular Research. https://doi.org/10.1093/cvr/cvaa045 Published in: Cardiovascular Research Document Version: Peer reviewed version Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights Copyright 2020 OUP. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [email protected]. Download date:01. Oct. -
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. -
Full-Length Cdna, Expression Pattern and Association Analysis of the Porcine FHL3 Gene
1473 Asian-Aust. J. Anim. Sci. Vol. 20, No. 10 : 1473 - 1477 October 2007 www.ajas.info Full-length cDNA, Expression Pattern and Association Analysis of the Porcine FHL3 Gene Bo Zuo*, YuanZhu Xiong, Hua Yang and Jun Wang Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture & Key Lab of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, P. R. China ABSTRACT : Four-and-a-half LIM-only protein 3 (FHL3) is a member of the LIM protein superfamily and can participate in mediating protein-protein interaction by binding one another through their LIM domains. In this study, the 5'- and 3'- cDNA ends were characterized by RACE (Rapid Amplification of the cDNA Ends) methodology in combination with in silica cloning based on the partial cDNA sequence obtained. Bioinformatics analysis showed FHL3 protein contained four LIM domains and four LIM zinc-binding domains. In silica mapping assigned this gene to the gene cluster MTF1-INPP5B-SF3A3-FHL3-CGI-94 on pig chromosome 6 where several QTL affecting intramuscular fat and eye muscle area had previously been identified. Transcription of the FHL3 gene was detected in spleen, liver, kidney, small intestine, skeletal muscle, fat and stomach, with the greatest expression in skeletal muscle. The A/G polymorphism in exon II was significantly associated with birth weight, average daily gain before weaning, drip loss rate, water holding capacity and intramuscular fat in a Landrace-derived pig population. Together, the present study provided the useful information for further studies to determine the roles of FHL3 gene in the regulation of skeletal muscle cell growth and differentiation in pigs. -
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 -
Clinical Utility of Recently Identified Diagnostic, Prognostic, And
Modern Pathology (2017) 30, 1338–1366 1338 © 2017 USCAP, Inc All rights reserved 0893-3952/17 $32.00 Clinical utility of recently identified diagnostic, prognostic, and predictive molecular biomarkers in mature B-cell neoplasms Arantza Onaindia1, L Jeffrey Medeiros2 and Keyur P Patel2 1Instituto de Investigacion Marques de Valdecilla (IDIVAL)/Hospital Universitario Marques de Valdecilla, Santander, Spain and 2Department of Hematopathology, MD Anderson Cancer Center, Houston, TX, USA Genomic profiling studies have provided new insights into the pathogenesis of mature B-cell neoplasms and have identified markers with prognostic impact. Recurrent mutations in tumor-suppressor genes (TP53, BIRC3, ATM), and common signaling pathways, such as the B-cell receptor (CD79A, CD79B, CARD11, TCF3, ID3), Toll- like receptor (MYD88), NOTCH (NOTCH1/2), nuclear factor-κB, and mitogen activated kinase signaling, have been identified in B-cell neoplasms. Chronic lymphocytic leukemia/small lymphocytic lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, Burkitt lymphoma, Waldenström macroglobulinemia, hairy cell leukemia, and marginal zone lymphomas of splenic, nodal, and extranodal types represent examples of B-cell neoplasms in which novel molecular biomarkers have been discovered in recent years. In addition, ongoing retrospective correlative and prospective outcome studies have resulted in an enhanced understanding of the clinical utility of novel biomarkers. This progress is reflected in the 2016 update of the World Health Organization classification of lymphoid neoplasms, which lists as many as 41 mature B-cell neoplasms (including provisional categories). Consequently, molecular genetic studies are increasingly being applied for the clinical workup of many of these neoplasms. In this review, we focus on the diagnostic, prognostic, and/or therapeutic utility of molecular biomarkers in mature B-cell neoplasms. -
Dynamic Transcriptomic Profiles of Zebrafish Gills in Response to Zinc
Zheng et al. BMC Genomics 2010, 11:548 http://www.biomedcentral.com/1471-2164/11/548 RESEARCH ARTICLE Open Access Dynamic transcriptomic profiles of zebrafish gills in response to zinc depletion Dongling Zheng1,4, Peter Kille2, Graham P Feeney2, Phil Cunningham1, Richard D Handy3, Christer Hogstrand1* Abstract Background: Zinc deficiency is detrimental to organisms, highlighting its role as an essential micronutrient contributing to numerous biological processes. To investigate the underlying molecular events invoked by zinc depletion we performed a temporal analysis of transcriptome changes observed within the zebrafish gill. This tissue represents a model system for studying ion absorption across polarised epithelial cells as it provides a major pathway for fish to acquire zinc directly from water whilst sharing a conserved zinc transporting system with mammals. Results: Zebrafish were treated with either zinc-depleted (water = 2.61 μgL-1; diet = 26 mg kg-1) or zinc-adequate (water = 16.3 μgL-1; diet = 233 mg kg-1) conditions for two weeks. Gill samples were collected at five time points and transcriptome changes analysed in quintuplicate using a 16K oligonucleotide array. Of the genes represented the expression of a total of 333 transcripts showed differential regulation by zinc depletion (having a fold-change greater than 1.8 and an adjusted P-value less than 0.1, controlling for a 10% False Discovery Rate). Down-regulation was dominant at most time points and distinct sets of genes were regulated at different stages. Annotation enrichment analysis revealed that ‘Developmental Process’ was the most significantly overrepresented Biological Process GO term (P = 0.0006), involving 26% of all regulated genes. -
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