Characterizing Rare and Low-Frequency Height-Associated
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Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations
The American Journal of Pathology, Vol. 180, No. 6, June 2012 Copyright © 2012 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ajpath.2012.01.044 Tumorigenesis and Neoplastic Progression Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations John M. Krill-Burger,* Maureen A. Lyons,*† The annual incidence of renal cell carcinoma (RCC) has Lori A. Kelly,*† Christin M. Sciulli,*† increased steadily in the United States for the past three Patricia Petrosko,*† Uma R. Chandran,†‡ decades, with approximately 58,000 new cases diag- 1,2 Michael D. Kubal,§ Sheldon I. Bastacky,*† nosed in 2010, representing 3% of all malignancies. Anil V. Parwani,*†‡ Rajiv Dhir,*†‡ and Treatment of RCC is complicated by the fact that it is not a single disease but composes multiple tumor types with William A. LaFramboise*†‡ different morphological characteristics, clinical courses, From the Departments of Pathology* and Biomedical and outcomes (ie, clear-cell carcinoma, 82% of RCC ‡ Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania; cases; type 1 or 2 papillary tumors, 11% of RCC cases; † the University of Pittsburgh Cancer Institute, Pittsburgh, chromophobe tumors, 5% of RCC cases; and collecting § Pennsylvania; and Life Technologies, Carlsbad, California duct carcinoma, approximately 1% of RCC cases).2,3 Benign renal neoplasms are subdivided into papillary adenoma, renal oncocytoma, and metanephric ade- Copy number variant (CNV) analysis was performed on noma.2,3 Treatment of RCC often involves surgical resec- renal cell carcinoma (RCC) specimens (chromophobe, tion of a large renal tissue component or removal of the clear cell, oncocytoma, papillary type 1, and papillary entire affected kidney because of the relatively large size of type 2) using high-resolution arrays (1.85 million renal tumors on discovery and the availability of a life-sus- probes). -
(HABP4): a Candidate Tumor Suppressor in Colorectal Cancer
www.oncotarget.com Oncotarget, 2020, Vol. 11, (No. 46), pp: 4325-4337 Research Paper Intracellular hyaluronic acid-binding protein 4 (HABP4): a candidate tumor suppressor in colorectal cancer Talita Diniz Melo-Hanchuk2,*, Carolina Colleti1,*, Ângela Saito3,*, Maria Carolina Santos Mendes4, José Barreto Campello Carvalheira4, Jose Vassallo5 and Jörg Kobarg1 1School of Pharmaceutical Sciences, State University of Campinas (UNICAMP), Campinas, SP, Brazil 2Department of Biochemistry and Tissue Biology, Institute of Biology, State University of Campinas (UNICAMP), Campinas, SP, Brazil 3Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP, Brazil 4Division of Oncology, Department of Internal Medicine, Faculty of Medical Sciences, State University of Campinas (UNICAMP), Campinas, SP, Brazil 5Laboratory of Investigative Pathology, CIPED, Faculty of Medical Sciences, State University of Campinas, Campinas, SP, Brazil *These authors contributed equally to this work Correspondence to: Jörg Kobarg, email: [email protected] Keywords: colon cancer; regulatory protein; gene knockout; proliferation; tumor marker Received: September 08, 2020 Accepted: October 27, 2020 Published: November 17, 2020 Copyright: © 2020 Melo-Hanchuk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Hyaluronic Acid-binding protein 4 (HABP4) is a regulatory protein of 57 kDa that is functionally involved in transcription regulation and RNA metabolism and shows several characteristics common to oncoproteins or tumor suppressors, including altered expression in cancer tissues, nucleus/cytoplasm shuttling, intrinsic lack of protein structure, complex interactomes and post translational modifications. -
Comparative Transcriptome Profiling of the Human and Mouse Dorsal Root Ganglia: an RNA-Seq-Based Resource for Pain and Sensory Neuroscience Research
bioRxiv preprint doi: https://doi.org/10.1101/165431; this version posted October 13, 2017. 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. Title: Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research Short Title: Human and mouse DRG comparative transcriptomics Pradipta Ray 1, 2 #, Andrew Torck 1 , Lilyana Quigley 1, Andi Wangzhou 1, Matthew Neiman 1, Chandranshu Rao 1, Tiffany Lam 1, Ji-Young Kim 1, Tae Hoon Kim 2, Michael Q. Zhang 2, Gregory Dussor 1 and Theodore J. Price 1, # 1 The University of Texas at Dallas, School of Behavioral and Brain Sciences 2 The University of Texas at Dallas, Department of Biological Sciences # Corresponding authors Theodore J Price Pradipta Ray School of Behavioral and Brain Sciences School of Behavioral and Brain Sciences The University of Texas at Dallas The University of Texas at Dallas BSB 14.102G BSB 10.608 800 W Campbell Rd 800 W Campbell Rd Richardson TX 75080 Richardson TX 75080 972-883-4311 972-883-7262 [email protected] [email protected] Number of pages: 27 Number of figures: 9 Number of tables: 8 Supplementary Figures: 4 Supplementary Files: 6 Word count: Abstract = 219; Introduction = 457; Discussion = 1094 Conflict of interest: The authors declare no conflicts of interest Patient anonymity and informed consent: Informed consent for human tissue sources were obtained by Anabios, Inc. (San Diego, CA). Human studies: This work was approved by The University of Texas at Dallas Institutional Review Board (MR 15-237). -
Drosophila Melanogaster Y Chromosome Genes Affect Male Sensitivity to Microbial Infections
insects Article Drosophila melanogaster Y Chromosome Genes Affect Male Sensitivity to Microbial Infections Gloria Bartolo, Leandra O. Gonzalez , Anastasia Levitin and Mikhail Martchenko Shilman * Henry E. Riggs School of Applied Life Sciences, Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA; [email protected] (G.B.); [email protected] (L.O.G.); [email protected] (A.L.) * Correspondence: [email protected] Simple Summary: This manuscript focuses on the protein-encoding genes of the Drosophila melanogaster Y chromosome and their role in immunity. Since their discovery, these genes have mainly been studied for their role(s) in male fertility, such as spermatogenesis. Two previously published papers have linked the polymorphism within the Y chromosome and immunity. Currently, there is no research article to our knowledge that has studied the effect of the individual genes of the Y chromosome on fly immunity. Here, we aim to address the lack of immunity-related knowledge of Y chromosome genes by determining the effect of many of these genes on male sensitivity to microbial infections. We challenged loss-of-function gene mutants with bacterial and fungal pathogens orally and observed any significant fly survival changes. We discovered several genes affecting male sensitivity to both bacterial and fungal infections. While most of the Y chromosome genes were found to reduce sensitivity, we found one gene increases sensitivity to fungal infection. Because several genes were found to increase male sensitivity to microbial infections, inhibitors for such genes could be introduced in areas where fruit flies are pests. Amid declining bee populations, Diptera pollinators could be protected from fungal infections with Y-gene inhibitors. -
Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations from Genome-Wide Data Calvin Mccarter
May 23, 2019 DRAFT Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter May 2019 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Seyoung Kim, Chair Pradeep Ravikumar Kathryn Roeder Dietrich Stephan (NeuBase Therapeutics, previously The University of Pittsburgh) Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright c 2019 Calvin McCarter May 23, 2019 DRAFT Keywords: probabilistic graphical models, sparse learning, structure learning, convex opti- mization, genomics, gene networks, systems biology May 23, 2019 DRAFT When thou shalt do wonderful things, we shall not bear them: thou didst come down, and at thy presence the mountains melted away. From the beginning of the world they have not heard, nor perceived with the ears: the eye hath not seen, O God, besides thee, what things thou hast prepared for them that wait for thee. May 23, 2019 DRAFT iv May 23, 2019 DRAFT Abstract Recent technologies are generating an abundance of genome sequence data and molecular and clinical phenotype data, providing an opportunity to understand the genetic architecture and molecular mechanisms underlying diseases. Previous ap- proaches have largely focused on the co-localization of single-nucleotide polymor- phisms (SNPs) associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus (eQTL) map- ping, and thus have provided only limited capabilities for uncovering the molecular mechanisms behind the SNPs influencing clinical phenotypes. Here we aim to ex- tract rich information on the functional role of trait-perturbing SNPs that goes far beyond this simple co-localization. -
Robles JTO Supplemental Digital Content 1
Supplementary Materials An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma based on mRNA, microRNA and DNA Methylation Biomarkers Ana I. Robles1, Eri Arai2, Ewy A. Mathé1, Hirokazu Okayama1, Aaron Schetter1, Derek Brown1, David Petersen3, Elise D. Bowman1, Rintaro Noro1, Judith A. Welsh1, Daniel C. Edelman3, Holly S. Stevenson3, Yonghong Wang3, Naoto Tsuchiya4, Takashi Kohno4, Vidar Skaug5, Steen Mollerup5, Aage Haugen5, Paul S. Meltzer3, Jun Yokota6, Yae Kanai2 and Curtis C. Harris1 Affiliations: 1Laboratory of Human Carcinogenesis, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 2Division of Molecular Pathology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 3Genetics Branch, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 4Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 5Department of Chemical and Biological Working Environment, National Institute of Occupational Health, NO-0033 Oslo, Norway. 6Genomics and Epigenomics of Cancer Prediction Program, Institute of Predictive and Personalized Medicine of Cancer (IMPPC), 08916 Badalona (Barcelona), Spain. List of Supplementary Materials Supplementary Materials and Methods Fig. S1. Hierarchical clustering of based on CpG sites differentially-methylated in Stage I ADC compared to non-tumor adjacent tissues. Fig. S2. Confirmatory pyrosequencing analysis of DNA methylation at the HOXA9 locus in Stage I ADC from a subset of the NCI microarray cohort. 1 Fig. S3. Methylation Beta-values for HOXA9 probe cg26521404 in Stage I ADC samples from Japan. Fig. S4. Kaplan-Meier analysis of HOXA9 promoter methylation in a published cohort of Stage I lung ADC (J Clin Oncol 2013;31(32):4140-7). Fig. S5. Kaplan-Meier analysis of a combined prognostic biomarker in Stage I lung ADC. -
W J B C Biological Chemistry
World Journal of W J B C Biological Chemistry Submit a Manuscript: https://www.f6publishing.com World J Biol Chem 2019 November 21; 10(3): 44-64 DOI: 10.4331/wjbc.v10.i3.44 ISSN 1949-8454 (online) REVIEW Complex interactomes and post-translational modifications of the regulatory proteins HABP4 and SERBP1 suggest pleiotropic cellular functions Carolina Colleti, Talita Diniz Melo-Hanchuk, Flávia Regina Moraes da Silva, Ângela Saito, Jörg Kobarg ORCID number: Carolina Colleti Carolina Colleti, Flávia Regina Moraes da Silva, Jörg Kobarg, Faculty of Pharmaceutical (0000-0001-7626-5601); Talita Diniz Sciences, University of Campinas, Campinas 13083-871, Brazil Melo-Hanchuk (0000-0001-7268-6687); Flávia Carolina Colleti, Talita Diniz Melo-Hanchuk, Flávia Regina Moraes da Silva, Jörg Kobarg, Institute Moraes da Silva of Biology, Departament of Biochemistry and Tissue Biology, University of Campinas, (0000-0003-0721-6843); Ângela Saito Campinas 13083-862, Brazil (0000-0003-4421-9226); Jörg Kobarg (0000-0002-9419-0145). Ângela Saito, Laboratório Nacional de Biociências, CNPEM, Campinas 13083-970, Brazil Author contributions: Colleti C, Corresponding author: Jörg Kobarg, PhD, Full Professor, Faculty of Pharmaceutical Sciences, Melo-Hanchuk TD, da Silva FRM, University of Campinas, Rua Monteiro Lobato 255, Bloco F, Sala 3, Campinas 13083-862, Saito  and Kobarg J performed Brazil. [email protected] the literature search, analyzed and Telephone: +55-19-35211443 interpreted the data, generated the figures, contributed different sections of the manuscript and collated the final version of the text together. All authors read, revised Abstract and approved the final version. The 57 kDa antigen recognized by the Ki-1 antibody, is also known as intracellular hyaluronic acid binding protein 4 and shares 40.7% identity and Supported by the “Conselho Nacional de Desenvolvimento 67.4% similarity with serpin mRNA binding protein 1, which is also named CGI- Cientifico e Tecnológico”, Grant 55, or plasminogen activator inhibitor type-1-RNA binding protein-1, indicating No. -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19 -
Predict AID Targeting in Non-Ig Genes Multiple Transcription Factor
Downloaded from http://www.jimmunol.org/ by guest on September 26, 2021 is online at: average * The Journal of Immunology published online 20 March 2013 from submission to initial decision 4 weeks from acceptance to publication Multiple Transcription Factor Binding Sites Predict AID Targeting in Non-Ig Genes Jamie L. Duke, Man Liu, Gur Yaari, Ashraf M. Khalil, Mary M. Tomayko, Mark J. Shlomchik, David G. Schatz and Steven H. Kleinstein J Immunol http://www.jimmunol.org/content/early/2013/03/20/jimmun ol.1202547 Submit online. Every submission reviewed by practicing scientists ? is published twice each month by http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://www.jimmunol.org/content/suppl/2013/03/20/jimmunol.120254 7.DC1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 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 26, 2021. Published March 20, 2013, doi:10.4049/jimmunol.1202547 The Journal of Immunology Multiple Transcription Factor Binding Sites Predict AID Targeting in Non-Ig Genes Jamie L. Duke,* Man Liu,†,1 Gur Yaari,‡ Ashraf M. Khalil,x Mary M. Tomayko,{ Mark J. Shlomchik,†,x David G. -
Supplemental Data
Supplementary Table 1. Gene sets from Figure 6. Lists of genes from each individual gene set defined in Figure 6, including the fold-change in expression of each gene in treatment group pair-wise comparisons. ENSEMBL: Ensembl gene identifier; Symbol: official gene symbol; logFC: log fold change; p value: significance of fold-change in a pair-wise comparison, P<0.05 cut-off; FDR: false discovery rate, expected proportion of false positives among the differentially expressed genes in a pair-wise comparison (FDR<0.25 cut-off). Sup. Table 1 SET I CP versus Sal CP versus CP+DCA DCA versus Sal ENSEMBL Symbol logFC PValue FDR logFC PValue FDR logFC PValue FDR Desc ENSMUSG00000020326 Ccng1 2.64 0.00 0.00 -0.06 0.13 0.96 0.40 0.00 0.23 cyclin G1 [Source:MGI Symbol;Acc:MGI:102890] ENSMUSG00000031886 Ces2e 3.97 0.00 0.00 -0.24 0.02 0.28 0.01 1.00 1.00 carboxylesterase 2E [Source:MGI Symbol;Acc:MGI:2443170] ENSMUSG00000041959 S100a10 2.31 0.00 0.00 -0.21 0.02 0.23 -0.11 0.53 1.00 S100 calcium binding protein A10 (calpactin) [Source:MGI Symbol;Acc:MGI:1339468] ENSMUSG00000092341 Malat1 1.09 0.00 0.00 -0.11 0.20 1.00 0.66 0.00 0.00 metastasis associated lung adenocarcinoma transcript 1 (non-coding RNA) [Source:MGI Symbol;Acc:MGI:1919539] ENSMUSG00000072949 Acot1 1.73 0.00 0.00 -0.22 0.01 0.12 -0.44 0.01 1.00 acyl-CoA thioesterase 1 [Source:MGI Symbol;Acc:MGI:1349396] ENSMUSG00000064339 mt-Rnr2 1.09 0.00 0.00 -0.08 0.17 1.00 0.67 0.00 0.07 mitochondrially encoded 16S rRNA [Source:MGI Symbol;Acc:MGI:102492] ENSMUSG00000025934 Gsta3 1.86 0.00 0.00 -0.28 -
466151-1.Pdf
1 1 Methods 1.1 Data Collection Human (9606) Salmonella (590) Uniprot taxon identifier mode: normal mode: recursive Uniprot (Swiss-Prot) normal recursive proteom (taxon identifier) QuickGO QuickGO, Uniprot (Taxonomy) RNA-binding annotation (taxon identifier) RNA-binding annotation proteins with length (all taxa which are hierarchically > 6000 or < 50 arranged to the specified taxon) Uniprot (Keywords) proteins from RNA associated CD-HIT (Li et al., 2001) keyword list (Cai and Lin et al., 2001) proteins with sequence such as RNA-, DNA- or similarity higher than 90 % Nucleotide-binding QuickGO positive data set RNA, DNA or nucleotide- binding annotation 1812 306 Pfam (526 profile HMMs) proteins containing RNA associated Pfam domains databases/applications CD-HIT (Li et al., 2001) collected proteins proteins with sequence removed proteins similarity higher than 90 % negative data set 12038 1415 Figure S1: Pipeline to collect RBPs (positive data) and non-RBPs (negative data). The fully automated pipeline requires the Uniprot identification number of a taxon to collect positive and negative data sets. The data sets form the foundation to train and validate the TriPepSVM classifier. The pipeline supports a recursive mode to collect positive data from all members of the specified taxon. In the figure we see the results for human (9606, normal mode) and Salmonella (590, recursive mode). Red boxes in the workflow correspond to filtering operations on the input sequences, while green boxes correspond to collection steps on the input sequences. 2 1.2 Parameter Tuning n data set m > n: class imbalance m test set n/10 s = 9n / 10 training set (performance) m/10 q = 9m / 10 KeBABS-package in R 10 - fold cross - validation 10 - fold 9s / 10 validation set s / 10 training set (model selection) q / 10 9q / 10 build models based on different parameter estimate error combinations parameter combination k with highest balanced accuracy cost Model selection W+ W- Figure S2: Data splitting and parameter tuning.