HBEGF, SRA1, and IK: Three Cosegregating Genes As Determinants of Cardiomyopathy
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Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
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. -
Long-Read Cdna Sequencing Identifies Functional Pseudogenes in the Human Transcriptome Robin-Lee Troskie1, Yohaann Jafrani1, Tim R
Troskie et al. Genome Biology (2021) 22:146 https://doi.org/10.1186/s13059-021-02369-0 SHORT REPORT Open Access Long-read cDNA sequencing identifies functional pseudogenes in the human transcriptome Robin-Lee Troskie1, Yohaann Jafrani1, Tim R. Mercer2, Adam D. Ewing1*, Geoffrey J. Faulkner1,3* and Seth W. Cheetham1* * Correspondence: adam.ewing@ mater.uq.edu.au; faulknergj@gmail. Abstract com; [email protected]. au Pseudogenes are gene copies presumed to mainly be functionless relics of evolution 1Mater Research Institute-University due to acquired deleterious mutations or transcriptional silencing. Using deep full- of Queensland, TRI Building, QLD length PacBio cDNA sequencing of normal human tissues and cancer cell lines, we 4102 Woolloongabba, Australia Full list of author information is identify here hundreds of novel transcribed pseudogenes expressed in tissue-specific available at the end of the article patterns. Some pseudogene transcripts have intact open reading frames and are translated in cultured cells, representing unannotated protein-coding genes. To assess the biological impact of noncoding pseudogenes, we CRISPR-Cas9 delete the nucleus-enriched pseudogene PDCL3P4 and observe hundreds of perturbed genes. This study highlights pseudogenes as a complex and dynamic component of the human transcriptional landscape. Keywords: Pseudogene, PacBio, Long-read, lncRNA, CRISPR Background Pseudogenes are gene copies which are thought to be defective due to frame- disrupting mutations or transcriptional silencing [1, 2]. Most human pseudogenes (72%) are derived from retrotransposition of processed mRNAs, mediated by proteins encoded by the LINE-1 retrotransposon [3, 4]. Due to the loss of parental cis-regula- tory elements, processed pseudogenes were initially presumed to be transcriptionally silent [1] and were excluded from genome-wide functional screens and most transcrip- tome analyses [2]. -
Hippo and Sonic Hedgehog Signalling Pathway Modulation of Human Urothelial Tissue Homeostasis
Hippo and Sonic Hedgehog signalling pathway modulation of human urothelial tissue homeostasis Thomas Crighton PhD University of York Department of Biology November 2020 Abstract The urinary tract is lined by a barrier-forming, mitotically-quiescent urothelium, which retains the ability to regenerate following injury. Regulation of tissue homeostasis by Hippo and Sonic Hedgehog signalling has previously been implicated in various mammalian epithelia, but limited evidence exists as to their role in adult human urothelial physiology. Focussing on the Hippo pathway, the aims of this thesis were to characterise expression of said pathways in urothelium, determine what role the pathways have in regulating urothelial phenotype, and investigate whether the pathways are implicated in muscle-invasive bladder cancer (MIBC). These aims were assessed using a cell culture paradigm of Normal Human Urothelial (NHU) cells that can be manipulated in vitro to represent different differentiated phenotypes, alongside MIBC cell lines and The Cancer Genome Atlas resource. Transcriptomic analysis of NHU cells identified a significant induction of VGLL1, a poorly understood regulator of Hippo signalling, in differentiated cells. Activation of upstream transcription factors PPARγ and GATA3 and/or blockade of active EGFR/RAS/RAF/MEK/ERK signalling were identified as mechanisms which induce VGLL1 expression in NHU cells. Ectopic overexpression of VGLL1 in undifferentiated NHU cells and MIBC cell line T24 resulted in significantly reduced proliferation. Conversely, knockdown of VGLL1 in differentiated NHU cells significantly reduced barrier tightness in an unwounded state, while inhibiting regeneration and increasing cell cycle activation in scratch-wounded cultures. A signalling pathway previously observed to be inhibited by VGLL1 function, YAP/TAZ, was unaffected by VGLL1 manipulation. -
Manuscript Submission Manuscript Draft Manuscript Number: BMB-20-087 Title: Emerging Functions for ANKHD1 in Cance
BMB Reports - Manuscript Submission Manuscript Draft Manuscript Number : BMB-20-087 Title : Emerging functions for ANKHD1 in cancer-related signaling pathways and cellular processes Article Type : Mini Review Keywords : Ankyrin repeat and KH domain containing 1; ANKHD1; JAK/STAT; YAP1; Cancer Corresponding Author : João Agostinho Machado-Neto Auth ors : Bruna Oliveira de Almeida 1, João Agostinho Machado-Neto 1,* Institution : 1Department of Pharmacology, Biomedical Sciences Institute, University of São Paulo, São Paulo, Brazil, UNCORRECTED PROOF Mini Review Emerging functions for ANKHD1 in cancer-related signaling pathways and cellular processes Bruna Oliveira de Almeida, João Agostinho Machado-Neto Department of Pharmacology, Biomedical Sciences Institute, University of São Paulo, São Paulo, Brazil Running title: ANKHD1, a multitask protein, in cancer cells Key words: Ankyrin repeat and KH domain containing 1; ANKHD1; JAK/STAT; YAP1; Cancer. Corresponding Author: João Agostinho Machado Neto, PhD Department of Pharmacology Institute of Biomedical Sciences of University of São Paulo Av. Prof. Lineu Prestes, 1524, CEP 05508-900, São Paulo, SP Phone: 55-11- 3091-7218; Fax: 55-11-3091-7322 E-mail: [email protected] UNCORRECTED PROOF 1 Abstract ANKHD1 (ankyrin repeat and KH domain containing 1) is a large protein characterized by the presence of multiple ankyrin repeats and a K-homology domain. Ankyrin repeat domains consist of widely existing protein motifs in nature, they mediate protein- protein interactions and regulate fundamental biological processes, while the KH domain binds to RNA or ssDNA and is associated with transcriptional and translational regulation. In recent years, studies containing relevant information on ANKHD1 in cancer biology and its clinical relevance, as well as the increasing complexity of signaling networks in which this protein acts, have been reported. -
Linkage and Association Analysis of Obesity Traits Reveals Novel Loci and Interactions with Dietary N-3 Fatty Acids in an Alaska Native (Yup’Ik) Population
METABOLISM CLINICAL AND EXPERIMENTAL XX (2015) XXX– XXX Available online at www.sciencedirect.com Metabolism www.metabolismjournal.com Linkage and association analysis of obesity traits reveals novel loci and interactions with dietary n-3 fatty acids in an Alaska Native (Yup’ik) population Laura Kelly Vaughan a, Howard W. Wiener b, Stella Aslibekyan b, David B. Allison c, Peter J. Havel d, Kimber L. Stanhope d, Diane M. O’Brien e, Scarlett E. Hopkins e, Dominick J. Lemas f, Bert B. Boyer e,⁎, Hemant K. Tiwari c a Department of Biology, King University, 1350 King College Rd, Bristol, TN 37620, USA b Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35294, USA c Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35294, USA d Departments of Nutrition and Molecular Biosciences, University of California at Davis, 1 Shields Ave, Davis, CA 95616, USA e USACenter for Alaska Native Health Research, Institute of Arctic Biology, 311 Irving I Building, University of Alaska Fairbanks, Fairbanks, AK 99775, USA f Department of Pediatrics, Section of Neonatology, University of Colorado Anschutz Medical Campus, 13123 East 16th Ave, Aurora, CO 80045, USA ARTICLE INFO ABSTRACT Article history: Objective. To identify novel genetic markers of obesity-related traits and to identify gene- Received 16 June 2014 diet interactions with n-3 polyunsaturated fatty acid (n-3 PUFA) intake in Yup’ik people. Accepted 28 February 2015 Material and methods. We measured body composition, plasma adipokines and ghrelin in 982 participants enrolled in the Center for Alaska Native Health Research (CANHR) Study. -
Gene and Alternative Splicing Annotation with AIR
Downloaded from genome.cshlp.org on May 8, 2012 - Published by Cold Spring Harbor Laboratory Press Gene and alternative splicing annotation with AIR Liliana Florea, Valentina Di Francesco, Jason Miller, et al. Genome Res. 2005 15: 54-66 Access the most recent version at doi:10.1101/gr.2889405 Supplemental http://genome.cshlp.org/content/suppl/2004/12/08/15.1.54.DC1.html Material References This article cites 49 articles, 34 of which can be accessed free at: http://genome.cshlp.org/content/15/1/54.full.html#ref-list-1 Article cited in: http://genome.cshlp.org/content/15/1/54.full.html#related-urls Creative This article is distributed exclusively by Cold Spring Harbor Laboratory Press Commons for the first six months after the full-issue publication date (see License http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/. Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions © 2005, Published by Cold Spring Harbor Laboratory Press Downloaded from genome.cshlp.org on May 8, 2012 - Published by Cold Spring Harbor Laboratory Press Methods Gene and alternative splicing annotation with AIR Liliana Florea,1,4,5 Valentina Di Francesco,2 Jason Miller,1 Russell Turner,1 Alison Yao,2 Michael Harris,2 Brian Walenz,1 Clark Mobarry,1 Gennady V. -
Spring 2003 Final3
NCBI News National Center for Biotechnology Information National Library of Medicine National Institutes of Health Department of Health and Human Services Spring 2003 [ A Field Guide to GenBank® and NCBI Resources: First Version of Human NCBI’s Scientific Outreach and Training Program Genome Reference Sequence Debuts on Biological sequence and structure use of NCBI databases and tools. DNA’s 50th information are now used in nearly The course, called “A Field Guide every field of biological research. A to GenBank and NCBI Resources”, working knowledge of these resources is designed especially for biologists April 14, 2003 marked the and standard computational biology who work at the bench or in the field 50th anniversary of the tools are an essential part of every but use sequence and structure data description of the structure biologist’s toolkit. However, keeping in their research. All researchers, of DNA and also saw the up with these databases and tools can educators and students who work release of the first version of be challenging in this period of rapidly with biological sequence and structure the 3 billion base pair refer- changing bioinformatics resources. data should find this to be a useful ence sequence of the human introduction and survey of the genome. Annotations to the In order to help researchers keep available NCBI tools and databases. raw sequence made public on April 14 abreast of enhancements and the Because of the rapid expansion of were released on April 29 when the increasing diversity of NCBI molecu- the resources, even experienced NCBI reference genome, NCBI build 33, lar biology resources, the NCBI users will likely learn something new appeared in the NCBI Map Viewer. -
A Literature-Based Database for Cell Senescence Genes and Its Application to Identify Critical Cell Aging Pathways and Associated Diseases
Citation: Cell Death and Disease (2016) 7, e2053; doi:10.1038/cddis.2015.414 OPEN & 2016 Macmillan Publishers Limited All rights reserved 2041-4889/16 www.nature.com/cddis CSGene: a literature-based database for cell senescence genes and its application to identify critical cell aging pathways and associated diseases M Zhao1, L Chen2 and H Qu*,2 Cell senescence is a cellular process in which normal diploid cells cease to replicate and is a major driving force for human cancers and aging-associated diseases. Recent studies on cell senescence have identified many new genetic components and pathways that control cell aging. However, there is no comprehensive resource for cell senescence that integrates various genetic studies and relationships with cell senescence, and the risk associated with complex diseases such as cancer is still unexplored. We have developed the first literature-based gene resource for exploring cell senescence genes, CSGene. We complied 504 experimentally verified genes from public data resources and published literature. Pathway analyses highlighted the prominent roles of cell senescence genes in the control of rRNA gene transcription and unusual rDNA repeat that constitute a center for the stability of the whole genome. We also found a strong association of cell senescence with HIV-1 infection and viral carcinogenesis that are mainly related to promoter/enhancer binding and chromatin modification processes. Moreover, pan-cancer mutation and network analysis also identified common cell aging mechanisms in cancers and uncovered a highly modular network structure. These results highlight the utility of CSGene for elucidating the complex cellular events of cell senescence. -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is. -
Navigo: Interactive Tool for Visualization and Functional Similarity and Coherence Analysis with Gene Ontology Qing Wei1, Ishita K
Wei et al. BMC Bioinformatics (2017) 18:177 DOI 10.1186/s12859-017-1600-5 SOFTWARE Open Access NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology Qing Wei1, Ishita K. Khan1, Ziyun Ding2, Satwica Yerneni3 and Daisuke Kihara2,1* Abstract Background: The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. Results: NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. Conclusions: We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo. Keywords: Gene function, Gene ontology, GO, Ontology, GO directed acyclic graph, Function similarity, Gene function prediction, GO annotation, function enrichment analysis, GO parental terms, GO association score Background their parental relationship make it non-trivial to provide Functional elucidation of genes is one of the central an intuitive summary of GO annotations of genes. -
The Human Protein Coevolution Network
Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Methods The human protein coevolution network Elisabeth R.M. Tillier1 and Robert L. Charlebois Department of Medical Biophysics, University of Toronto, and Ontario Cancer Institute, University Health Network, Toronto, Ontario M5G 1L7, Canada Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein–protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein–protein interactions than are currently known from high-throughput and other experimental evidence.