Table S3 - List of 489 Non-Synonymous and Frame-Shifting Variants Which Were Predicted to Be Damaging (X: Termination Codon)
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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Cooperativity of Imprinted Genes Inactivated by Acquired Chromosome 20Q Deletions
Cooperativity of imprinted genes inactivated by acquired chromosome 20q deletions Athar Aziz, … , Anne C. Ferguson-Smith, Anthony R. Green J Clin Invest. 2013;123(5):2169-2182. https://doi.org/10.1172/JCI66113. Research Article Oncology Large regions of recurrent genomic loss are common in cancers; however, with a few well-characterized exceptions, how they contribute to tumor pathogenesis remains largely obscure. Here we identified primate-restricted imprinting of a gene cluster on chromosome 20 in the region commonly deleted in chronic myeloid malignancies. We showed that a single heterozygous 20q deletion consistently resulted in the complete loss of expression of the imprinted genes L3MBTL1 and SGK2, indicative of a pathogenetic role for loss of the active paternally inherited locus. Concomitant loss of both L3MBTL1 and SGK2 dysregulated erythropoiesis and megakaryopoiesis, 2 lineages commonly affected in chronic myeloid malignancies, with distinct consequences in each lineage. We demonstrated that L3MBTL1 and SGK2 collaborated in the transcriptional regulation of MYC by influencing different aspects of chromatin structure. L3MBTL1 is known to regulate nucleosomal compaction, and we here showed that SGK2 inactivated BRG1, a key ATP-dependent helicase within the SWI/SNF complex that regulates nucleosomal positioning. These results demonstrate a link between an imprinted gene cluster and malignancy, reveal a new pathogenetic mechanism associated with acquired regions of genomic loss, and underline the complex molecular and cellular consequences of “simple” cancer-associated chromosome deletions. Find the latest version: https://jci.me/66113/pdf Research article Cooperativity of imprinted genes inactivated by acquired chromosome 20q deletions Athar Aziz,1,2 E. Joanna Baxter,1,2,3 Carol Edwards,4 Clara Yujing Cheong,5 Mitsuteru Ito,4 Anthony Bench,3 Rebecca Kelley,1,2 Yvonne Silber,1,2 Philip A. -
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. -
MUC4/MUC16/Muc20high Signature As a Marker of Poor Prognostic for Pancreatic, Colon and Stomach Cancers
Jonckheere and Van Seuningen J Transl Med (2018) 16:259 https://doi.org/10.1186/s12967-018-1632-2 Journal of Translational Medicine RESEARCH Open Access Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large‑scale genomic databases: MUC4/MUC16/ MUC20 signature is associated with poor survival in human carcinomas Nicolas Jonckheere* and Isabelle Van Seuningen* Abstract Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). Results: We identifed 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signal- ing. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with diferent localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adeno- carcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically signifcant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer. -
Genome-Wide Parent-Of-Origin DNA Methylation Analysis Reveals The
Downloaded from genome.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press Research Genome-wide parent-of-origin DNA methylation analysis reveals the intricacies of human imprinting and suggests a germline methylation-independent mechanism of establishment Franck Court,1,15 Chiharu Tayama,2,15 Valeria Romanelli,1,15 Alex Martin-Trujillo,1,15 Isabel Iglesias-Platas,3 Kohji Okamura,4 Naoko Sugahara,2 Carlos Simo´n,5 Harry Moore,6 Julie V. Harness,7 Hans Keirstead,7 Jose Vicente Sanchez-Mut,8 Eisuke Kaneki,9 Pablo Lapunzina,10 Hidenobu Soejima,11 Norio Wake,9 Manel Esteller,8,12,13 Tsutomu Ogata,14 Kenichiro Hata,2 Kazuhiko Nakabayashi,2,16,17 and David Monk1,16,17 1–14[Author affiliations appear at the end of the paper.] Differential methylation between the two alleles of a gene has been observed in imprinted regions, where the methylation of one allele occurs on a parent-of-origin basis, the inactive X-chromosome in females, and at those loci whose methylation is driven by genetic variants. We have extensively characterized imprinted methylation in a substantial range of normal human tissues, reciprocal genome-wide uniparental disomies, and hydatidiform moles, using a combination of whole- genome bisulfite sequencing and high-density methylation microarrays. This approach allowed us to define methylation profiles at known imprinted domains at base-pair resolution, as well as to identify 21 novel loci harboring parent-of-origin methylation, 15 of which are restricted to the placenta. We observe that the extent of imprinted differentially methylated regions (DMRs) is extremely similar between tissues, with the exception of the placenta. -
Transcriptional Control of Tissue-Resident Memory T Cell Generation
Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells. -
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Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes. -
Investigation of Candidate Genes and Mechanisms Underlying Obesity
Prashanth et al. BMC Endocrine Disorders (2021) 21:80 https://doi.org/10.1186/s12902-021-00718-5 RESEARCH ARTICLE Open Access Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules G. Prashanth1 , Basavaraj Vastrad2 , Anandkumar Tengli3 , Chanabasayya Vastrad4* and Iranna Kotturshetti5 Abstract Background: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. Methods: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. Results: A total of 820 DEGs were identified between -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Imprinting of the Human L3MBTL Gene, a Polycomb Family Member Located in a Region of Chromosome 20 Deleted in Human Myeloid Malignancies
Correction GENETICS Correction for “Imprinting of the human L3MBTL gene, a poly- The authors note that a mistake occurred during the prepa- comb family member located in a region of chromosome 20 deleted ration of Fig. 1B. Three control lanes (6, 7, and 8) were inad- in human myeloid malignancies,” by Juan Li, Anthony J. Bench, vertently duplicated and also shown as lanes 3, 4, and 5. This George S. Vassiliou, Nasios Fourouclas, Anne C. Ferguson-Smith, resulted from a technical error during preparation of the figure and Anthony R. Green, first published April 30, 2004; 10.1073/ and does not alter any of the conclusions of the paper. The pnas.0308195101 (Proc. Natl. Acad. Sci. U.S.A. 101, 7341–7346). corrected figure and the unaltered figure legend appear below. A CORRECTION B C Fig. 1. Allelic methylation of the L3MBTL gene. (A) Locus of the L3MBTL gene and intron/exon structure of its 5′ and 3′ regions. Coding and noncoding exons are shown in black and gray boxes, respectively. Bent arrows indicate two putative promoters. Patterned horizontal bars represent the four CpG islands. Asterisks indicate the locations of SNPs used in the study, and primers spanning the respective SNPs are indicated (P1–P8). (B) Methylation analysis of the CpG island 3 by using SSCP. Lane 1, universally methylated genomic control DNA; lanes 2–5, normal bone marrow; lanes 6–9, normal granulocytes; lanes 10–13, granulocytes from patients, with an acquired 20q deletion. (C) Bisulfite sequencing of CpG island 3. Sequences relate to samples corresponding to individual lanes from B, as indicated. -
The Role of Regulated Necrosis in Endocrine Diseases
PERSPECTIVES system results in the typical morphological features such as rapid shrinking of the cell, The role of regulated necrosis nuclear condensation, DNA fragmentation, exposure of phosphatidylserine and a in endocrine diseases process known as membrane blebbing11,12. Phosphatidylserine exposure functions Wulf Tonnus , Alexia Belavgeni , Felix Beuschlein , Graeme Eisenhofer, as an ‘eat me’ signal to macrophages13–15. Martin Fassnacht , Matthias Kroiss , Nils P. Krone, Martin Reincke , Importantly, the plasma membrane remains intact in apoptotically dying cells, Stefan R. Bornstein and Andreas Linkermann a mechanism that prevents the release of Abstract | The death of endocrine cells is involved in type 1 diabetes mellitus, intracellular content to the interstitial and/or autoimmunity, adrenopause and hypogonadotropism. Insights from research on extracellular space. Therefore, apoptosis is immunologically silent. The detection basic cell death have revealed that most pathophysiologically important cell death of apoptosis has been misinterpreted for is necrotic in nature, whereas regular metabolism is maintained by apoptosis decades by the TdT-mediated dUTP-biotin programmes. Necrosis is defined as cell death by plasma membrane rupture, which nick end- labelling (TUNEL) method (BOx 1). allows the release of damage- associated molecular patterns that trigger an Mechanistically, extrinsic apoptosis immune response referred to as necroinflammation. Regulated necrosis comes in is mediated by death receptors such as different forms, such as necroptosis, pyroptosis and ferroptosis. In this Perspective, tumour necrosis factor receptor 1 (TNFR1) or the FAS receptor (also known as with a focus on the endocrine environment, we introduce these cell death CD95)16. To kill a cell through a TNFR1 pathways and discuss the specific consequences of regulated necrosis. -
Relating Metatranscriptomic Profiles to the Micropollutant
1 Relating Metatranscriptomic Profiles to the 2 Micropollutant Biotransformation Potential of 3 Complex Microbial Communities 4 5 Supporting Information 6 7 Stefan Achermann,1,2 Cresten B. Mansfeldt,1 Marcel Müller,1,3 David R. Johnson,1 Kathrin 8 Fenner*,1,2,4 9 1Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, 10 Switzerland. 2Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 11 Zürich, Switzerland. 3Institute of Atmospheric and Climate Science, ETH Zürich, 8092 12 Zürich, Switzerland. 4Department of Chemistry, University of Zürich, 8057 Zürich, 13 Switzerland. 14 *Corresponding author (email: [email protected] ) 15 S.A and C.B.M contributed equally to this work. 16 17 18 19 20 21 This supporting information (SI) is organized in 4 sections (S1-S4) with a total of 10 pages and 22 comprises 7 figures (Figure S1-S7) and 4 tables (Table S1-S4). 23 24 25 S1 26 S1 Data normalization 27 28 29 30 Figure S1. Relative fractions of gene transcripts originating from eukaryotes and bacteria. 31 32 33 Table S1. Relative standard deviation (RSD) for commonly used reference genes across all 34 samples (n=12). EC number mean fraction bacteria (%) RSD (%) RSD bacteria (%) RSD eukaryotes (%) 2.7.7.6 (RNAP) 80 16 6 nda 5.99.1.2 (DNA topoisomerase) 90 11 9 nda 5.99.1.3 (DNA gyrase) 92 16 10 nda 1.2.1.12 (GAPDH) 37 39 6 32 35 and indicates not determined. 36 37 38 39 S2 40 S2 Nitrile hydration 41 42 43 44 Figure S2: Pearson correlation coefficients r for rate constants of bromoxynil and acetamiprid with 45 gene transcripts of ECs describing nucleophilic reactions of water with nitriles.