Mapping of Craniofacial Traits in Outbred Mice Identifies Major Developmental Genes Involved in Shape Determination
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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. -
DNA Breakpoint Assay Reveals a Majority of Gross Duplications Occur in Tandem Reducing VUS Classifications in Breast Cancer Predisposition Genes
© American College of Medical Genetics and Genomics ARTICLE Corrected: Correction DNA breakpoint assay reveals a majority of gross duplications occur in tandem reducing VUS classifications in breast cancer predisposition genes Marcy E. Richardson, PhD1, Hansook Chong, PhD1, Wenbo Mu, MS1, Blair R. Conner, MS1, Vickie Hsuan, MS1, Sara Willett, MS1, Stephanie Lam, MS1, Pei Tsai, CGMBS, MB (ASCP)1, Tina Pesaran, MS, CGC1, Adam C. Chamberlin, PhD1, Min-Sun Park, PhD1, Phillip Gray, PhD1, Rachid Karam, MD, PhD1 and Aaron Elliott, PhD1 Purpose: Gross duplications are ambiguous in terms of clinical cohort, while the remainder have unknown tandem status. Among interpretation due to the limitations of the detection methods that the tandem gross duplications that were eligible for reclassification, cannot infer their context, namely, whether they occur in tandem or 95% of them were upgraded to pathogenic. are duplicated and inserted elsewhere in the genome. We Conclusion: DBA is a novel, high-throughput, NGS-based method investigated the proportion of gross duplications occurring in that informs the tandem status, and thereby the classification of, tandem in breast cancer predisposition genes with the intent of gross duplications. This method revealed that most gross duplica- informing their classifications. tions in the investigated genes occurred in tandem and resulted in a Methods: The DNA breakpoint assay (DBA) is a custom, paired- pathogenic classification, which helps to secure the necessary end, next-generation sequencing (NGS) method designed to treatment options for their carriers. capture and detect deep-intronic DNA breakpoints in gross duplications in BRCA1, BRCA2, ATM, CDH1, PALB2, and CHEK2. Genetics in Medicine (2019) 21:683–693; https://doi.org/10.1038/s41436- Results: DBA allowed us to ascertain breakpoints for 44 unique 018-0092-7 gross duplications from 147 probands. -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
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. -
Properties of Human Genes Guided by Their Enrichment in Rare and Common Variants
Properties of human genes guided by their enrichment in rare and common variants Authors: Eman Alhuzimi, Luis G. Leal, Michael J.E. Sternberg, Alessia David Affiliation: Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK SUPPLEMENTARY MATERIAL Construction of the dataset Genetic variants occurring in protein coding genes were extracted from ExAC (version 0.3, Release: 13-Jan-2015), UniProt (humsavar.txt, release: 04-Feb-2015) and ClinVar (release:7-Jan-2015). Variants were classified as ‘disease-causing’ if a disease association was reported in humsavar.txt or ClinVar. For variants reported in ClinVar, we defined the variant as disease-causing only if it was annotated as “pathogenic”. In order to avoid a potential bias, variants annotated as “likely pathogenic” were not included in the analysis. Variants were classified as ‘neutral’ when no association with disease was present (variants reported as “polymorphisms” in humsavar.txt and variants from ExAC, not reported as disease-causing in other databases). Non-disease variants were divided according to their global minor allele frequencies (MAF) into: ‘rare variants’ (MAF < 0.01) and ‘common variants’ (MAF ≥ 0.01). Global MAF data were extracted from Ensembl using the BioMart data-mining tool. We used the global MAF calculated in the ExAC project. For variants not reported in ExAC database we used the global MAF reported in dbSNP (which is calculated from the 1000Genomes project), when available. Variants with no MAF information or reported as “unclassified” in humsavar.txt, were not included in the analysis. When the gene enrichment analysis (described below) was performed, one gene overlapped between the disease- and rare- EVsets and three genes between the disease- and common- EVsets. -
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). -
Noninvasive Sleep Monitoring in Large-Scale Screening of Knock-Out Mice
bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Noninvasive sleep monitoring in large-scale screening of knock-out mice reveals novel sleep-related genes Shreyas S. Joshi1*, Mansi Sethi1*, Martin Striz1, Neil Cole2, James M. Denegre2, Jennifer Ryan2, Michael E. Lhamon3, Anuj Agarwal3, Steve Murray2, Robert E. Braun2, David W. Fardo4, Vivek Kumar2, Kevin D. Donohue3,5, Sridhar Sunderam6, Elissa J. Chesler2, Karen L. Svenson2, Bruce F. O'Hara1,3 1Dept. of Biology, University of Kentucky, Lexington, KY 40506, USA, 2The Jackson Laboratory, Bar Harbor, ME 04609, USA, 3Signal solutions, LLC, Lexington, KY 40503, USA, 4Dept. of Biostatistics, University of Kentucky, Lexington, KY 40536, USA, 5Dept. of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. 6Dept. of Biomedical Engineering, University of Kentucky, Lexington, KY 40506, USA. *These authors contributed equally Address for correspondence and proofs: Shreyas S. Joshi, Ph.D. Dept. of Biology University of Kentucky 675 Rose Street 101 Morgan Building Lexington, KY 40506 U.S.A. Phone: (859) 257-2805 FAX: (859) 257-1717 Email: [email protected] Running title: Sleep changes in knockout mice bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Effects of Rapamycin on Social Interaction Deficits and Gene
Kotajima-Murakami et al. Molecular Brain (2019) 12:3 https://doi.org/10.1186/s13041-018-0423-2 RESEARCH Open Access Effects of rapamycin on social interaction deficits and gene expression in mice exposed to valproic acid in utero Hiroko Kotajima-Murakami1,2, Toshiyuki Kobayashi3, Hirofumi Kashii1,4, Atsushi Sato1,5, Yoko Hagino1, Miho Tanaka1,6, Yasumasa Nishito7, Yukio Takamatsu7, Shigeo Uchino1,2 and Kazutaka Ikeda1* Abstract The mammalian target of rapamycin (mTOR) signaling pathway plays a crucial role in cell metabolism, growth, and proliferation. The overactivation of mTOR has been implicated in the pathogenesis of syndromic autism spectrum disorder (ASD), such as tuberous sclerosis complex (TSC). Treatment with the mTOR inhibitor rapamycin improved social interaction deficits in mouse models of TSC. Prenatal exposure to valproic acid (VPA) increases the incidence of ASD. Rodent pups that are exposed to VPA in utero have been used as an animal model of ASD. Activation of the mTOR signaling pathway was recently observed in rodents that were exposed to VPA in utero, and rapamycin ameliorated social interaction deficits. The present study investigated the effect of rapamycin on social interaction deficits in both adolescence and adulthood, and gene expressions in mice that were exposed to VPA in utero. We subcutaneously injected 600 mg/kg VPA in pregnant mice on gestational day 12.5 and used the pups as a model of ASD. The pups were intraperitoneally injected with rapamycin or an equal volume of vehicle once daily for 2 consecutive days. The social interaction test was conducted in the offspring after the last rapamycin administration at 5–6 weeks of ages (adolescence) or 10–11 weeks of age (adulthood). -
Loss of the Tumor Suppressor Spinophilin (PPP1R9B) Increases the Cancer Stem Cell Population in Breast Tumors
Oncogene (2016) 35, 2777–2788 © 2016 Macmillan Publishers Limited All rights reserved 0950-9232/16 www.nature.com/onc ORIGINAL ARTICLE Loss of the tumor suppressor spinophilin (PPP1R9B) increases the cancer stem cell population in breast tumors I Ferrer1, EM Verdugo-Sivianes1, MA Castilla1, R Melendez1, JJ Marin1,2, S Muñoz-Galvan1, JL Lopez-Guerra3, B Vieites4, MJ Ortiz-Gordillo3, JM De León5, JM Praena-Fernandez6, M Perez1, J Palacios7 and A Carnero1,8 The spinophilin (Spn, PPP1R9B) gene is located at 17q21.33, a region frequently associated with microsatellite instability and loss of heterozygosity, especially in breast tumors. Spn is a regulatory subunit of phosphatase1a (PP1), which targets the catalytic subunit to distinct subcellular locations. Spn downregulation reduces PPP1CA activity against the retinoblastoma protein, pRb, thereby maintaining higher levels of phosphorylated pRb. This effect contributes to an increase in the tumorigenic properties of cells in certain contexts. Here, we explored the mechanism of how Spn downregulation contributes to the malignant phenotype and poor prognosis in breast tumors and found an increase in the stemness phenotype. Analysis of human breast tumors showed that Spn mRNA and protein are reduced or lost in 15% of carcinomas, correlating with a worse prognosis, a more aggressive tumor phenotype and triple-negative tumors, whereas luminal tumors showed high Spn levels. Downregulation of Spn by shRNA increased the stemness properties along with the expression of stem-related genes (Sox2, KLF4, Nanog and OCT4), whereas ectopic overexpression of Spn cDNA reduced these properties. Breast tumor stem cells appeared to have low levels of Spn mRNA, and Spn loss correlated with increased stem-like cell appearance in breast tumors as indicated by an increase in CD44+/CD24- cells. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
Ribosomal RNA‑Depleted RNA Sequencing Reveals the Pathogenesis of Refractory Mycoplasma Pneumoniae Pneumonia in Children
MOLECULAR MEDICINE REPORTS 24: 761, 2021 Ribosomal RNA‑depleted RNA sequencing reveals the pathogenesis of refractory Mycoplasma pneumoniae pneumonia in children FENG HUANG1,2*, HUIFENG FAN1*, DIYUAN YANG1, JUNSONG ZHANG3, TINGTING SHI1, DONGWEI ZHANG1 and GEN LU1 1Department of Respiration, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510120; 2Institute of Human Virology, Zhongshan School of Medicine, Sun Yat‑sen University; 3Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, P.R. China Received April 17, 2020; Accepted May 28, 2021 DOI: 10.3892/mmr.2021.12401 Abstract. Pneumonia caused by Mycoplasma pneumoniae in the NRMPP and RMPP comparative groups were primarily (M. pneumoniae) is a major cause of community‑acquired enriched in ‘herpes simplex virus 1 infection’, ‘viral carcinogen‑ pneumonia in children. In some cases, M. pneumoniae pneu‑ esis’ and ‘RNA transport’. In the present study, a comprehensive monia (MPP) can develop into refractory MPP (RMPP), which analysis of the differences between the NRMPP and RMPP shows no clinical or radiological response to macrolides, and cases was performed based on rRNA‑depleted RNA‑sequencing can progress to severe and complicated pneumonia. However, techniques, and the selected genes and circRNAs may be closely the pathogenesis of RMPP remains poorly understood. The associated with the complex pathogenesis of RMPP. present study aimed to identify target genes that could be used as biomarkers for the clinical diagnosis of early‑stage RMPP Introduction through high‑throughput sequencing technology. The differences in long non‑coding (lnc)RNAs, mRNAs and circular (circ)RNAs Mycoplasma pneumoniae (M. pneumoniae) is one of the were examined between whole‑blood samples from two patients main pathogens that cause respiratory tract infections in with non‑refractory MPP (NRMPP), two patients with RMPP humans (1,2). -
Chemical Genetic Screen Identifies Gapex-5/GAPVD1 and STBD1 As Novel AMPK Substrates
Chemical genetic screen identifies Gapex-5/GAPVD1 and STBD1 as novel AMPK substrates Ducommun, Serge; Deak, Maria; Zeigerer, Anja; Göransson, Olga; Seitz, Susanne; Collodet, Caterina; Madsen, Agnete Bjerregaard; Jensen, Thomas Elbenhardt; Viollet, Benoit; Foretz, Marc; Gut, Philipp; Sumpton, David; Sakamoto, Kei Published in: Cellular Signalling DOI: 10.1016/j.cellsig.2019.02.001 Publication date: 2019 Document version Publisher's PDF, also known as Version of record Document license: CC BY-NC-ND Citation for published version (APA): Ducommun, S., Deak, M., Zeigerer, A., Göransson, O., Seitz, S., Collodet, C., Madsen, A. B., Jensen, T. E., Viollet, B., Foretz, M., Gut, P., Sumpton, D., & Sakamoto, K. (2019). Chemical genetic screen identifies Gapex- 5/GAPVD1 and STBD1 as novel AMPK substrates. Cellular Signalling, 57, 45-57. https://doi.org/10.1016/j.cellsig.2019.02.001 Download date: 24. Sep. 2021 Cellular Signalling 57 (2019) 45–57 Contents lists available at ScienceDirect Cellular Signalling journal homepage: www.elsevier.com/locate/cellsig Chemical genetic screen identifies Gapex-5/GAPVD1 and STBD1 as novel AMPK substrates T Serge Ducommuna,b,1, Maria Deaka, Anja Zeigererc,d,e, Olga Göranssonf, Susanne Seitzc,d,e, Caterina Collodeta,b, Agnete B. Madseng, Thomas E. Jenseng, Benoit Violleth,i,j, Marc Foretzh,i,j, ⁎ Philipp Guta, David Sumptonk, Kei Sakamotoa,b, a Nestlé Research, École Polytechnique Fédérale de Lausanne (EPFL) Innovation Park, bâtiment G, 1015 Lausanne, Switzerland b School of Life Sciences, EPFL, 1015 Lausanne, Switzerland