KAP-1, a Novel Corepressor for the Highly Conserved KRAB Repression Domain
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Topological Scoring of Protein Interaction Networks
bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Topological Scoring of Protein Interaction Networks Mihaela E. Sardiu1, Joshua M. Gilmore1,2, Brad D. Groppe1,3, Arnob Dutta1,4, Laurence Florens1, and Michael P. Washburn1,5‡ 1Stowers Institute for Medical Research, Kansas City, MO 64110 U.S.A. 2Current Address: Boehringer Ingelheim Vetmedica, St. Joseph, MO 64506 U.S.A. 3Current Address: Thermo Fisher Scientific, Waltham, MA 02451, U.S.A. 4Current Address: Department of Cell and Molecular Biology, University of Rhode Island, 287 CBLS, 120 Flagg Road, Kingston, RI 02881. 5 Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, Kansas 66160, USA ‡To whom correspondence should be addressed: Michael Washburn, Ph.D. Stowers Institute for Medical Research 1000 E. 50th St. Kansas City, MO 64110 Phone: 816-926-4457 E-mail: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Abstract It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic analyses of affinity purifications. -
Genetic Causes of Severe Childhood Obesity: a Remarkably High Prevalence in an Inbred Population of Pakistan
1424 Diabetes Volume 69, July 2020 Genetic Causes of Severe Childhood Obesity: A Remarkably High Prevalence in an Inbred Population of Pakistan Sadia Saeed,1,2 Muhammad Arslan,3 Jaida Manzoor,4 Sadia M. Din,5 Qasim M. Janjua,5,6 Hina Ayesha,7 Qura-tul Ain,5 Laraib Inam,3 Stephane Lobbens,1 Emmanuel Vaillant,1 Emmanuelle Durand,1 Mehdi Derhourhi,1 Souhila Amanzougarene,1 Alaa Badreddine,1 Lionel Berberian,1 Stefan Gaget,1 Waqas I. Khan,8 Taeed A. Butt,9 Amélie Bonnefond,1,2 and Philippe Froguel1,2 Diabetes 2020;69:1424–1438 | https://doi.org/10.2337/db19-1238 Monogenic forms of obesity have been identified in £10% material in the quest of new genes/variants influencing of severely obese European patients. However, the overall energy balance. spectrum of deleterious variants (point mutations and structural variants) responsible for childhood severe obe- sity remains elusive. In this study, we genetically screened The monogenic forms of obesity have defined the current 225 severely obese children from consanguineous Pakis- concepts of the central regulation of energy balance and have tani families through a combination of techniques, includ- opened new avenues for precision medicine (1,2). Monogenic ing an in-house–developed augmented whole-exome nonsyndromic obesity is due to pathogenic mutations in sequencing method (CoDE-seq) that enables simultaneous genes involved in leptin-melanocortin signaling, resulting in detection of whole-exome copy number variations (CNVs) extreme, early-onset obesity with an insatiable craving for fi and point mutations in coding regions. We identi ed food (2). In addition to excessive adiposity, syndromic obesity OBESITY STUDIES 110 (49%) probands carrying 55 different pathogenic point associates with other abnormalities such as dysmorphic fea- mutations and CNVs in 13 genes/loci responsible for non- tures, intellectual disability, and organ-specific anomalies (3). -
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. -
Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits
Cai et al. Genet Sel Evol (2020) 52:19 https://doi.org/10.1186/s12711-020-00538-6 Genetics Selection Evolution RESEARCH ARTICLE Open Access Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle Zexi Cai1*†, Magdalena Dusza2†, Bernt Guldbrandtsen1, Mogens Sandø Lund1 and Goutam Sahana1 Abstract Background: Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that afect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Efect-bearing variants often afect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic efects of variants in candidate genes. However, large sample sizes are required to achieve sufcient power. Multi-trait meta-analysis is one approach to deal with this prob- lem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. Results: For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis com- pared with the subjective assessment of overlapping of single-trait QTL confdence intervals. Pleiotropic efects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confrmed by bivariate association analysis. The previously reported pleiotropic efects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic efects of variants in GHR on milk yield and fat yield were con- frmed. -
The Evolutionarily Conserved Kruppel-Associated Box Domain
Proc. Natl. Acad. Sci. USA Vol. 88, pp. 3608-3612, May 1991 Biochemistry The evolutionarily conserved Kruppel-associated box domain defines a subfamily of eukaryotic multifingered proteins (DNA-binding proteins/sequence conservation/ceil differentiation) ERIC J. BELLEFROID, DOMINIQUE A. PONCELET, PIERRE J. LECOCQ, OLIVIER REVELANT, AND JOSEPH A. MARTIAL Laboratoire de Biologie Moldculaire et de Genie G6ndtique, Universit6 de Liege, Institut de Chimie B6, B-4000 Sart Tilman, Belgium Communicated by William J. Rutter, January 2, 1991 ABSTRACT We have previously shown that the human number of finger proteins (20-23), is highly conserved in genome includes hundreds of genes coding for putative factors evolution and appears always associated with finger repeats. related to the Krfippel zinc-ringer protein, which regulates Drosophila segmentation. We report herein that about one- third of these genes code for proteins that share a very MATERIALS AND METHODS conserved region of about 75 amino acids in their N-terminal cDNA Library Screening. A human AgtlO cDNA library nonfinger portion. Homologous regions are found in a number from undifferentiated NT2D1 cells (24) was screened with a of previously described finger proteins, including mouse Zfp-l 546-base-pair EcoRI-EcoRI fragment corresponding to the 5' and Xenopus Xfin. We named this region the Kruppel- nonfinger portion of the coding region of HPF4 (where HPF associated box (KRAB). This domain has the potential to form is human placental finger) (19). The DNA probes were two amphipathic a-helices. Southern blot analysis of "zoo" labeled with a multiprimed labeling kit (Boehringer Mann- blots suggests that the Krfippel-associated box is highly con- heim) to a specific activity of5 x 101 cpm/pug. -
The Interactome of KRAB Zinc Finger Proteins Reveals the Evolutionary History of Their Functional Diversification
Resource The interactome of KRAB zinc finger proteins reveals the evolutionary history of their functional diversification Pierre-Yves Helleboid1,†, Moritz Heusel2,†, Julien Duc1, Cécile Piot1, Christian W Thorball1, Andrea Coluccio1, Julien Pontis1, Michaël Imbeault1, Priscilla Turelli1, Ruedi Aebersold2,3,* & Didier Trono1,** Abstract years ago (MYA) (Imbeault et al, 2017). Their products harbor an N-terminal KRAB (Kru¨ppel-associated box) domain related to that of Krüppel-associated box (KRAB)-containing zinc finger proteins Meisetz (a.k.a. PRDM9), a protein that originated prior to the diver- (KZFPs) are encoded in the hundreds by the genomes of higher gence of chordates and echinoderms, and a C-terminal array of zinc vertebrates, and many act with the heterochromatin-inducing fingers (ZNF) with sequence-specific DNA-binding potential (Urru- KAP1 as repressors of transposable elements (TEs) during early tia, 2003; Birtle & Ponting, 2006; Imbeault et al, 2017). KZFP genes embryogenesis. Yet, their widespread expression in adult tissues multiplied by gene and segment duplication to count today more and enrichment at other genetic loci indicate additional roles. than 350 and 700 representatives in the human and mouse Here, we characterized the protein interactome of 101 of the ~350 genomes, respectively (Urrutia, 2003; Kauzlaric et al, 2017). A human KZFPs. Consistent with their targeting of TEs, most KZFPs majority of human KZFPs including all primate-restricted family conserved up to placental mammals essentially recruit KAP1 and members target sequences derived from TEs, that is, DNA trans- associated effectors. In contrast, a subset of more ancient KZFPs posons, ERVs (endogenous retroviruses), LINEs, SINEs (long and rather interacts with factors related to functions such as genome short interspersed nuclear elements, respectively), or SVAs (SINE- architecture or RNA processing. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
Role of the PTPRD Locus and Limited Pleiotropy with Other Neuropathologies
CORE Metadata, citation and similar papers at core.ac.uk Provided by Harvard University - DASH Susceptibility to neurofibrillary tangles: role of the PTPRD locus and limited pleiotropy with other neuropathologies The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Chibnik, L. B., C. C. White, S. Mukherjee, T. Raj, L. Yu, E. B. Larson, T. J. Montine, et al. 2017. “Susceptibility to neurofibrillary tangles: role of the PTPRD locus and limited pleiotropy with other neuropathologies.” Molecular psychiatry :10.1038/mp.2017.20. doi:10.1038/mp.2017.20. http://dx.doi.org/10.1038/mp.2017.20. Published Version doi:10.1038/mp.2017.20 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:34492045 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Mol Psychiatry Manuscript Author . Author Manuscript Author manuscript; available in PMC 2017 September 22. Susceptibility to neurofibrillary tangles: role of the PTPRD locus and limited pleiotropy with other neuropathologies Lori B Chibnik, PhD, MPH1,2,3,4, Charles C White, PhD1,3, Shubhabrata Mukherjee, PhD5, Towfique Raj, PhD1,3, Lei Yu, PhD6, Eric B. Larson, MD, MPH7, Thomas J. Montine, MD, PhD8, C. Dirk Keene, MD, PhD8, Joshua Sonnen, MD9, Julie A. Schneider, MD6, Paul K. Crane, MD, MPH5, Joshua M. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Genetic Interactions Between ANLN and KDR Are Prognostic for Breast Cancer Survival
ONCOLOGY REPORTS 42: 2255-2266, 2019 Genetic interactions between ANLN and KDR are prognostic for breast cancer survival XIAOFENG DAI1*, XIAO CHEN2*, OLIVIER HAKIZIMANA2 and YI MEI2 1Wuxi School of Medicine, 2School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, P.R. China Received April 3, 2019; Accepted August 7, 2019 DOI: 10.3892/or.2019.7332 Abstract. Single nucleotide polymorphisms (SNPs) are the of ~627,000 annually estimated in 2018 (2). Uncontrolled most common genetic variation in mammalian cells with proliferative growth and angiogenesis are two basic cancer prognostic potential. Anillin-actin binding protein (ANLN) hallmarks governing the critical transitions towards malig- has been identified as being involved in PI3K/PTEN signaling, nancy during carcinogenesis (3). PI3K/PTEN signaling, which is critical in cell life/death control, and kinase insert frequently altered in breast carcinoma (4), confers a survival domain receptor (KDR) encodes a key receptor mediating advantage to tumor cells (5). Anillin, encoded by anillin the cancer angiogenesis/metastasis switch. Knowledge of actin-binding protein (ANLN), is an actin-binding protein, the intrinsic connections between PI3K/PTEN and KDR which has been identified as being involved in the PI3K/PTEN signaling, which represent two critical transitions in carcino- pathway (6,7). It is an F‑actin binding protein, which maintains genesis, led the present study to investigate the effects of the podocyte cytoskeletal dynamics, cell motility and signaling potential synergy between ANLN and KDR on breast cancer through its interaction with CD2-associated protein, which outcome and identify relevant SNPs driving such a synergy stimulates the phosphorylation of AKT at serine 473 (6,8). -
Research Article Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets
Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 170289, 10 pages http://dx.doi.org/10.1155/2014/170289 Research Article Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets Fang Liu,1 Yaning Feng,1 Zhenye Li,2 Chao Pan,1 Yuncong Su,1 Rui Yang,1 Liying Song,1 Huilong Duan,1 and Ning Deng1 1 Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China 2 General Hospital of Ningxia Medical University, Yinchuan 750004, China Correspondence should be addressed to Ning Deng; [email protected] Received 30 March 2014; Accepted 12 May 2014; Published 2 June 2014 Academic Editor: Degui Zhi Copyright © 2014 Fang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. -
Immunomic Analysis of Human Renal Cell Carcinoma
Immunomic analysis of human renal cell carcinoma Zur Erlangung des akademischen Grades eines DOKTORS DER NATURWISSENSCHAFTEN (Dr. rer. nat.) der Fakultät für Chemie und Biowissenschaften der Universität Karlsruhe (TH) vorgelegte DISSERTATION von Gerard Devitt aus Cork, Irland Dekan: Prof. Dr. Manfred Kappes Referent: Prof. Dr. Margot Zöller Korreferent: Prof. Dr. Jonathan Sleeman Tag der mündlichen Prüfung: 19th July 2004 For James, Roseanne, Niall, Owen and Nadia Zusammenfassung - 3 – Das Nierenzellkarzinom ist der am häufigsten in den Nieren vorkommende Tumor. Er macht bis zu 3% aller malignen Erkrankungen bei Erwachsenen aus. Die Zahl der Nierenzellkarzinompatienten steigt jährlich. Bisher gibt es keine international standardisierte Therapie für das metastasierende Nierenzellkarzinom und die vollständige Entfernung der betroffenen Niere ist zur Zeit die einzige Behandlungsmöglichkeit. Das Nierenzellkarzinom ist sehr resistent gegen Bestrahlung und Chemotherapie, zeigt aber in manchen Untersuchungen eine spontane Regression, die auf Immunogenität des Tumors schließen lässt. Die Identifikation von nierenzellkarzinomassoziierten immunogenen Molekülen ist daher wichtig, um 1.) die grundlegenden Mechanismen der Immunogenität des Nierenzellkarzinoms zu verstehen, 2.) für mögliche neue Ansätze in Diagnose, Prognose und Therapie. In dieser Arbeit wurden drei Techniken eingesetzt, um nierenzellkarzinomassoziierte Gene und Antigene zu erkennen: 1) Suppression substractive hybridization (SSH), 2) Serological analysis of recombinant cDNA expression