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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Integrated Analysis of the Critical Region 5P15.3–P15.2 Associated with Cri-Du-Chat Syndrome
Genetics and Molecular Biology, 42, 1(suppl), 186-196 (2019) Copyright © 2019, Sociedade Brasileira de Genética. Printed in Brazil DOI: http://dx.doi.org/10.1590/1678-4685-GMB-2018-0173 Research Article Integrated analysis of the critical region 5p15.3–p15.2 associated with cri-du-chat syndrome Thiago Corrêa1, Bruno César Feltes2 and Mariluce Riegel1,3* 1Post-Graduate Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. 2Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. 3Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil. Abstract Cri-du-chat syndrome (CdCs) is one of the most common contiguous gene syndromes, with an incidence of 1:15,000 to 1:50,000 live births. To better understand the etiology of CdCs at the molecular level, we investigated theprotein–protein interaction (PPI) network within the critical chromosomal region 5p15.3–p15.2 associated with CdCs using systemsbiology. Data were extracted from cytogenomic findings from patients with CdCs. Based on clin- ical findings, molecular characterization of chromosomal rearrangements, and systems biology data, we explored possible genotype–phenotype correlations involving biological processes connected with CdCs candidate genes. We identified biological processes involving genes previously found to be associated with CdCs, such as TERT, SLC6A3, and CTDNND2, as well as novel candidate proteins with potential contributions to CdCs phenotypes, in- cluding CCT5, TPPP, MED10, ADCY2, MTRR, CEP72, NDUFS6, and MRPL36. Although further functional analy- ses of these proteins are required, we identified candidate proteins for the development of new multi-target genetic editing tools to study CdCs. -
Organizing Knowledge to Enable Faster Data Interpretation in COVID
F1000Research 2021, 10(ELIXIR):703 Last updated: 01 SEP 2021 DATA NOTE Organizing knowledge to enable faster data interpretation in COVID-19 research [version 1; peer review: 2 approved with reservations] Joseph Hearnshaw, Marco Brandizi, Ajit Singh , Chris Rawlings , Keywan Hassani-Pak Rothamsted Research, Harpenden, AL5 2JQ, UK v1 First published: 30 Jul 2021, 10(ELIXIR):703 Open Peer Review https://doi.org/10.12688/f1000research.54071.1 Latest published: 30 Jul 2021, 10(ELIXIR):703 https://doi.org/10.12688/f1000research.54071.1 Reviewer Status Invited Reviewers Abstract Enormous volumes of COVID-19 research data have been published 1 2 and this continues to increase daily. This creates challenges for researchers to interpret, prioritize and summarize their own findings version 1 in the context of published literature, clinical trials, and a multitude of 30 Jul 2021 report report databases. Overcoming the data interpretation bottleneck is vital to help researchers to be more efficient in their quest to identify COVID- 1. Justin Reese , Lawrence Berkeley National 19 risk factors, potential treatments, drug side-effects, and much more. As a proof of concept, we have organized and integrated a Laboratory, Berkeley, USA range of COVID-19 and human biomedical data and literature into a knowledge graph (KG). Here we present the datasets we have 2. Keith Hall , Google Research, New York, integrated so far and the content of the KG which consists of 674,969 USA biological concepts and over 1.6 million relationships between them. The COVID-19 KG is available via KnetMiner, an interactive online Any reports and responses or comments on the platform for gene discovery and knowledge mining, or via RDF and article can be found at the end of the article. -
The Genetics of Bipolar Disorder
Molecular Psychiatry (2008) 13, 742–771 & 2008 Nature Publishing Group All rights reserved 1359-4184/08 $30.00 www.nature.com/mp FEATURE REVIEW The genetics of bipolar disorder: genome ‘hot regions,’ genes, new potential candidates and future directions A Serretti and L Mandelli Institute of Psychiatry, University of Bologna, Bologna, Italy Bipolar disorder (BP) is a complex disorder caused by a number of liability genes interacting with the environment. In recent years, a large number of linkage and association studies have been conducted producing an extremely large number of findings often not replicated or partially replicated. Further, results from linkage and association studies are not always easily comparable. Unfortunately, at present a comprehensive coverage of available evidence is still lacking. In the present paper, we summarized results obtained from both linkage and association studies in BP. Further, we indicated new potential interesting genes, located in genome ‘hot regions’ for BP and being expressed in the brain. We reviewed published studies on the subject till December 2007. We precisely localized regions where positive linkage has been found, by the NCBI Map viewer (http://www.ncbi.nlm.nih.gov/mapview/); further, we identified genes located in interesting areas and expressed in the brain, by the Entrez gene, Unigene databases (http://www.ncbi.nlm.nih.gov/entrez/) and Human Protein Reference Database (http://www.hprd.org); these genes could be of interest in future investigations. The review of association studies gave interesting results, as a number of genes seem to be definitively involved in BP, such as SLC6A4, TPH2, DRD4, SLC6A3, DAOA, DTNBP1, NRG1, DISC1 and BDNF. -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2- -
Supplementary Table 2
Supplementary Table 2. Differentially Expressed Genes following Sham treatment relative to Untreated Controls Fold Change Accession Name Symbol 3 h 12 h NM_013121 CD28 antigen Cd28 12.82 BG665360 FMS-like tyrosine kinase 1 Flt1 9.63 NM_012701 Adrenergic receptor, beta 1 Adrb1 8.24 0.46 U20796 Nuclear receptor subfamily 1, group D, member 2 Nr1d2 7.22 NM_017116 Calpain 2 Capn2 6.41 BE097282 Guanine nucleotide binding protein, alpha 12 Gna12 6.21 NM_053328 Basic helix-loop-helix domain containing, class B2 Bhlhb2 5.79 NM_053831 Guanylate cyclase 2f Gucy2f 5.71 AW251703 Tumor necrosis factor receptor superfamily, member 12a Tnfrsf12a 5.57 NM_021691 Twist homolog 2 (Drosophila) Twist2 5.42 NM_133550 Fc receptor, IgE, low affinity II, alpha polypeptide Fcer2a 4.93 NM_031120 Signal sequence receptor, gamma Ssr3 4.84 NM_053544 Secreted frizzled-related protein 4 Sfrp4 4.73 NM_053910 Pleckstrin homology, Sec7 and coiled/coil domains 1 Pscd1 4.69 BE113233 Suppressor of cytokine signaling 2 Socs2 4.68 NM_053949 Potassium voltage-gated channel, subfamily H (eag- Kcnh2 4.60 related), member 2 NM_017305 Glutamate cysteine ligase, modifier subunit Gclm 4.59 NM_017309 Protein phospatase 3, regulatory subunit B, alpha Ppp3r1 4.54 isoform,type 1 NM_012765 5-hydroxytryptamine (serotonin) receptor 2C Htr2c 4.46 NM_017218 V-erb-b2 erythroblastic leukemia viral oncogene homolog Erbb3 4.42 3 (avian) AW918369 Zinc finger protein 191 Zfp191 4.38 NM_031034 Guanine nucleotide binding protein, alpha 12 Gna12 4.38 NM_017020 Interleukin 6 receptor Il6r 4.37 AJ002942 -
TPPP (N-19): Sc-82065
SAN TA C RUZ BI OTEC HNOL OG Y, INC . TPPP (N-19): sc-82065 BACKGROUND APPLICATIONS Tubulin family members are globular proteins important in the assembly of TPPP (N-19) is recommended for detection of TPPP of mouse, rat and microtubules. Microtubules are structural components that play important human origin by Western Blotting (starting dilution 1:200, dilution range roles in mitosis, cytokinesis and vesicle transport. TPPP (tubulin polymeriza - 1:100-1:1000), immunoprecipitation [1-2 µg per 100-500 µg of total protein tion-promoting protein), also known as p24 and p25, is a widely expressed (1 ml of cell lysate)], immunofluorescence (starting dilution 1:50, dilution 219 amino acid protein found in the perinuclear region of the cytoplasm. TPPP range 1:50-1:500) and solid phase ELISA (starting dilution 1:30, dilution may form dimers and functions in polymerizing tubulin into double-walled range 1:30-1:3000). tubules, polymorphic aggregates, or stabilized blocks. TPPP overexpression Suitable for use as control antibody for TPPP siRNA (h): sc-76720, TPPP prevents formation of the mitotic spindle assembly and breakdown of the siRNA (m): sc-76721, TPPP shRNA Plasmid (h): sc-76720-SH, TPPP shRNA nuclear envelope. TPPP is phosphorylated by TPK II and is encoded by a gene Plasmid (m): sc-76721-SH, TPPP shRNA (h) Lentiviral Particles: sc-76720-V that maps to human chromosome 5, which contains 181 million base pairs and TPPP shRNA (m) Lentiviral Particles: sc-76721-V. and comprises nearly 6% of the human genome. Molecular Weight of TPPP: 24 kDa. REFERENCES Positive Controls: mouse brain extract: sc-2253, mouse cerebellum extract: 1. -
Fibroblast-Derived Induced Pluripotent Stem Cells Show No Common Retroviral Vector Insertions
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Harvard University - DASH Fibroblast-Derived Induced Pluripotent Stem Cells Show No Common Retroviral Vector Insertions The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Varas, Florencio, Matthias Stadtfeld, Luisa de Andres-Aguayo, Nimet Maherali, Alessandro di Tullio, Lorena Pantano, Cedric Notredame, et al. 2009. Fibroblast-Derived Induced Pluripotent Stem Cells Show No Common Retroviral Vector Insertions. Stem Cells 27(2): 300-306. Published Version doi:10.1634/stemcells.2008-0696 Accessed February 18, 2015 8:46:59 PM EST Citable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:4459933 Terms of Use This article was downloaded from Harvard University's DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn- 3:HUL.InstRepos:dash.current.terms-of-use#OAP (Article begins on next page) EMBRYONIC STEM CELLS/INDUCED PLURIPOTENT STEM CELLS Fibroblast-Derived Induced Pluripotent Stem Cells Show No Common Retroviral Vector Insertions FLORENCIO VARAS,a MATTHIAS STADTFELD,b LUISA DE ANDRES-AGUAYO,a NIMET MAHERALI,b ALESSANDRO DI TULLIO,a LORENA PANTANO,c CEDRIC NOTREDAME,c KONRAD HOCHEDLINGER,b THOMAS GRAFa,d aDifferentiation and Cancer and cBioinformatics Program, Center for Genomic Regulation and Pompeu Fabra University, Barcelona, Spain; bCancer Center and Center for Regenerative Medicine, Massachusetts General Hospital, Harvard Stem Cell Institute, Boston, Massachusetts, USA; dInstitucio´ Catalana de Recerca i Estudis Avanc¸ats Key Words. -
Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Olecm Ular Determinants Critical to the Development of Glioblastoma Brian W
Florida International University FIU Digital Commons Robert Stempel College of Public Health & Social Environmental Health Sciences Work 5-30-2013 Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines olecM ular Determinants Critical to the Development of Glioblastoma Brian W. Kunkle Department of Environmental Health Sciences, Florida International University Changwon Yoo Department of Biostatistics, Florida International University, [email protected] Deodutta Roy Department of Environmental Health Sciences, Florida International University, [email protected] Follow this and additional works at: https://digitalcommons.fiu.edu/eoh_fac Part of the Medicine and Health Sciences Commons Recommended Citation Kunkle BW, Yoo C, Roy D (2013) Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma. PLoS ONE 8(5): e64140. https://doi.org/10.1371/ journal.pone.0064140 This work is brought to you for free and open access by the Robert Stempel College of Public Health & Social Work at FIU Digital Commons. It has been accepted for inclusion in Environmental Health Sciences by an authorized administrator of FIU Digital Commons. For more information, please contact [email protected]. Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma Brian W. Kunkle1, Changwon Yoo2, Deodutta Roy1* 1 Department of Environmental and Occupational Health, Florida International University, Miami, Florida, United States of America, 2 Department of Biostatistics, Florida International University, Miami, Florida, United States of America Abstract In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. -
Copy Number Variation, Chromosome Rearrangement, and Their Association with Recombination During Avian Evolution
Downloaded from genome.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Research Copy number variation, chromosome rearrangement, and their association with recombination during avian evolution Martin Vo¨lker,1 Niclas Backstro¨m,2 Benjamin M. Skinner,1,3 Elizabeth J. Langley,1,4 Sydney K. Bunzey,1 Hans Ellegren,2 and Darren K. Griffin1,5 1School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, United Kingdom; 2Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyva¨gen 18D, SE-752 36 Uppsala, Sweden Chromosomal rearrangements and copy number variants (CNVs) play key roles in genome evolution and genetic disease; however, the molecular mechanisms underlying these types of structural genomic variation are not fully understood. The availability of complete genome sequences for two bird species, the chicken and the zebra finch, provides, for the first time, an ideal opportunity to analyze the relationship between structural genomic variation (chromosomal and CNV) and recombination on a genome-wide level. The aims of this study were therefore threefold: (1) to combine bioinformatics, physical mapping to produce comprehensive comparative maps of the genomes of chicken and zebra finch. In so doing, this allowed the identification of evolutionary chromosomal rearrangements distinguishing them. The previously reported interchromosomal conservation of synteny was confirmed, but a larger than expected number of intrachromosomal rearrangements were reported; (2) to hybridize zebra finch genomic DNA to a chicken tiling path microarray and identify CNVs in the zebra finch genome relative to chicken; 32 interspecific CNVs were identified; and (3) to test the hypothesis that there is an association between CNV, chromosomal rearrangements, and recombination by correlating data from (1) and (2) with recombination rate data from a high-resolution genetic linkage map of the zebra finch. -
Co-Expression Network Analysis Identifies a Gene Signature As A
Zhu et al. Cancer Cell Int (2020) 20:259 https://doi.org/10.1186/s12935-020-01352-2 Cancer Cell International PRIMARY RESEARCH Open Access Co-expression network analysis identifes a gene signature as a predictive biomarker for energy metabolism in osteosarcoma Naiqiang Zhu1* , Jingyi Hou2, Guiyun Ma1, Shuai Guo1, Chengliang Zhao1 and Bin Chen1 Abstract Background: Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occur- ring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. Methods: Energy metabolism-related genes were obtained from the TARGET database. We applied the “NFM” algo- rithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identifed by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selec- tion operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. Results: Two subtypes associated with energy metabolism was determined using the “NFM” algorithm, and signif- cant modules related to energy metabolism were identifed by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the signifcant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis.