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Lupus-Associated Endogenous Retroviral LTR Polymorphism and Epigenetic Imprinting Promote HRES-1/Rab4 Expression and Mtor Activation
Lupus-associated endogenous retroviral LTR polymorphism and epigenetic imprinting promote HRES-1/Rab4 expression and mTOR activation Aparna Godavarthy, … , Katalin Banki, Andras Perl JCI Insight. 2019. https://doi.org/10.1172/jci.insight.134010. Research In-Press Preview Immunology Graphical abstract Find the latest version: https://jci.me/134010/pdf LUPUS-ASSOCIATED ENDOGENOUS RETROVIRAL LTR POLYMORPHISM AND EPIGENETIC IMPRINTING PROMOTE HRES-1/RAB4 EXPRESSION AND MTOR ACTIVATION Aparna Godavarthy*1, Ryan Kelly*1, John Jimah1, Miguel Beckford1, Tiffany Caza1,2, David Fernandez1,2, Nick Huang1,3, Manuel Duarte1,2, Joshua Lewis1,2, Hind J. Fadel4, Eric M. Poeschla4, Katalin Banki5, and Andras Perl1,2,3 * These authors contributed equally to the study. 1, Division of Rheumatology, Department of Medicine; 2, Department of Microbiology and Immunology; 3, Department of Biochemistry and Molecular Biology, and 5, Department of Pathology, State University of New York, Upstate Medical University, College of Medicine, 750 East Adams Street, Syracuse, New York 13210; 4, Department of Molecular Medicine; Mayo Clinic College of Medicine, 200 First Street SW, Rochester 55905, USA; Correspondence: Andras Perl, M.D., Ph.D. , State University of New York, College of Medicine, 750 East Adams Street, Syracuse, New York 13210; Phone: (315) 464-4194; Fax: (315) 464- 4176; E-mail: [email protected] Key Words: Systemic Lupus Erythematosus, T Cells, HRES-1/Rab4. mTOR, Autoimmunity The authors have declared that no conflict of interest exists. Supplementary Materials include Supplemental Methods and Supplementary Figures S1-S26. 1 ABSTRACT Overexpression and long terminal repeat (LTR) polymorphism of the HRES-1/Rab4 human endogenous retrovirus locus have been associated with T-cell activation and disease manifestations in systemic lupus erythematosus (SLE). -
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. -
Behavioral and Other Phenotypes in a Cytoplasmic Dynein Light Intermediate Chain 1 Mutant Mouse
The Journal of Neuroscience, April 6, 2011 • 31(14):5483–5494 • 5483 Cellular/Molecular Behavioral and Other Phenotypes in a Cytoplasmic Dynein Light Intermediate Chain 1 Mutant Mouse Gareth T. Banks,1* Matilda A. Haas,5* Samantha Line,6 Hazel L. Shepherd,6 Mona AlQatari,7 Sammy Stewart,7 Ida Rishal,8 Amelia Philpott,9 Bernadett Kalmar,2 Anna Kuta,1 Michael Groves,3 Nicholas Parkinson,1 Abraham Acevedo-Arozena,10 Sebastian Brandner,3,4 David Bannerman,6 Linda Greensmith,2,4 Majid Hafezparast,9 Martin Koltzenburg,2,4,7 Robert Deacon,6 Mike Fainzilber,8 and Elizabeth M. C. Fisher1,4 1Department of Neurodegenerative Disease, 2Sobell Department of Motor Science and Movement Disorders, 3Division of Neuropathology, and 4Medical Research Council (MRC) Centre for Neuromuscular Diseases, University College London (UCL) Institute of Neurology, London WC1N 3BG, United Kingdom, 5MRC National Institute for Medical Research, London NW7 1AA, United Kingdom, 6Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, United Kingdom, 7UCL Institute of Child Health, London WC1N 1EH, United Kingdom, 8Department of Biological Chemistry, Weizmann Institute of Science, 76100 Rehovot, Israel, 9School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom, and 10MRC Mammalian Genetics Unit, Harwell OX11 ORD, United Kingdom The cytoplasmic dynein complex is fundamentally important to all eukaryotic cells for transporting a variety of essential cargoes along microtubules within the cell. This complex also plays more specialized roles in neurons. The complex consists of 11 types of protein that interact with each other and with external adaptors, regulators and cargoes. Despite the importance of the cytoplasmic dynein complex, weknowcomparativelylittleoftherolesofeachcomponentprotein,andinmammalsfewmutantsexistthatallowustoexploretheeffects of defects in dynein-controlled processes in the context of the whole organism. -
Rabbit Anti-RABEP1 Antibody-SL19721R
SunLong Biotech Co.,LTD Tel: 0086-571- 56623320 Fax:0086-571- 56623318 E-mail:[email protected] www.sunlongbiotech.com Rabbit Anti-RABEP1 antibody SL19721R Product Name: RABEP1 Chinese Name: RABEP1蛋白抗体 Neurocrescin; Rab GTPase binding effector protein 1; RAB5EP; Rabaptin 4; Rabaptin Alias: 5; Rabaptin 5alpha; RABPT5; RABPT5A; Renal carcinoma antigen NY REN 17; Renal carcinoma antigen NYREN17. Organism Species: Rabbit Clonality: Polyclonal React Species: Human,Mouse,Rat, ELISA=1:500-1000IHC-P=1:400-800IHC-F=1:400-800ICC=1:100-500IF=1:100- 500(Paraffin sections need antigen repair) Applications: not yet tested in other applications. optimal dilutions/concentrations should be determined by the end user. Molecular weight: 99kDa Cellular localization: The cell membrane Form: Lyophilized or Liquid Concentration: 1mg/ml immunogen: KLH conjugated synthetic peptide derived from human RABEP1:501-600/862 Lsotype: IgGwww.sunlongbiotech.com Purification: affinity purified by Protein A Storage Buffer: 0.01M TBS(pH7.4) with 1% BSA, 0.03% Proclin300 and 50% Glycerol. Store at -20 °C for one year. Avoid repeated freeze/thaw cycles. The lyophilized antibody is stable at room temperature for at least one month and for greater than a year Storage: when kept at -20°C. When reconstituted in sterile pH 7.4 0.01M PBS or diluent of antibody the antibody is stable for at least two weeks at 2-4 °C. PubMed: PubMed RABEP1 is a Rab effector protein acting as linker between gamma-adaptin, RAB4A and RAB5A. It is involved in endocytic membrane fusion and membrane trafficking of Product Detail: recycling endosomes. Stimulates RABGEF1 mediated nucleotide exchange on RAB5A. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
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 -
Supplementary Materials and Tables a and B
SUPPLEMENTARY MATERIAL 1 Table A. Main characteristics of the subset of 23 AML patients studied by high-density arrays (subset A) WBC BM blasts MYST3- MLL Age/Gender WHO / FAB subtype Karyotype FLT3-ITD NPM status (x109/L) (%) CREBBP status 1 51 / F M4 NA 21 78 + - G A 2 28 / M M4 t(8;16)(p11;p13) 8 92 + - G G 3 53 / F M4 t(8;16)(p11;p13) 27 96 + NA G NA 4 24 / M PML-RARα / M3 t(15;17) 5 90 - - G G 5 52 / M PML-RARα / M3 t(15;17) 1.5 75 - - G G 6 31 / F PML-RARα / M3 t(15;17) 3.2 89 - - G G 7 23 / M RUNX1-RUNX1T1 / M2 t(8;21) 38 34 - + ND G 8 52 / M RUNX1-RUNX1T1 / M2 t(8;21) 8 68 - - ND G 9 40 / M RUNX1-RUNX1T1 / M2 t(8;21) 5.1 54 - - ND G 10 63 / M CBFβ-MYH11 / M4 inv(16) 297 80 - - ND G 11 63 / M CBFβ-MYH11 / M4 inv(16) 7 74 - - ND G 12 59 / M CBFβ-MYH11 / M0 t(16;16) 108 94 - - ND G 13 41 / F MLLT3-MLL / M5 t(9;11) 51 90 - + G R 14 38 / F M5 46, XX 36 79 - + G G 15 76 / M M4 46 XY, der(10) 21 90 - - G NA 16 59 / M M4 NA 29 59 - - M G 17 26 / M M5 46, XY 295 92 - + G G 18 62 / F M5 NA 67 88 - + M A 19 47 / F M5 del(11q23) 17 78 - + M G 20 50 / F M5 46, XX 61 59 - + M G 21 28 / F M5 46, XX 132 90 - + G G 22 30 / F AML-MD / M5 46, XX 6 79 - + M G 23 64 / M AML-MD / M1 46, XY 17 83 - + M G WBC: white blood cell. -
A Spatially Aware Likelihood Test to Detect Sweeps from Haplotype Distributions
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.12.443825; this version posted May 13, 2021. 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-NC-ND 4.0 International license. A spatially aware likelihood test to detect sweeps from haplotype distributions Michael DeGiorgio1;∗, Zachary A. Szpiech2;3;∗ 1Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA 2Department of Biology, Pennsylvania State University, University Park, PA 16801, USA 3Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16801, USA ∗Corresponding authors: [email protected] (M.D.), [email protected] (Z.A.S.) Keywords: haplotypes, hard sweeps, soft sweeps, maximum likelihood 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.05.12.443825; this version posted May 13, 2021. 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-NC-ND 4.0 International license. Abstract The inference of positive selection in genomes is a problem of great interest in evolutionary genomics. By identifying putative regions of the genome that contain adaptive mutations, we are able to learn about the biology of organisms and their evolutionary history. Here we introduce a composite likelihood method that identifies recently completed or ongoing positive selection by searching for extreme distortions in the spatial distribution of the haplotype fre- quency spectrum relative to the genome-wide expectation taken as neutrality. -
Single Cell Transcriptional and Chromatin Accessibility Profiling Redefine Cellular Heterogeneity in the Adult Human Kidney
ARTICLE https://doi.org/10.1038/s41467-021-22368-w OPEN Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney Yoshiharu Muto 1,7, Parker C. Wilson 2,7, Nicolas Ledru 1, Haojia Wu1, Henrik Dimke 3,4, ✉ Sushrut S. Waikar 5 & Benjamin D. Humphreys 1,6 1234567890():,; The integration of single cell transcriptome and chromatin accessibility datasets enables a deeper understanding of cell heterogeneity. We performed single nucleus ATAC (snATAC- seq) and RNA (snRNA-seq) sequencing to generate paired, cell-type-specific chromatin accessibility and transcriptional profiles of the adult human kidney. We demonstrate that snATAC-seq is comparable to snRNA-seq in the assignment of cell identity and can further refine our understanding of functional heterogeneity in the nephron. The majority of differ- entially accessible chromatin regions are localized to promoters and a significant proportion are closely associated with differentially expressed genes. Cell-type-specific enrichment of transcription factor binding motifs implicates the activation of NF-κB that promotes VCAM1 expression and drives transition between a subpopulation of proximal tubule epithelial cells. Our multi-omics approach improves the ability to detect unique cell states within the kidney and redefines cellular heterogeneity in the proximal tubule and thick ascending limb. 1 Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA. 2 Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA. 3 Department of Cardiovascular and Renal Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark. 4 Department of Nephrology, Odense University Hospital, Odense, Denmark. -
Parent-Of-Origin Effects on Quantitative Phenotypes in a Large Hutterite Pedigree
ARTICLE https://doi.org/10.1038/s42003-018-0267-4 OPEN Parent-of-origin effects on quantitative phenotypes in a large Hutterite pedigree Sahar V. Mozaffari 1,2, Jeanne M. DeCara3, Sanjiv J. Shah4, Carlo Sidore5, Edoardo Fiorillo5, Francesco Cucca5,6, Roberto M. Lang3, Dan L. Nicolae1,2,3,7 & Carole Ober1,2 1234567890():,; The impact of the parental origin of associated alleles in GWAS has been largely ignored. Yet sequence variants could affect traits differently depending on whether they are inherited from the mother or the father, as in imprinted regions, where identical inherited DNA sequences can have different effects based on the parental origin. To explore parent-of-origin effects (POEs), we studied 21 quantitative phenotypes in a large Hutterite pedigree to identify variants with single parent (maternal-only or paternal-only) effects, and then variants with opposite parental effects. Here we show that POEs, which can be opposite in direction, are relatively common in humans, have potentially important clinical effects, and will be missed in traditional GWAS. We identified POEs with 11 phenotypes, most of which are risk factors for cardiovascular disease. Many of the loci identified are characteristic of imprinted regions and are associated with the expression of nearby genes. 1 Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 2 Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA. 3 Department of Medicine, University of Chicago, Chicago, IL 60637, USA. 4 Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA. 5 Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato 09042, Italy. -
Multiscale Genomic Analysis of The
University of Tennessee Health Science Center UTHSC Digital Commons Theses and Dissertations (ETD) College of Graduate Health Sciences 5-2009 Multiscale Genomic Analysis of the Corticolimbic System: Uncovering the Molecular and Anatomic Substrates of Anxiety-Related Behavior Khyobeni Mozhui University of Tennessee Health Science Center Follow this and additional works at: https://dc.uthsc.edu/dissertations Part of the Mental and Social Health Commons, Nervous System Commons, and the Neurosciences Commons Recommended Citation Mozhui, Khyobeni , "Multiscale Genomic Analysis of the Corticolimbic System: Uncovering the Molecular and Anatomic Substrates of Anxiety-Related Behavior" (2009). Theses and Dissertations (ETD). Paper 180. http://dx.doi.org/10.21007/etd.cghs.2009.0219. This Dissertation is brought to you for free and open access by the College of Graduate Health Sciences at UTHSC Digital Commons. It has been accepted for inclusion in Theses and Dissertations (ETD) by an authorized administrator of UTHSC Digital Commons. For more information, please contact [email protected]. Multiscale Genomic Analysis of the Corticolimbic System: Uncovering the Molecular and Anatomic Substrates of Anxiety-Related Behavior Document Type Dissertation Degree Name Doctor of Philosophy (PhD) Program Anatomy and Neurobiology Research Advisor Robert W. Williams, Ph.D. Committee John D. Boughter, Ph.D. Eldon E. Geisert, Ph.D. Kristin M. Hamre, Ph.D. Jeffery D. Steketee, Ph.D. DOI 10.21007/etd.cghs.2009.0219 This dissertation is available at UTHSC Digital -
Supplementary Figure 1 Standardization of Gene Expression
Supplementary Figure 1 Standardization of gene expression Notes: (A) Standardization of GSE86544, (B) standardization of GSE103479, (C) standardization of GSE102238, (D) Standardization of GSE7055. The blue bar represents the data before normalization, and the red bar represents the data after normalization. Supplementary Figure 2 Correlation between module eigengenes and clinical traits especially PNI in GSE103479 and GSE102238 datasets. Notes: (A, B) Module-trait relationships in GSE103479 and GSE102238 datasets. The correlation coefficients and corresponding P-values in the brackets are contained in each cell. The table is color- coded by correlation between eigengenes and traits according to the color legend on the right side. The modules with the most significant differences are displayed in brackets. Abbreviations: PNI, perineural invasion. Supplementary Figure 3 The expression values of CCNB2 in pancreatic cancer (GSE102238) and colon cancer (GSE103479). Notes: (A, B) CCNB2 expression values were detected in GSE102238 and GSE103479. Abbreviations: CCNB2, cyclin B2 Supplementary Table 1 Results of top 20 pathway enrichment analysis of GSE7055 Term Category Description Count Log10(P) Genes GO:0000280 GO Biological nuclear division 33 -23.4 BIRC5,BUB1B,CCNB1,CCNE1,CDC20, Processes CKS2,KIF11,MAD2L1,MYBL2,SPAST, TOP2A,TTK,PRC1,PKMYT1,PTTG1,T RIP13,DLGAP5,TACC3,SMC2,SPAG5, UBE2C,ZWINT,TPX2,FBXO5,RACGA P1,NUSAP1,SPDL1,CDCA8,CEP55,ND C1,NSFL1C,KIF18B,ASPM GO:1902850 GO Biological microtubule 15 -12.89 BIRC5,CCNB1,CDC20,KIF11,MAD2L1 Processes