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Table 2. Functional Classification of Genes Differentially Regulated After HOXB4 Inactivation in HSC/Hpcs
Table 2. Functional classification of genes differentially regulated after HOXB4 inactivation in HSC/HPCs Symbol Gene description Fold-change (mean ± SD) Signal transduction Adam8 A disintegrin and metalloprotease domain 8 1.91 ± 0.51 Arl4 ADP-ribosylation factor-like 4 - 1.80 ± 0.40 Dusp6 Dual specificity phosphatase 6 (Mkp3) - 2.30 ± 0.46 Ksr1 Kinase suppressor of ras 1 1.92 ± 0.42 Lyst Lysosomal trafficking regulator 1.89 ± 0.34 Mapk1ip1 Mitogen activated protein kinase 1 interacting protein 1 1.84 ± 0.22 Narf* Nuclear prelamin A recognition factor 2.12 ± 0.04 Plekha2 Pleckstrin homology domain-containing. family A. (phosphoinosite 2.15 ± 0.22 binding specific) member 2 Ptp4a2 Protein tyrosine phosphatase 4a2 - 2.04 ± 0.94 Rasa2* RAS p21 activator protein 2 - 2.80 ± 0.13 Rassf4 RAS association (RalGDS/AF-6) domain family 4 3.44 ± 2.56 Rgs18 Regulator of G-protein signaling - 1.93 ± 0.57 Rrad Ras-related associated with diabetes 1.81 ± 0.73 Sh3kbp1 SH3 domain kinase bindings protein 1 - 2.19 ± 0.53 Senp2 SUMO/sentrin specific protease 2 - 1.97 ± 0.49 Socs2 Suppressor of cytokine signaling 2 - 2.82 ± 0.85 Socs5 Suppressor of cytokine signaling 5 2.13 ± 0.08 Socs6 Suppressor of cytokine signaling 6 - 2.18 ± 0.38 Spry1 Sprouty 1 - 2.69 ± 0.19 Sos1 Son of sevenless homolog 1 (Drosophila) 2.16 ± 0.71 Ywhag 3-monooxygenase/tryptophan 5- monooxygenase activation protein. - 2.37 ± 1.42 gamma polypeptide Zfyve21 Zinc finger. FYVE domain containing 21 1.93 ± 0.57 Ligands and receptors Bambi BMP and activin membrane-bound inhibitor - 2.94 ± 0.62 -
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. -
A Cell Surface Interaction Network of Neural Leucine-Rich Repeat Receptors Christian Söllner*† and Gavin J Wright*
Open Access Research2009SöllnerVolume and 10, IssueWright 9, Article R99 A cell surface interaction network of neural leucine-rich repeat receptors Christian Söllner*† and Gavin J Wright* Addresses: *Cell Surface Signalling Laboratory, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1HH, UK. †Current address: Max Planck Institute for Developmental Biology, Department 3 (Genetics), Spemannstraße 35, 72076 Tübingen, Germany. Correspondence: Christian Söllner. Email: [email protected]. Gavin J Wright. Email: [email protected] Published: 18 September 2009 Received: 9 June 2009 Revised: 18 August 2009 Genome Biology 2009, 10:R99 (doi:10.1186/gb-2009-10-9-r99) Accepted: 18 September 2009 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2009/10/9/R99 © 2009 Söllner and Wright; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An<p>A extracellular network of neuroreceptor Zebrafish extracellular interaction neuroreceptor network interactions are revealed using AVEXIS, a highly stringent interaction assay.</p> Abstract Background: The vast number of precise intercellular connections within vertebrate nervous systems is only partly explained by the comparatively few known extracellular guidance cues. Large families -
Global H3k4me3 Genome Mapping Reveals Alterations of Innate Immunity Signaling and Overexpression of JMJD3 in Human Myelodysplastic Syndrome CD34 Þ Cells
Leukemia (2013) 27, 2177–2186 & 2013 Macmillan Publishers Limited All rights reserved 0887-6924/13 www.nature.com/leu ORIGINAL ARTICLE Global H3K4me3 genome mapping reveals alterations of innate immunity signaling and overexpression of JMJD3 in human myelodysplastic syndrome CD34 þ cells YWei1, R Chen2, S Dimicoli1, C Bueso-Ramos3, D Neuberg4, S Pierce1, H Wang2, H Yang1, Y Jia1, H Zheng1, Z Fang1, M Nguyen3, I Ganan-Gomez1,5, B Ebert6, R Levine7, H Kantarjian1 and G Garcia-Manero1 The molecular bases of myelodysplastic syndromes (MDS) are not fully understood. Trimethylated histone 3 lysine 4 (H3K4me3) is present in promoters of actively transcribed genes and has been shown to be involved in hematopoietic differentiation. We performed a genome-wide H3K4me3 CHIP-Seq (chromatin immunoprecipitation coupled with whole genome sequencing) analysis of primary MDS bone marrow (BM) CD34 þ cells. This resulted in the identification of 36 genes marked by distinct higher levels of promoter H3K4me3 in MDS. A majority of these genes are involved in nuclear factor (NF)-kB activation and innate immunity signaling. We then analyzed expression of histone demethylases and observed significant overexpression of the JmjC-domain histone demethylase JMJD3 (KDM6b) in MDS CD34 þ cells. Furthermore, we demonstrate that JMJD3 has a positive effect on transcription of multiple CHIP-Seq identified genes involved in NF-kB activation. Inhibition of JMJD3 using shRNA in primary BM MDS CD34 þ cells resulted in an increased number of erythroid colonies in samples isolated from patients with lower-risk MDS. Taken together, these data indicate the deregulation of H3K4me3 and associated abnormal activation of innate immunity signals have a role in the pathogenesis of MDS and that targeting these signals may have potential therapeutic value in MDS. -
Human Blastocysts of Normal and Abnormal Karyotypes Display Distinct Transcriptome Profles Received: 16 March 2018 Frederick Licciardi1, Tenzin Lhakhang2, Yael G
www.nature.com/scientificreports OPEN Human blastocysts of normal and abnormal karyotypes display distinct transcriptome profles Received: 16 March 2018 Frederick Licciardi1, Tenzin Lhakhang2, Yael G. Kramer3, Yutong Zhang4, Adriana Heguy4,5,6 & Accepted: 26 September 2018 Aristotelis Tsirigos 2,5,6 Published: xx xx xxxx Unveiling the transcriptome of human blastocysts can provide a wealth of important information regarding early embryonic ontology. Comparing the mRNA production of embryos with normal and abnormal karyotypes allows for a deeper understanding of the protein pathways leading to viability and aberrant fetal development. In addition, identifying transcripts specifc for normal or abnormal chromosome copy number could aid in the search for secreted substances that could be used to non- invasively identify embryos best suited for IVF embryo transfer. Using RNA-seq, we characterized the transcriptome of 71 normally developing human blastocysts that were karyotypically normal vs. trisomic or monosomic. Every monosomy and trisomy of the autosomal and sex chromosomes were evaluated, mostly in duplicate. We frst mapped the transcriptome of three normal embryos and found that a common core of more than 3,000 genes is expressed in all embryos. These genes represent pathways related to actively dividing cells, such as ribosome biogenesis and function, spliceosome, oxidative phosphorylation, cell cycle and metabolic pathways. We then compared transcriptome profles of aneuploid embryos to those of normal embryos. We observed that non-viable embryos had a large number of dysregulated genes, some showing a hundred-fold diference in expression. On the contrary, sex chromosome abnormalities, XO and XXX displayed transcriptomes more closely mimicking those embryos with 23 normal chromosome pairs. -
Multivariate Analysis Reveals Genetic Associations of the Resting Default
Multivariate analysis reveals genetic associations of PNAS PLUS the resting default mode network in psychotic bipolar disorder and schizophrenia Shashwath A. Medaa,1, Gualberto Ruañob,c, Andreas Windemuthb, Kasey O’Neila, Clifton Berwisea, Sabra M. Dunna, Leah E. Boccaccioa, Balaji Narayanana, Mohan Kocherlab, Emma Sprootena, Matcheri S. Keshavand, Carol A. Tammingae, John A. Sweeneye, Brett A. Clementzf, Vince D. Calhoung,h,i, and Godfrey D. Pearlsona,h,j aOlin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT 06102; bGenomas Inc., Hartford, CT 06102; cGenetics Research Center, Hartford Hospital, Hartford, CT 06102; dDepartment of Psychiatry, Beth Israel Deaconess Hospital, Harvard Medical School, Boston, MA 02215; eDepartment of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390; fDepartment of Psychology, University of Georgia, Athens, GA 30602; gThe Mind Research Network, Albuquerque, NM 87106; Departments of hPsychiatry and jNeurobiology, Yale University, New Haven, CT 06520; and iDepartment of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM 87106 Edited by Robert Desimone, Massachusetts Institute of Technology, Cambridge, MA, and approved April 4, 2014 (received for review July 15, 2013) The brain’s default mode network (DMN) is highly heritable and is Although risk for psychotic illnesses is driven in small part by compromised in a variety of psychiatric disorders. However, ge- highly penetrant, often private mutations such as copy number netic control over the DMN in schizophrenia (SZ) and psychotic variants, substantial risk also is likely conferred by multiple genes bipolar disorder (PBP) is largely unknown. Study subjects (n = of small effect sizes interacting together (7). According to the 1,305) underwent a resting-state functional MRI scan and were “common disease common variant” (CDCV) model, one would analyzed by a two-stage approach. -
45568 ROBO2 (E4M6D) Rabbit Mab
Revision 1 C 0 2 - t ROBO2 (E4M6D) Rabbit mAb a e r o t S Orders: 877-616-CELL (2355) [email protected] 8 Support: 877-678-TECH (8324) 6 5 Web: [email protected] 5 www.cellsignal.com 4 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source/Isotype: UniProt ID: Entrez-Gene Id: WB, IP, IF-IC H M R Endogenous 180, 220 Rabbit IgG Q9HCK4 6092 Product Usage Information Application Dilution Western Blotting 1:1000 Immunoprecipitation 1:50 Immunofluorescence (Immunocytochemistry) 1:1600 Storage Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA, 50% glycerol and less than 0.02% sodium azide. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity ROBO2 (E4M6D) Rabbit mAb recognizes endogenous levels of total ROBO2 protein. Species Reactivity: Human, Mouse, Rat Source / Purification Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Leu1172 of human ROBO2 protein. Background ROBO2 is a member of the roundabout (ROBO) receptor family (1). The activation of ROBO2 by SLIT ligand regulates various biological processes, including promoting stem cell senescence via WNT inhibition, destabilizing podocyte actin polymerization and adhesion, and activation of Ena/VASP to facilitate tumor cell extrusion from epithelia (2- 5). In development, the SLIT-ROBO pathways play important roles in neuronal axon guidance and synapse function, retinal neurovascular formation, and muscle patterning (6- 9). Loss of function mutations of ROBO2 have been associated with urinary tract anomalies and vesicoureteral reflux (10). -
COX7A2L Rabbit Pab
Leader in Biomolecular Solutions for Life Science COX7A2L Rabbit pAb Catalog No.: A8298 Basic Information Background Catalog No. Cytochrome c oxidase (COX), the terminal component of the mitochondrial respiratory A8298 chain, catalyzes the electron transfer from reduced cytochrome c to oxygen. This component is a heteromeric complex consisting of 3 catalytic subunits encoded by Observed MW mitochondrial genes and multiple structural subunits encoded by nuclear genes. The 13KDa mitochondrially-encoded subunits function in electron transfer, and the nuclear- encoded subunits may function in the regulation and assembly of the complex. This Calculated MW nuclear gene encodes a protein similar to polypeptides 1 and 2 of subunit VIIa in the C- 12kDa terminal region, and also highly similar to the mouse Sig81 protein sequence. This gene is expressed in all tissues, and upregulated in a breast cancer cell line after estrogen Category treatment. It is possible that this gene represents a regulatory subunit of COX and mediates the higher level of energy production in target cells by estrogen. Several Primary antibody transcript variants, some protein-coding and others non-protein coding, have been found for this gene. Applications WB Cross-Reactivity Human, Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 9167 O14548 Immunogen Recombinant fusion protein containing a sequence corresponding to amino acids 1-114 of human COX7A2L (NP_004709.2). Synonyms COX7A2L;COX7AR;COX7RP;EB1;SIG81 Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. -
Identification of ROBO2 As a Potential Locus Associated with Inhaled
Journal of Personalized Medicine Article Identification of ROBO2 as a Potential Locus Associated with Inhaled Corticosteroid Response in Childhood Asthma Natalia Hernandez-Pacheco 1,2,3,*,†, Mario Gorenjak 4 , Jiang Li 5 , Katja Repnik 4,6, Susanne J. Vijverberg 7,8,9, Vojko Berce 4,10 , Andrea Jorgensen 11, Leila Karimi 12 , Maximilian Schieck 13,14, Lesly-Anne Samedy-Bates 15,16, Roger Tavendale 17, Jesús Villar 3,18,19 , Somnath Mukhopadhyay 17,20, Munir Pirmohamed 21 , Katia M. C. Verhamme 12, Michael Kabesch 13, Daniel B. Hawcutt 22,23, Steve Turner 24 , Colin N. Palmer 17, Kelan G. Tantisira 5,25, Esteban G. Burchard 15,16, Anke H. Maitland-van der Zee 7,8,9 , Carlos Flores 1,3,26,27 , Uroš Potoˇcnik 4,6,*,‡ and Maria Pino-Yanes 2,3,27,‡ 1 Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Carretera General del Rosario 145, 38010 Santa Cruz de Tenerife, Spain; cfl[email protected] 2 Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, Avenida Astrofísico Francisco Sánchez s/n, Faculty of Science, Apartado 456, 38200 San Cristóbal de La Laguna, Spain; [email protected] 3 CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Avenida de Monforte de Lemos, 5, 28029 Madrid, Spain; [email protected] 4 Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia; [email protected] (M.G.); [email protected] (K.R.); [email protected] -
Identification of Key Pathways and Genes in Dementia Via Integrated Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. 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. Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. 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 To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis. -
Genome Sequencing Unveils a Regulatory Landscape of Platelet Reactivity
ARTICLE https://doi.org/10.1038/s41467-021-23470-9 OPEN Genome sequencing unveils a regulatory landscape of platelet reactivity Ali R. Keramati 1,2,124, Ming-Huei Chen3,4,124, Benjamin A. T. Rodriguez3,4,5,124, Lisa R. Yanek 2,6, Arunoday Bhan7, Brady J. Gaynor 8,9, Kathleen Ryan8,9, Jennifer A. Brody 10, Xue Zhong11, Qiang Wei12, NHLBI Trans-Omics for Precision (TOPMed) Consortium*, Kai Kammers13, Kanika Kanchan14, Kruthika Iyer14, Madeline H. Kowalski15, Achilleas N. Pitsillides4,16, L. Adrienne Cupples 4,16, Bingshan Li 12, Thorsten M. Schlaeger7, Alan R. Shuldiner9, Jeffrey R. O’Connell8,9, Ingo Ruczinski17, Braxton D. Mitchell 8,9, ✉ Nauder Faraday2,18, Margaret A. Taub17, Lewis C. Becker1,2, Joshua P. Lewis 8,9,125 , 2,14,125✉ 3,4,125✉ 1234567890():,; Rasika A. Mathias & Andrew D. Johnson Platelet aggregation at the site of atherosclerotic vascular injury is the underlying patho- physiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes. -
Supplementary Information.Pdf
Supplementary Information Whole transcriptome profiling reveals major cell types in the cellular immune response against acute and chronic active Epstein‐Barr virus infection Huaqing Zhong1, Xinran Hu2, Andrew B. Janowski2, Gregory A. Storch2, Liyun Su1, Lingfeng Cao1, Jinsheng Yu3, and Jin Xu1 Department of Clinical Laboratory1, Children's Hospital of Fudan University, Minhang District, Shanghai 201102, China; Departments of Pediatrics2 and Genetics3, Washington University School of Medicine, Saint Louis, Missouri 63110, United States. Supplementary information includes the following: 1. Supplementary Figure S1: Fold‐change and correlation data for hyperactive and hypoactive genes. 2. Supplementary Table S1: Clinical data and EBV lab results for 110 study subjects. 3. Supplementary Table S2: Differentially expressed genes between AIM vs. Healthy controls. 4. Supplementary Table S3: Differentially expressed genes between CAEBV vs. Healthy controls. 5. Supplementary Table S4: Fold‐change data for 303 immune mediators. 6. Supplementary Table S5: Primers used in qPCR assays. Supplementary Figure S1. Fold‐change (a) and Pearson correlation data (b) for 10 cell markers and 61 hypoactive and hyperactive genes identified in subjects with acute EBV infection (AIM) in the primary cohort. Note: 23 up‐regulated hyperactive genes were highly correlated positively with cytotoxic T cell (Tc) marker CD8A and NK cell marker CD94 (KLRD1), and 38 down‐regulated hypoactive genes were highly correlated positively with B cell, conventional dendritic cell