1 Myc Is Required for Adaptive ß-Cell Replication in Young
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Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
Analysis and Characterisation of the Mouse Hic2 Gene
Aus dem Institut für Entwicklungsgenetik des GSF-Forschungszentrums für Umwelt und Gesundheit, GmbH Direktor: Prof. Dr. Wolfgang Wurst Anfertigung unter der Leitung von Prof. Dr. Jochen Graw Vorgelegt über den Lehrstuhl für Molekulare Tierzucht und Biotechnologie Der Tierärztlichen Fakultät der Ludwig-Maximilians-Universität München Vorstand: Prof. Dr. Eckhard Wolf Untersuchung und Charakterisierung des Hic2-Gens der Maus Inaugural-Dissertation Zur Erlangung der tiermedizinischen Doktorwürde der Tierärztlichen Fakultät der Ludwig-Maximilians-Universität München von Aleksandra Terzic aus Sarajevo/Bosnia und Herzegowina München 2004 II Aus dem Institut für Entwicklungsgenetik des GSF-Forschungszentrums für Umwelt und Gesundheit, GmbH Direktor: Prof. Dr. Wolfgang Wurst Anfertigung unter der Leitung von Prof. Dr. Jochen Graw Vorgelegt über den Lehrstuhl für Molekulare Tierzucht und Biotechnologie Der Tierärztlichen Fakultät der Ludwig-Maximilians-Universität München Vorstand: Prof. Dr. Eckhard Wolf Analysis and characterisation of the mouse Hic2 gene Inaugural-Dissertation Zur Erlangung der tiermedizinischen Doktorwürde der Tierärztlichen Fakultät der Ludwig-Maximilians-Universität München von Aleksandra Terzic aus Sarajevo/Bosnia und Herzegowina München 2004 III Gedruckt mit Genehmigung der Tierärztlichen Fakultät der Ludwig-Maximilians-Universität München Dekan: Univ.-Prof. Dr. A.Stolle Referent: Univ.-Prof. Dr. E. Wolf Korreferent: Univ.-Prof. Dr. K. Heinritzi Tag der Promotion: 13. Februar 2004 IV List of contents 1 INTRODUCTION.............................................................................................................1 -
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). -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Transcriptional Control of Tissue-Resident Memory T Cell Generation
Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells. -
Integrated Analysis of Differentially Expressed Genes in Breast Cancer Pathogenesis
2560 ONCOLOGY LETTERS 9: 2560-2566, 2015 Integrated analysis of differentially expressed genes in breast cancer pathogenesis DAOBAO CHEN and HONGJIAN YANG Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, P.R. China Received October 20, 2014; Accepted March 10, 2015 DOI: 10.3892/ol.2015.3147 Abstract. The present study aimed to detect the differences ducts or from the lobules that supply the ducts (1). Breast between breast cancer cells and normal breast cells, and inves- cancer affects ~1.2 million women worldwide and accounts tigate the potential pathogenetic mechanisms of breast cancer. for ~50,000 mortalities every year (2). Despite major advances The sample GSE9574 series was downloaded, and the micro- in surgical and nonsurgical management of the disease, breast array data was analyzed to identify differentially expressed cancer metastasis remains a significant clinical challenge genes (DEGs). Gene Ontology (GO) cluster analysis using affecting numerous of patients (3). The prognosis and survival the GO Enrichment Analysis Software Toolkit platform and rates for breast cancer are highly variable, and depend on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway the cancer type, treatment strategy, stage of the disease and analysis for DEGs was conducted using the Gene Set Analysis geographical location of the patient (4). Toolkit V2. In addition, a protein-protein interaction (PPI) Microarray technology, which may be used to simultane- network was constructed, and target sites of potential transcrip- ously interrogate 10,000-40,000 genes, has provided new tion factors and potential microRNA (miRNA) molecules were insight into the molecular classification of different cancer screened. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
Physiological and Pathophysiological Regulation of the Ryanodine Receptor in Skeletal Muscle
Physiological and pathophysiological regulation of the ryanodine receptor in skeletal muscle Alisa Umanskaya Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2015 © 2015 Alisa Umanskaya All rights reserved Abstract Physiological and pathophysiological regulation of ryanodine receptor in skeletal muscle Alisa Umanskaya Ryanodine receptor calcium release channels are essential for skeletal muscle contraction, as they mediate the release of calcium ions from intracellular stores into the cytosol. The data presented in this dissertation demonstrate the evolutionarily conserved mechanisms of skeletal muscle ryanodine receptor regulation in the physiological and pathophysiological states. Adrenergic stimulation causes increased skeletal muscle force, however, despite the well- established role of this physiological response, the molecular mechanism is not known. Here we present a mechanism whereby phosphorylation of a single amino acid on the ryanodine receptor is a key signal in the physiological stress-induced inotropic response in mouse skeletal muscle. Therefore acute post-translational modifications of ryanodine receptor channels are important for healthy muscle contraction. Conversely, chronic stress-induced post-translational modifications result in poorly functioning murine ryanodine receptor channels that contribute to skeletal muscle dysfunction in age- dependent skeletal muscle weakness and Muscular Dystrophies. Finally, we present data that demonstrates striking evolutionary conservation in ryanodine receptor regulation in the physiological and pathophysiological states between mice and C. elegans. This work has broad implications for understanding the underlying mechanisms of skeletal muscle contraction and important disorders that affect human health. Furthermore, this works presents ryanodine receptor channels as a viable therapeutic target for age-related skeletal muscle weakness, Muscular Dystrophies, and also implicates C. -
GATA2 Regulates Mast Cell Identity and Responsiveness to Antigenic Stimulation by Promoting Chromatin Remodeling at Super- Enhancers
ARTICLE https://doi.org/10.1038/s41467-020-20766-0 OPEN GATA2 regulates mast cell identity and responsiveness to antigenic stimulation by promoting chromatin remodeling at super- enhancers Yapeng Li1, Junfeng Gao 1, Mohammad Kamran1, Laura Harmacek2, Thomas Danhorn 2, Sonia M. Leach1,2, ✉ Brian P. O’Connor2, James R. Hagman 1,3 & Hua Huang 1,3 1234567890():,; Mast cells are critical effectors of allergic inflammation and protection against parasitic infections. We previously demonstrated that transcription factors GATA2 and MITF are the mast cell lineage-determining factors. However, it is unclear whether these lineage- determining factors regulate chromatin accessibility at mast cell enhancer regions. In this study, we demonstrate that GATA2 promotes chromatin accessibility at the super-enhancers of mast cell identity genes and primes both typical and super-enhancers at genes that respond to antigenic stimulation. We find that the number and densities of GATA2- but not MITF-bound sites at the super-enhancers are several folds higher than that at the typical enhancers. Our studies reveal that GATA2 promotes robust gene transcription to maintain mast cell identity and respond to antigenic stimulation by binding to super-enhancer regions with dense GATA2 binding sites available at key mast cell genes. 1 Department of Immunology and Genomic Medicine, National Jewish Health, Denver, CO 80206, USA. 2 Center for Genes, Environment and Health, National Jewish Health, Denver, CO 80206, USA. 3 Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, ✉ CO 80045, USA. email: [email protected] NATURE COMMUNICATIONS | (2021) 12:494 | https://doi.org/10.1038/s41467-020-20766-0 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-20766-0 ast cells (MCs) are critical effectors in immunity that at key MC genes. -
Genetically Enhancing Mitochondrial Antioxidant Activity Improves Muscle Function in Aging
Genetically enhancing mitochondrial antioxidant activity improves muscle function in aging Alisa Umanskayaa,1,GaetanoSantullia,1,WenjunXiea,1, Daniel C. Anderssona,StevenR.Reikena, and Andrew R. Marksa,b,2 aDepartment of Physiology and Cellular Biophysics, The Clyde and Helen Wu Center for Molecular Cardiology, Columbia University College of Physiciansand Surgeons, New York, NY 10032; and bDepartment of Medicine, Columbia University College of Physicians and Surgeons, New York, NY 10032 Edited by Eric N. Olson, University of Texas Southwestern Medical Center, Dallas, TX, and approved September 15, 2014 (received for review July 7, 2014) Age-related skeletal muscle dysfunction is a leading cause of reduction in skeletal muscle specific force (10). Acute induction + morbidity that affects up to half the population aged 80 or greater. of RyR1-mediated SR Ca2 leak with rapamycin, which com- Here we tested the effects of increased mitochondrial antioxidant petes the channel-stabilizing subunit, calstabin1, off from RyR1 activity on age-dependent skeletal muscle dysfunction using trans- (14, 16), resulted in defective mitochondrial function associated genic mice with targeted overexpression of the human catalase with elevated free radical production (10). However, the role of gene to mitochondria (MCat mice). Aged MCat mice exhibited mitochondrial ROS in age-dependent reduction in skeletal improved voluntary exercise, increased skeletal muscle specific force muscle function and exercise capacity has not been elucidated. and tetanic Ca2+ transients, decreased intracellular Ca2+ leak and Recently, there have been numerous efforts to study mito- + increased sarcoplasmic reticulum (SR) Ca2 load compared with chondria-derived free radicals in health and lifespan by experi- age-matched wild type (WT) littermates. -
Table S1. Detailed Gene Lists Related with Glaucoma, Sclera and Extracellular Matrix (ECM) Remodeling from Text Mining
Table S1. Detailed gene lists related with Glaucoma, Sclera and Extracellular matrix (ECM) remodeling from text mining. Glaucoma related genes Sclera related genes ECM remodeling related genes Common genes GSTM1 NID1 AKT3 NID1 TAP2 EPRS NID1 TGFB2 LGALS8 TGFB2 ACTA1 HSD11B1 NID1 PROX1 PARP1 CD34 TBCE IRF6 MIXL1 REN MIXL1 HSD11B1 TGFB2 DES LBR PLXNA2 PROX1 MPZ EPRS CD34 HSD11B1 ABCB6 TGFB2 CD46 CD34 FN1 IRF6 CR2 IL10 RPE HSD11B1 CD55 REN SST PLXNA2 REN FMOD KNG1 CD34 FMOD CHI3L1 VCAM1 CD46 PRELP CHIT1 F3 IL10 ADORA1 AGXT SOD2 NFASC CFH GPC1 RHO REN PRG4 PTPRC SHH OPTC MYOC COL6A3 NOS3 CHI3L1 SELE PTGS2 GJA1 RNPEP SLC4A3 LAMC2 TIMP2 LMOD1 DES SOAT1 MKI67 PER2 MPZ FASLG APOA1 PTPRC ABCB6 SERPINE2 CALD1 PRLH USF1 SELP C2 CRB1 CD244 POU2F1 VAMP8 CFH VANGL2 DES MMP3 PTGS2 FN1 DDR2 MMP1 PDC NES MPZ GH1 C1orf27 RPE ABCB6 SLC17A5 DHX9 TFPI ADAMTS4 NOVA2 LAMC1 NBPF14 IHH DMPK SOAT1 SST CYP27A1 NOS1 PSMD1 OLFML3 NHLH1 ACE PRDX6 KNG1 CRP PVR TNFSF4 CRYGS IGFBP5 SERPINE1 FASLG ADORA3 NTRK1 ACHE MYOC GCG FN1 ENG COL4A3 VCAM1 NES BGN SELE F3 RPE CD79A SELL MBNL1 CREB1 GALNS F5 PLG MUC1 TGFB1 SELP SOD2 NRP2 VEGFA ATP1B1 RHO SHC1 HSPA5 DPT VIPR2 BMPR2 COL1A1 SLC4A3 B3GAT1 SUMO1 IGF1 DES NLRP5 CASP8 GSN PBX1 SHH S100A1 COL1A2 MPZ LIMS1 S100A4 DCN ABCB6 NOS3 FLG LUM USF1 HGS CTSK TNC CD244 HGD CTSS GFAP VANGL2 GJA1 ECM1 CDH1 PEX19 TIMP2 TFPI CDKN1A ATP1A2 MKI67 CALCRL PLAU PNKD KLK1 ITGAV MFAP2 CRP APOA1 MFI2 CTNNB1 DARC TFG TTN C10orf27 IGFBP2 CALD1 ATF2 ELN NTRK1 C2 NGF PTCH1 FN1 PAEP TP63 MMP2 LMNA TSPAN33 SST CNOT8 RPE CNTN3 ITGA6 EDN1 MAP2 DBH ADIPOQ -
Genetic Background of Ataxia in Children Younger Than 5 Years in Finland E444
Volume 6, Number 4, August 2020 Neurology.org/NG A peer-reviewed clinical and translational neurology open access journal ARTICLE Genetic background of ataxia in children younger than 5 years in Finland e444 ARTICLE Cerebral arteriopathy associated with heterozygous variants in the casitas B-lineage lymphoma gene e448 ARTICLE Somatic SLC35A2 mosaicism correlates with clinical fi ndings in epilepsy brain tissuee460 ARTICLE Synonymous variants associated with Alzheimer disease in multiplex families e450 Academy Officers Neurology® is a registered trademark of the American Academy of Neurology (registration valid in the United States). James C. Stevens, MD, FAAN, President Neurology® Genetics (eISSN 2376-7839) is an open access journal published Orly Avitzur, MD, MBA, FAAN, President Elect online for the American Academy of Neurology, 201 Chicago Avenue, Ann H. Tilton, MD, FAAN, Vice President Minneapolis, MN 55415, by Wolters Kluwer Health, Inc. at 14700 Citicorp Drive, Bldg. 3, Hagerstown, MD 21742. Business offices are located at Two Carlayne E. Jackson, MD, FAAN, Secretary Commerce Square, 2001 Market Street, Philadelphia, PA 19103. Production offices are located at 351 West Camden Street, Baltimore, MD 21201-2436. Janis M. Miyasaki, MD, MEd, FRCPC, FAAN, Treasurer © 2020 American Academy of Neurology. Ralph L. Sacco, MD, MS, FAAN, Past President Neurology® Genetics is an official journal of the American Academy of Neurology. Journal website: Neurology.org/ng, AAN website: AAN.com CEO, American Academy of Neurology Copyright and Permission Information: Please go to the journal website (www.neurology.org/ng) and click the Permissions tab for the relevant Mary E. Post, MBA, CAE article. Alternatively, send an email to [email protected].