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Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent -
Analysis of Gene–Environment Interactions in Postnatal
– Analysis of gene environment interactions in postnatal INAUGURAL ARTICLE development of the mammalian intestine Seth Rakoff-Nahouma,b,c,1, Yong Kongd, Steven H. Kleinsteine, Sathish Subramanianf, Philip P. Ahernf, Jeffrey I. Gordonf, and Ruslan Medzhitova,b,1 aHoward Hughes Medical Institute, bDepartment of Immunobiology, dDepartment of Molecular Biophysics and Biochemistry, W. M. Keck Foundation Biotechnology Resource Laboratory, and eInterdepartmental Program in Computational Biology and Bioinformatics and Department of Pathology, Yale University School of Medicine, New Haven, CT 06510; fCenter for Genome Sciences and Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108; and cDivision of Infectious Diseases, Department of Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115 This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2010. Contributed by Ruslan Medzhitov, December 31, 2014 (sent for review December 25, 2014; reviewed by Alexander V. Chervonsky and Alexander Y. Rudensky) Unlike mammalian embryogenesis, which takes place in the relatively immediately after birth, the intestine is exposed to mother’s milk predictable and stable environment of the uterus, postnatal develop- and undergoes initial colonization with microorganisms. Second, ment can be affected by a multitude of highly variable environmental after weaning, the intestinal tract becomes exposed to solid foods factors, including diet, exposure to noxious substances, and micro- and is no longer exposed to mother’s milk components, the host organisms. Microbial colonization of the intestine is thought to play a immune system matures, and the microbiota shifts. particularly important role in postnatal development of the gastroin- Although it is widely recognized that these transitions have testinal, metabolic, and immune systems. -
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. -
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). -
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 -
Supplemental Table S1. Primers for Sybrgreen Quantitative RT-PCR Assays
Supplemental Table S1. Primers for SYBRGreen quantitative RT-PCR assays. Gene Accession Primer Sequence Length Start Stop Tm GC% GAPDH NM_002046.3 GAPDH F TCCTGTTCGACAGTCAGCCGCA 22 39 60 60.43 59.09 GAPDH R GCGCCCAATACGACCAAATCCGT 23 150 128 60.12 56.52 Exon junction 131/132 (reverse primer) on template NM_002046.3 DNAH6 NM_001370.1 DNAH6 F GGGCCTGGTGCTGCTTTGATGA 22 4690 4711 59.66 59.09% DNAH6 R TAGAGAGCTTTGCCGCTTTGGCG 23 4797 4775 60.06 56.52% Exon junction 4790/4791 (reverse primer) on template NM_001370.1 DNAH7 NM_018897.2 DNAH7 F TGCTGCATGAGCGGGCGATTA 21 9973 9993 59.25 57.14% DNAH7 R AGGAAGCCATGTACAAAGGTTGGCA 25 10073 10049 58.85 48.00% Exon junction 9989/9990 (forward primer) on template NM_018897.2 DNAI1 NM_012144.2 DNAI1 F AACAGATGTGCCTGCAGCTGGG 22 673 694 59.67 59.09 DNAI1 R TCTCGATCCCGGACAGGGTTGT 22 822 801 59.07 59.09 Exon junction 814/815 (reverse primer) on template NM_012144.2 RPGRIP1L NM_015272.2 RPGRIP1L F TCCCAAGGTTTCACAAGAAGGCAGT 25 3118 3142 58.5 48.00% RPGRIP1L R TGCCAAGCTTTGTTCTGCAAGCTGA 25 3238 3214 60.06 48.00% Exon junction 3124/3125 (forward primer) on template NM_015272.2 Supplemental Table S2. Transcripts that differentiate IPF/UIP from controls at 5%FDR Fold- p-value Change Transcript Gene p-value p-value p-value (IPF/UIP (IPF/UIP Cluster ID RefSeq Symbol gene_assignment (Age) (Gender) (Smoking) vs. C) vs. C) NM_001178008 // CBS // cystathionine-beta- 8070632 NM_001178008 CBS synthase // 21q22.3 // 875 /// NM_0000 0.456642 0.314761 0.418564 4.83E-36 -2.23 NM_003013 // SFRP2 // secreted frizzled- 8103254 NM_003013 -
UC San Diego UC San Diego Electronic Theses and Dissertations
UC San Diego UC San Diego Electronic Theses and Dissertations Title Insights from reconstructing cellular networks in transcription, stress, and cancer Permalink https://escholarship.org/uc/item/6s97497m Authors Ke, Eugene Yunghung Ke, Eugene Yunghung Publication Date 2012 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Insights from Reconstructing Cellular Networks in Transcription, Stress, and Cancer A dissertation submitted in the partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics and Systems Biology by Eugene Yunghung Ke Committee in charge: Professor Shankar Subramaniam, Chair Professor Inder Verma, Co-Chair Professor Web Cavenee Professor Alexander Hoffmann Professor Bing Ren 2012 The Dissertation of Eugene Yunghung Ke is approved, and it is acceptable in quality and form for the publication on microfilm and electronically ________________________________________________________________ ________________________________________________________________ ________________________________________________________________ ________________________________________________________________ Co-Chair ________________________________________________________________ Chair University of California, San Diego 2012 iii DEDICATION To my parents, Victor and Tai-Lee Ke iv EPIGRAPH [T]here are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there -
Anti-Proliferative Effect of Estrogen in Breast Cancer Cells That Re-Express Erα Is Mediated by Aberrant Regulation of Cell
535 Anti-proliferative effect of estrogen in breast cancer cells that re-express ER is mediated by aberrant regulation of cell cycle genes J G Moggs, T C Murphy, F L Lim, D J Moore, R Stuckey, K Antrobus, I Kimber and G Orphanides Syngenta CTL, Alderley Park, Cheshire SK10 4TJ, UK (Requests for offprints should be addressed to J G Moggs; Email: [email protected]) Abstract Estrogen receptor (ER)-negative breast carcinomas do not respond to hormone therapy, making their effective treatment very difficult. The re-expression of ER in ER-negative MDA-MB-231 breast cancer cells has been used as a model system, in which hormone-dependent responses can be restored. Paradoxically, in contrast to the mitogenic activity of 17-estradiol (E2) in ER-positive breast cancer cells, E2 suppresses proliferation in ER-negative breast cancer cells in which ER has been re-expressed. We have used global gene expression profiling to investigate the mechanism by which E2 suppresses proliferation in MDA-MB-231 cells that express ER through adenoviral infection. We show that a number of genes known to promote cell proliferation and survival are repressed by E2 in these cells. These include genes encoding the anti-apoptosis factor SURVIVIN, positive cell cycle regulators (CDC2, CYCLIN B1, CYCLIN B2, CYCLIN G1, CHK1, BUB3, STK6, SKB1, CSE1 L) and chromosome replication proteins (MCM2, MCM3, FEN1, RRM2, TOP2A, RFC1). In parallel, E2-induced the expression of the negative cell cycle regulators KIP2 and QUIESCIN Q6, and the tumour-suppressor genes E-CADHERIN and NBL1. Strikingly, the expression of several of these genes is regulated in the opposite direction by E2 compared with their regulation in ER-positive MCF-7 cells. -
Regulation of Hepatic Drug-Metabolizing Enzymes in Germ-Free Mice by Conventionalization and Probiotics S
Supplemental material to this article can be found at: http://dmd.aspetjournals.org/content/suppl/2015/11/19/dmd.115.067504.DC1 1521-009X/44/2/262–274$25.00 http://dx.doi.org/10.1124/dmd.115.067504 DRUG METABOLISM AND DISPOSITION Drug Metab Dispos 44:262–274, February 2016 Copyright ª 2016 by The American Society for Pharmacology and Experimental Therapeutics Regulation of Hepatic Drug-Metabolizing Enzymes in Germ-Free Mice by Conventionalization and Probiotics s Felcy Pavithra Selwyn, Sunny Lihua Cheng, Curtis D. Klaassen, and Julia Yue Cui Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington Received September 30, 2015; accepted November 18, 2015 ABSTRACT Little is known regarding the effect of intestinal microbiota modi- 4a gene expression correlated with alterations in the pregnane X fiers, such as probiotics and conventionalization with exogenous receptor and peroxisome proliferator–activated receptor a–DNA bacteria, on host hepatic drug metabolism. Therefore, the goal of binding, respectively (chromatin immunoprecipitation–qPCR). VSL3 this study was to determine the effect of these modifiers on the increased each bacterial component in the large intestinal content Downloaded from expression of various drug-metabolizing enzymes of the host liver. of the CV mice, and increased these bacteria even more in GF mice, VSL3 is a probiotic that contains eight live strains of bacteria. Five likely due to less competition for growth in the GF environ- groups of mice were used: 1) conventional mice (CV), 2) conventional ment. VSL3 given to conventional mice increased the mRNAs of mice treated with VSL3 in drinking water, 3) germ-free (GF) mice, 4) Cyp4v3, alcohol dehydrogenase 1, and carboxyesterase 2a, but GF mice treated with VSL3, and 5) GF mice exposed to the decreased the mRNAs of multiple phase II glutathione-S-transfer- conventional environment for 2 months. -
Supplementary Tables 1-18 Contain the Predictive Signatures Determined for Each AML Subgroup Using Prediction Analysis for Microarrays (PAM)
SUPPLEMENTARY TABLES Supplementary Tables 1-18 contain the predictive signatures determined for each AML subgroup using Prediction Analysis for Microarrays (PAM). Each row indicates probe set ID , gene symbol and abnormality. In addition, two scores are indicated, which represent the standardized centroids for each class for probe sets surviving at the chosen threshold. By definition, cases with the abnormality are coded as class "2" and cases without the abnormality as "1". Supplementary Table 19 contains class prediction data using Prediction Analysis for Microarrays within normal karyotype AML. Table 1. Predictive signature AML and inv(16) Probe set ID Gene symbol Abnormality 1.score 2.score 1 201497_x_at MYH11 inv(16) -0.0793 0.8532 2 207961_x_at MYH11 inv(16) -0.0089 0.0959 Table 2. Predictive signature AML and t(15;17) Probe set ID Gene symbol Abnormality 1.score 2.score 1 226210_s_at MEG3 t(15;17) -0.0506 0.6432 2 205110_s_at FGF13 t(15;17) -0.0326 0.4151 3 210998_s_at HGF t(15;17) -0.0262 0.3333 4 210997_at HGF t(15;17) -0.0179 0.2276 5 223828_s_at LGALS12 t(15;17) -0.0178 0.2267 6 210794_s_at MEG3 t(15;17) -0.0127 0.1611 7 204537_s_at GABRE t(15;17) -0.0064 0.081 Table 3. Predictive signature AML and t(8;21) Probe set ID Gene symbol Abnormality 1.score 2.score 1 205529_s_at RUNX1T1 t(8;21) -0.6454 6.9461 2 228827_at --- t(8;21) -0.6058 6.5197 3 205528_s_at RUNX1T1 t(8;21) -0.4917 5.2917 4 213194_at ROBO1 t(8;21) -0.2334 2.5115 5 206940_s_at POU4F1 t(8;21) -0.1883 2.0269 6 216831_s_at RUNX1T1 t(8;21) -0.1705 1.8353 7 211341_at -
Supplemental Figures 04 12 2017
Jung et al. 1 SUPPLEMENTAL FIGURES 2 3 Supplemental Figure 1. Clinical relevance of natural product methyltransferases (NPMTs) in brain disorders. (A) 4 Table summarizing characteristics of 11 NPMTs using data derived from the TCGA GBM and Rembrandt datasets for 5 relative expression levels and survival. In addition, published studies of the 11 NPMTs are summarized. (B) The 1 Jung et al. 6 expression levels of 10 NPMTs in glioblastoma versus non‐tumor brain are displayed in a heatmap, ranked by 7 significance and expression levels. *, p<0.05; **, p<0.01; ***, p<0.001. 8 2 Jung et al. 9 10 Supplemental Figure 2. Anatomical distribution of methyltransferase and metabolic signatures within 11 glioblastomas. The Ivy GAP dataset was downloaded and interrogated by histological structure for NNMT, NAMPT, 12 DNMT mRNA expression and selected gene expression signatures. The results are displayed on a heatmap. The 13 sample size of each histological region as indicated on the figure. 14 3 Jung et al. 15 16 Supplemental Figure 3. Altered expression of nicotinamide and nicotinate metabolism‐related enzymes in 17 glioblastoma. (A) Heatmap (fold change of expression) of whole 25 enzymes in the KEGG nicotinate and 18 nicotinamide metabolism gene set were analyzed in indicated glioblastoma expression datasets with Oncomine. 4 Jung et al. 19 Color bar intensity indicates percentile of fold change in glioblastoma relative to normal brain. (B) Nicotinamide and 20 nicotinate and methionine salvage pathways are displayed with the relative expression levels in glioblastoma 21 specimens in the TCGA GBM dataset indicated. 22 5 Jung et al. 23 24 Supplementary Figure 4. -
SUPPLEMENTARY MATERIALS and METHODS PBMC Transcriptomics
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Gut SUPPLEMENTARY MATERIALS AND METHODS PBMC transcriptomics identifies immune-metabolism disorder during the development of HBV-ACLF Contents l Supplementary methods l Supplementary Figure 1 l Supplementary Figure 2 l Supplementary Figure 3 l Supplementary Figure 4 l Supplementary Figure 5 l Supplementary Table 1 l Supplementary Table 2 l Supplementary Table 3 l Supplementary Table 4 l Supplementary Tables 5-14 l Supplementary Table 15 l Supplementary Table 16 l Supplementary Table 17 Li J, et al. Gut 2021;0:1–13. doi: 10.1136/gutjnl-2020-323395 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Gut SUPPLEMENTARY METHODS Test for HBV DNA The levels of HBV DNA were detected using real-time PCR with a COBAS® AmpliPrep/COBAS® TaqMan 48 System (Roche, Basel, Switzerland) and HBV Test v2.0. Criteria for diagnosing cirrhosis Pathology The gold standard for the diagnosis of cirrhosis is a liver biopsy obtained through a percutaneous or transjugular approach.1 Ultrasonography was performed 2-4 hours before biopsy. Liver biopsy specimens were obtained by experienced physicians. Percutaneous transthoracic puncture of the liver was performed according to the standard criteria. After biopsy, patients were monitored in the hospital with periodic analyses of haematocrit and other vital signs for 24 hours. Cirrhosis was diagnosed according to the globally agreed upon criteria.2 Cirrhosis is defined based on its pathological features under a microscope: (a) the presence of parenchymal nodules, (b) differences in liver cell size and appearance, (c) fragmentation of the biopsy specimen, (d) fibrous septa, and (d) an altered architecture and vascular relationships.