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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. -
Palmitoyl-Protein Thioesterase 1 Deficiency in Drosophila Melanogaster Causes Accumulation
Genetics: Published Articles Ahead of Print, published on February 1, 2006 as 10.1534/genetics.105.053306 Palmitoyl-protein thioesterase 1 deficiency in Drosophila melanogaster causes accumulation of abnormal storage material and reduced lifespan Anthony J. Hickey*,†,1, Heather L. Chotkowski*, Navjot Singh*, Jeffrey G. Ault*, Christopher A. Korey‡,2, Marcy E. MacDonald‡, and Robert L. Glaser*,†,3 * Wadsworth Center, New York State Department of Health, Albany, NY 12201-2002 † Department of Biomedical Sciences, State University of New York, Albany, NY 12201-0509 ‡ Molecular Neurogenetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114 1 current address: Albany Medical College, Albany, NY 12208 2 current address: Department of Biology, College of Charleston, Charleston, SC 294243 3 corresponding author: Wadsworth Center, NYS Dept. Health, P. O. Box 22002, Albany, NY 12201-2002 E-mail: [email protected] 1 running title: Phenotypes of Ppt1-deficient Drosophila key words: Batten disease infantile neuronal ceroid lipofuscinosis palmitoyl-protein thioesterase CLN1 Drosophila corresponding author: Robert L. Glaser Wadsworth Center, NYS Dept. Health P. O. Box 22002 Albany, NY 12201-2002 E-mail: [email protected] phone: 518-473-4201 fax: 518-474-3181 2 ABSTRACT Human neuronal ceroid lipofuscinoses (NCLs) are a group of genetic neurodegenerative diseases characterized by progressive death of neurons in the central nervous system (CNS) and accumulation of abnormal lysosomal storage material. Infantile NCL (INCL), the most severe form of NCL, is caused by mutations in the Ppt1 gene, which encodes the lysosomal enzyme palmitoyl-protein thioesterase 1 (Ppt1). We generated mutations in the Ppt1 ortholog of Drosophila melanogaster in order to characterize phenotypes caused by Ppt1-deficiency in flies. -
Cell, Volume 139 Supplemental Data Profiling the Human Protein-DNA
Cell, Volume 139 Supplemental Data Profiling the Human Protein-DNA Interactome Reveals ERK2 as a Transcriptional Repressor of Interferon Signaling Shaohui Hu, Zhi Xie, Akishi Onishi, Xueping Yu, Lizhi Jiang, Jimmy Lin, Hee-sool Rho, Crystal Woodard, Hong Wang, Jun-Seop Jeong, Shunyou Long, Xiaofei He, Herschel Wade, Seth Blackshaw, Jiang Qian, and Heng Zhu Supplemental Experimental Procedures Identifying Tissue-specific Motifs We developed a program to identify tissue-specific motifs. We first defined sets of tissue-specific or tissue-enriched genes by examining their gene expression profiles across multiple tissues (Yu et al., 2006). We then calculated the most over-represented single motifs (8-mers, including a wide character) in the promoters of each set of tissue-specific genes. The program then enumerated all possible combinations of the top n motifs (e.g. n = 100). For each motif pair, the program recorded the occurrence of the motif pair in the promoter sequences. We then calculated the significance score for each motif pair, which was defined as the negative logarithm of the p value, -log(p). The motif pairs with scores above a specified threshold were considered putative TF binding motif pairs in the promoter sequences. With these predicted motif pairs, we could calculate a number of partners for each motif and select a certain number of top non-redundant motifs to be tested in the protein chip experiments. Both the p values for a single motif and those for a motif pair were calculated using hypergeometric distribution. Here, we use a motif pair as an example to show the ij, procedure. -
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). -
Investigation of Candidate Genes and Mechanisms Underlying Obesity
Prashanth et al. BMC Endocrine Disorders (2021) 21:80 https://doi.org/10.1186/s12902-021-00718-5 RESEARCH ARTICLE Open Access Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules G. Prashanth1 , Basavaraj Vastrad2 , Anandkumar Tengli3 , Chanabasayya Vastrad4* and Iranna Kotturshetti5 Abstract Background: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. Methods: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. Results: A total of 820 DEGs were identified between -
Inflammatory Stimuli Induce Acyl-Coa Thioesterase 7 and Remodeling of Phospholipids Containing Unsaturated Long (C20)-Acyl Chains in Macrophages
Supplemental Material can be found at: http://www.jlr.org/content/suppl/2017/04/17/jlr.M076489.DC1 .html Inflammatory stimuli induce acyl-CoA thioesterase 7 and remodeling of phospholipids containing unsaturated long (C20)-acyl chains in macrophages Valerie Z. Wall,*,† Shelley Barnhart,* Farah Kramer,* Jenny E. Kanter,* Anuradha Vivekanandan-Giri,§ Subramaniam Pennathur,§ Chiara Bolego,** Jessica M. Ellis,§§,*** Miguel A. Gijón,††† Michael J. Wolfgang,*** and Karin E. Bornfeldt1,*,† Department of Medicine,* Division of Metabolism, Endocrinology and Nutrition, and Department of Pathology,† UW Medicine Diabetes Institute, University of Washington, Seattle, WA; Department of Internal Medicine,§ University of Michigan, Ann Arbor, MI; Department of Pharmaceutical and Pharmacological Sciences,** University of Padova, Padova, Italy; Department of Nutrition Science,§§ Purdue University, West Lafayette, IN; Department of Biological Chemistry,*** Johns Hopkins University School of Medicine, Baltimore, MD; and Department of Pharmacology,††† University of Downloaded from Colorado Denver, Aurora, CO Abstract Acyl-CoA thioesterase 7 (ACOT7) is an intracel- containing unsaturated long (C20)-acyl chains in macro- lular enzyme that converts acyl-CoAs to FFAs. ACOT7 is in- phages, and, although ACOT7 has preferential thioesterase duced by lipopolysaccharide (LPS); thus, we investigated activity toward these lipid species, loss of ACOT7 has no ma- www.jlr.org downstream effects of LPS-induced induction of ACOT7 jor detrimental effect on macrophage inflammatory pheno- and its role in inflammatory settings in myeloid cells. Enzy- types.—Wall, V. Z., S. Barnhart, F. Kramer, J. E. Kanter, matic thioesterase activity assays in WT and ACOT7-deficient A. Vivekanandan-Giri, S. Pennathur, C. Bolego, J. M. Ellis, macrophage lysates indicated that endogenous ACOT7 con- M. -
The Metabolic Serine Hydrolases and Their Functions in Mammalian Physiology and Disease Jonathan Z
REVIEW pubs.acs.org/CR The Metabolic Serine Hydrolases and Their Functions in Mammalian Physiology and Disease Jonathan Z. Long* and Benjamin F. Cravatt* The Skaggs Institute for Chemical Biology and Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States CONTENTS 2.4. Other Phospholipases 6034 1. Introduction 6023 2.4.1. LIPG (Endothelial Lipase) 6034 2. Small-Molecule Hydrolases 6023 2.4.2. PLA1A (Phosphatidylserine-Specific 2.1. Intracellular Neutral Lipases 6023 PLA1) 6035 2.1.1. LIPE (Hormone-Sensitive Lipase) 6024 2.4.3. LIPH and LIPI (Phosphatidic Acid-Specific 2.1.2. PNPLA2 (Adipose Triglyceride Lipase) 6024 PLA1R and β) 6035 2.1.3. MGLL (Monoacylglycerol Lipase) 6025 2.4.4. PLB1 (Phospholipase B) 6035 2.1.4. DAGLA and DAGLB (Diacylglycerol Lipase 2.4.5. DDHD1 and DDHD2 (DDHD Domain R and β) 6026 Containing 1 and 2) 6035 2.1.5. CES3 (Carboxylesterase 3) 6026 2.4.6. ABHD4 (Alpha/Beta Hydrolase Domain 2.1.6. AADACL1 (Arylacetamide Deacetylase-like 1) 6026 Containing 4) 6036 2.1.7. ABHD6 (Alpha/Beta Hydrolase Domain 2.5. Small-Molecule Amidases 6036 Containing 6) 6027 2.5.1. FAAH and FAAH2 (Fatty Acid Amide 2.1.8. ABHD12 (Alpha/Beta Hydrolase Domain Hydrolase and FAAH2) 6036 Containing 12) 6027 2.5.2. AFMID (Arylformamidase) 6037 2.2. Extracellular Neutral Lipases 6027 2.6. Acyl-CoA Hydrolases 6037 2.2.1. PNLIP (Pancreatic Lipase) 6028 2.6.1. FASN (Fatty Acid Synthase) 6037 2.2.2. PNLIPRP1 and PNLIPR2 (Pancreatic 2.6.2. -
Identification of Acyl-Coa Thioesterase in Mouse Mesenteric Lymph Nodes
866 Note Biol. Pharm. Bull. 36(5) 866–871 (2013) Vol. 36, No. 5 Identification of Acyl-CoA Thioesterase in Mouse Mesenteric Lymph Nodes Takayuki Ohtomo,a Chisato Nakao,a Megumi Sumiya,a Osamu Kaminuma,b Akemi Abe,c Akio Mori,c Niro Inaba,d Tetsuta Kato,e and Junji Yamada*,a a Department of Pharmacotherapeutics, Tokyo University of Pharmacy and Life Sciences; d Center for Fundamental Laboratory Education, Tokyo University of Pharmacy and Life Sciences; e Center for the Advancement of Pharmaceutical Education, Tokyo University of Pharmacy and Life Sciences; Tokyo 192–0392, Japan: b Allergy and Immunology Project, Tokyo Metropolitan Institute of Medical Science; 2–1–7 Kamikitazawa, Setagaya-ku, Tokyo 156–8506, Japan: and c National Hospital Organization, Sagamihara National Hospital, Clinical Research Center for Allergy and Rheumatology; 18–1 Sakuradai, Minami-ku, Sagamihara, Kanagawa 252–0392, Japan. Received December 19, 2012; accepted February 20, 2013 Acyl-CoA thioesterases (ACOTs) are a group of enzymes that catalyze the hydrolysis of fatty acyl-CoAs to free fatty acids and CoA, with the potential to regulate the intracellular levels of these molecules. In this study, we show that a cytosolic isoform, ACOT7, is expressed at a significant level in the mesenteric lymph nodes (MLNs) of mice. While crude preparations of the mesenteric visceral fat contained significant levels of palmitoyl-CoA thioesterase activity, enzyme activity was concentrated 6.9-fold in MLNs compared with the residual adipose portion after excision of MLNs. When MLN homogenates were centrifuged, 82% of the en- zyme activity was recovered in the cytosolic fraction, concomitant with almost exclusive recovery of ACOT7. -
Application to Neuroimaging Biomarkers in Alzheimer's
The Author(s) BMC Medical Informatics and Decision Making 2017, 17(Suppl 1):61 DOI 10.1186/s12911-017-0454-0 RESEARCH Open Access Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer’s disease Dokyoon Kim1,2, Anna O. Basile2, Lisa Bang1, Emrin Horgusluoglu4, Seunggeun Lee3, Marylyn D. Ritchie1,2, Andrew J. Saykin4 and Kwangsik Nho4* From The 6th Translational Bioinformatics Conference Je Ju Island, Korea. 15-17 October 2016 Abstract Background: Rapid advancement of next generation sequencing technologies such as whole genome sequencing (WGS) has facilitated the search for genetic factors that influence disease risk in the field of human genetics. To identify rare variants associated with human diseases or traits, an efficient genome-wide binning approach is needed. In this study we developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer’sdisease(LOAD). Methods: For rare-variant analysis, we used the knowledge-driven binning approach implemented in Bin-KAT, an automated tool, that provides 1) binning/collapsing methods for multi-level variant aggregation with a flexible, biologically informed binning strategy and 2) an option of performing unified collapsing and statistical rare variant analyses in one tool. A total of 750 non-Hispanic Caucasian participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort who had both WGS data and magnetic resonance imaging (MRI) scans were used in this study. Mean bilateral cortical thickness of the entorhinal cortex extracted from MRI scans was used as an AD-related neuroimaging endophenotype. -
Long Noncoding RNA LYPLAL1-AS1 Regulates Adipogenic Differentiation
Yang et al. Cell Death Discovery (2021) 7:105 https://doi.org/10.1038/s41420-021-00500-5 Cell Death Discovery ARTICLE Open Access Long noncoding RNA LYPLAL1-AS1 regulates adipogenic differentiation of human mesenchymal stem cells by targeting desmoplakin and inhibiting the Wnt/β-catenin pathway Yanlei Yang1,2,JunfenFan1,HaoyingXu1,LinyuanFan1,LuchanDeng1,JingLi 1,DiLi1, Hongling Li1, Fengchun Zhang2 and Robert Chunhua Zhao1 Abstract Long noncoding RNAs are crucial factors for modulating adipogenic differentiation, but only a few have been identified in humans. In the current study, we identified a previously unknown human long noncoding RNA, LYPLAL1- antisense RNA1 (LYPLAL1-AS1), which was dramatically upregulated during the adipogenic differentiation of human adipose-derived mesenchymal stem cells (hAMSCs). Based on 5′ and 3′ rapid amplification of cDNA ends assays, full- length LYPLAL1-AS1 was 523 nt. Knockdown of LYPLAL1-AS1 decreased the adipogenic differentiation of hAMSCs, whereas overexpression of LYPLAL1-AS1 enhanced this process. Desmoplakin (DSP) was identified as a direct target of LYPLAL1-AS1. Knockdown of DSP enhanced adipogenic differentiation and rescued the LYPLAL1-AS1 depletion- induced defect in adipogenic differentiation of hAMSCs. Further experiments showed that LYPLAL1-AS1 modulated DSP protein stability possibly via proteasome degradation, and the Wnt/β-catenin pathway was inhibited during 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; adipogenic differentiation regulated by the LYPLAL1-AS1/DSP complex. Together, our work provides a new mechanism by which long noncoding RNA regulates adipogenic differentiation of human MSCs and suggests that LYPLAL1-AS1 may serve as a novel therapeutic target for preventing and combating diseases related to abnormal adipogenesis, such as obesity. -
Supp Table 2
Supplementary Table 2 : Transcripts and pathways down-regulated in SLE compared to control renal biopsies Differences in (Log2-transformed, mean-centered) gene expression between lupus and control biopsies were analyzed using a moderated t test with Benjamini-Hochberg correction for multiple comparisons (p value threshold set to 0.05). Pathway analyses were performed using DAVID software. Enrichment scores are –log10 p values, calculated by modified Fisher Exact test by comparing proportions of transcripts belonging to a given pathway in the tested gene list compared to the whole transcriptome.[14, 15] Transcripts Pathways Identifier [Control] [SLE] Gene Symbol Annotation Cluster 1 Enrichment Score: 7.43 Database Pathway Count P_Value Benjamini ILMN_1751607 2.8195102 0.25910223 FOSB SP_PIR_KEYWORDS mitochondrion 52 6.80E-20 2.30E-17 ILMN_1682763 2.1716797 -0.4070203 ALB GOTERM_CC_FAT mitochondrion 60 2.70E-19 7.50E-17 ILMN_1781285 1.7535293 -0.0462692 DUSP1 GOTERM_CC_FAT mitochondrial part 42 7.40E-17 1.50E-14 ILMN_1723522 1.718832 -0.2997489 APOLD1 GOTERM_CC_FAT mitochondrial enveLope 34 3.00E-15 2.80E-13 ILMN_1765232 1.632873 0.14076953 RNLS GOTERM_CC_FAT mitochondrial inner membrane 28 8.40E-14 5.80E-12 ILMN_1662880 1.6177534 -0.1763568 FIS GOTERM_CC_FAT mitochondrial membrane 31 1.50E-13 8.60E-12 ILMN_1813361 1.6158535 0.08449265 ANGPTL7 UP_SEQ_FEATURE tranSit peptide:Mitochondrion 32 3.90E-13 2.80E-10 ILMN_2047618 1.6081884 -0.0522185 KCNE1 GOTERM_CC_FAT organeLLe inner membrane 28 4.80E-13 2.20E-11 ILMN_1711015 1.577714 -0.2619377 -
Findings from ACCORD Lipid Short Title: Genetics of Cardiovascular Response to Fibrates
Diabetes Page 2 of 58 A PPARA Polymorphism Influences the Cardiovascular Benefit of Fenofibrate in Type 2 Diabetes: Findings from ACCORD Lipid Short title: Genetics of cardiovascular response to fibrates Authors: Mario Luca Morieri M.D.1,2,3, Hetal S. Shah M.D., M.P.H. 1,2, Jennifer Sjaarda, B.S.4, Petra A. Lenzini, M.S.5, Hannah Campbell, M.P.H5,6, Alison A. Motsinger-Reif Ph.D.7, He Gao Ph.D.1,2, Laura Lovato M.S.8, Sabrina Prudente Ph.D.9, Assunta Pandolfi Ph.D.10, Marcus G. Pezzolesi Ph.D., M.P.H.11, Ronald J. Sigal M.D., M.P.H.12, Guillaume Paré M.D., M.Sc.4, Santica M. Marcovina Ph.D., D.Sc.13, Daniel M. Rotroff Ph.D.14, Elisabetta Patorno M.D., Dr.P.H.15, Luana Mercuri Ph.D.9, Vincenzo Trischitta M.D.9,16, Emily Y. Chew M.D.17, Peter Kraft Ph.D.18, John B. Buse M.D. Ph.D.19, Michael J. Wagner Ph.D.20, Sharon Cresci, M.D. 5,6, Hertzel C. Gerstein M.D. M.Sc.4, Henry N. Ginsberg, M.D.21, Josyf C. Mychaleckyj M.A., D.Phil.22, Alessandro Doria M.D., Ph.D., M.P.H..1,2 1: Research Division, Joslin Diabetes Center, Boston, MA 2: Department of Medicine, Harvard Medical School, Boston, MA 3: Department of Medicine, University of Padova, Padova, Italy 4: McMaster University and the Population Health Research Institute, Hamilton, Ontario, Canada 5: Department of Genetics, Washington University School of Medicine, St.