Exploring the Cellular Network of Metabolic Flexibility in the Adipose Tissue Samar H
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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 -
ROS Production Induced by BRAF Inhibitor Treatment Rewires
Cesi et al. Molecular Cancer (2017) 16:102 DOI 10.1186/s12943-017-0667-y RESEARCH Open Access ROS production induced by BRAF inhibitor treatment rewires metabolic processes affecting cell growth of melanoma cells Giulia Cesi, Geoffroy Walbrecq, Andreas Zimmer, Stephanie Kreis*† and Claude Haan† Abstract Background: Most melanoma patients with BRAFV600E positive tumors respond well to a combination of BRAF kinase and MEK inhibitors. However, some patients are intrinsically resistant while the majority of patients eventually develop drug resistance to the treatment. For patients insufficiently responding to BRAF and MEK inhibitors, there is an ongoing need for new treatment targets. Cellular metabolism is such a promising new target line: mutant BRAFV600E has been shown to affect the metabolism. Methods: Time course experiments and a series of western blots were performed in a panel of BRAFV600E and BRAFWT/ NRASmut human melanoma cells, which were incubated with BRAF and MEK1 kinase inhibitors. siRNA approaches were used to investigate the metabolic players involved. Reactive oxygen species (ROS) were measured by confocal microscopy and AZD7545, an inhibitor targeting PDKs (pyruvate dehydrogenase kinase) was tested. Results: We show that inhibition of the RAS/RAF/MEK/ERK pathway induces phosphorylation of the pyruvate dehydrogenase PDH-E1α subunit in BRAFV600E and in BRAFWT/NRASmut harboring cells. Inhibition of BRAF, MEK1 and siRNA knock-down of ERK1/2 mediated phosphorylation of PDH. siRNA-mediated knock-down of all PDKs or the use of DCA (a pan-PDK inhibitor) abolished PDH-E1α phosphorylation. BRAF inhibitor treatment also induced the upregulation of ROS, concomitantly with the induction of PDH phosphorylation. -
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. -
32-3099: PRKAB1 Recombinant Protein Description
9853 Pacific Heights Blvd. Suite D. San Diego, CA 92121, USA Tel: 858-263-4982 Email: [email protected] 32-3099: PRKAB1 Recombinant Protein Alternative Name : AMPK,HAMPKb,5'-AMP-activated protein kinase subunit beta-1,AMPK subunit beta-1,AMPKb,PRKAB1. Description Source : E.coli. PRKAB1 Human Recombinant produced in E.Coli is a single, non-glycosylated, polypeptide chain containing 293 amino acids (1-270 a.a.) and having a molecular mass of 32.8 kDa. The PRKAB1 is fused to a 23 amino acid His Tag at N- Terminus and purified by proprietary chromatographic techniques. 5'-AMP-activated protein kinase subunit beta-1 (PRKAB1) hinders protein, carbohydrate and lipid biosynthesis, in addition to cell growth and proliferation. AMPK is a heterotrimer comprised of an alpha catalytic subunit, and non-catalytic beta and gamma subunits. AMPK acts via direct phosphorylation of metabolic enzymes, and longer-term effects by phosphorylation of transcription regulators. PRKAB1 is a regulator of cellular polarity by remodeling the actin cytoskeleton; most likely by indirectly activating myosin. Beta non-catalytic subunit acts as a scaffold on which the AMPK complex compiles, through its C-terminus that joins alpha (PRKAA1 or PRKAA2) and gamma subunits (PRKAG1, PRKAG2 or PRKAG3). Product Info Amount : 5 µg Purification : Greater than 85% as determined by SDS-PAGE. The PRKAB1 protein solution (0.5mg/ml) contains 20mM Tris-HCl buffer (pH 8.0), 0.15M NaCl, Content : 10% glycerol and 1mM DTT. Store at 4°C if entire vial will be used within 2-4 weeks. Store, frozen at -20°C for longer periods of Storage condition : time. -
Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces Cerevisiae
Metabolites 2013, 3, 1102-1117; doi:10.3390/metabo3041102 OPEN ACCESS metabolites ISSN 2218-1989 www.mdpi.com/journal/metabolites/ Article Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces cerevisiae Stefan Jenkins 1,2,3,*, Steven M. Fischer 2, Lily Chen 3 and Theodore R. Sana 2 1 Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA 2 Agilent Technologies, Life Sciences, Diagnostics and Applied Markets, Santa Clara, CA 95051, USA; E-Mails: [email protected] (S.M.F.); [email protected] (T.R.S.) 3 Biology Department, San Francisco State University, San Francisco, CA 94132, USA; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-650-922-5046; Fax: +1-510-486-4545. Received: 15 October 2013; in revised form: 20 November 2013 / Accepted: 25 November 2013 / Published: 6 December 2013 Abstract: Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. -
And Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis
G C A T T A C G G C A T genes Article Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis 1, 2 3 Reyhaneh Naderi y, Homa Saadati Mollaei , Arne Elofsson 3, , and Saman Hosseini Ashtiani * y 1 Department of Artificial Intelligence and Robotics, Faculty of Computer Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran; [email protected] 2 Department of Advanced Sciences and Technology, Islamic Azad University Tehran Medical Sciences, Tehran 1916893813, Iran; [email protected] 3 Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, 106 91 Stockholm, Sweden; [email protected] * Correspondence: [email protected]; Tel.: +46-762623644 These authors contributed equally to this work and should be considered joint first authors. y Received: 9 July 2020; Accepted: 6 August 2020; Published: 10 August 2020 Abstract: (1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis’ etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein–protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein–protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. -
Tricarboxylic Acid (TCA) Cycle Intermediates: Regulators of Immune Responses
life Review Tricarboxylic Acid (TCA) Cycle Intermediates: Regulators of Immune Responses Inseok Choi , Hyewon Son and Jea-Hyun Baek * School of Life Science, Handong Global University, Pohang, Gyeongbuk 37554, Korea; [email protected] (I.C.); [email protected] (H.S.) * Correspondence: [email protected]; Tel.: +82-54-260-1347 Abstract: The tricarboxylic acid cycle (TCA) is a series of chemical reactions used in aerobic organisms to generate energy via the oxidation of acetylcoenzyme A (CoA) derived from carbohydrates, fatty acids and proteins. In the eukaryotic system, the TCA cycle occurs completely in mitochondria, while the intermediates of the TCA cycle are retained inside mitochondria due to their polarity and hydrophilicity. Under cell stress conditions, mitochondria can become disrupted and release their contents, which act as danger signals in the cytosol. Of note, the TCA cycle intermediates may also leak from dysfunctioning mitochondria and regulate cellular processes. Increasing evidence shows that the metabolites of the TCA cycle are substantially involved in the regulation of immune responses. In this review, we aimed to provide a comprehensive systematic overview of the molecular mechanisms of each TCA cycle intermediate that may play key roles in regulating cellular immunity in cell stress and discuss its implication for immune activation and suppression. Keywords: Krebs cycle; tricarboxylic acid cycle; cellular immunity; immunometabolism 1. Introduction The tricarboxylic acid cycle (TCA, also known as the Krebs cycle or the citric acid Citation: Choi, I.; Son, H.; Baek, J.-H. Tricarboxylic Acid (TCA) Cycle cycle) is a series of chemical reactions used in aerobic organisms (pro- and eukaryotes) to Intermediates: Regulators of Immune generate energy via the oxidation of acetyl-coenzyme A (CoA) derived from carbohydrates, Responses. -
Biological Pathways
Biological pathways Bing Zhang Department of Biomedical Informatics Vanderbilt University [email protected] Biological pathway A biological pathway is a series of actions among molecules in a cell that leads to a certain product or a change in a cell. Different types of biological pathways Signal transduction pathways Gene regulation pathways Metabolic pathways 2 Applied Bioinformatics, Spring 2011 Outline Pathway databases Pathway assembly and editing Pathway mapping and enrichment analysis 3 Applied Bioinformatics, Spring 2011 Selected pathway databases KEGG Kyoto Encyclopedia of Genes and Genomes http://www.genome.jp/kegg/pathway.html Reactome Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute http://www.reactome.org/ WikiPathways University of Maastricht and UCSF http://www.wikipathways.org/ 4 Applied Bioinformatics, Spring 2011 KEGG: data model Molecular building blocks KEGG GENES: genes and proteins generated by genome sequencing projects KEGG ORTHOLOGY: orthology (KO) groups KEGG COMPOUND: small molecules KEGG REACTIONS: biochemical reactions KEGG PATHWAY: pathway maps Created in a general way to be applicable to all organisms, in terms of the orthologs defined by KO groups Organism-specific pathways can be generated by converting KO groups to gene identifiers in a given organism 5 Applied Bioinformatics, Spring 2011 KEGG: TCA cycle 6 Applied Bioinformatics, Spring 2011 Reactome: data model Entities -
NFKBIZ Mutation Prevalence in the Arthur/Schmitz/Chapuy Cohorts
Supplemental Tables/Figures: Table S1: NFKBIZ mutation prevalence in the Arthur/Schmitz/Chapuy cohorts. Subtype Cohort All ABC DLC Schmitz Chapuy # patients 1006 511 330 466 210 UTR 101 (10) 66 (13) 39 (12) 49 (11) 13 (6) Amplification 86 (8.5) 66 (12.9) 23 (7) 35 (8) 28 (13) NFKBIZ Mutation TOTAL 174 (17) 120 (23.5) 57 (17) 79 (17) 38 (18) Table S2: NFKBIZ 3′ UTR mutations in other WGS cohorts. Cohort # Patients NFKBIZ UTR mutations (%) Publication BL_Adult 81 1 (1.2) Grande_et_al,2019 BL_Pediatric 124 1 (0.8) unpublished DLBCL_cell_lines 15 2 (13.3) Morin_et_al,2013 DLBCL_BC 117 11 (9.4) Arthur_et_al,2018 DLBCL_ICGC 87 11 (12.6) Hübschmann_et_al,2021 FL_ICGC 100 4 (4) Hübschmann_et_al,2021 FL_Kridel 48 1 (2.1) Kridel_et_al,2016 Table S3: COO and LymphGen classifications of DLBCLs and FLs from other cohorts. NFKBIZ UTR mutations (%) DLBCL (%) FL (%) ABC 13 (41.9) 13 (54.2) 0 (0) GCB 4 (12.9) 4 (16.7) 0 (0) COO UNCLASS 2 (6.5) 2 (8.3) 0 (0) NA 12 (38.7) 5 (20.8) 5 (100) TOTAL 31 24 5 BN2 13 (41.9) 9 (37.5) 3 (60) EZB 4 (12.9) 2 (8.3) 2 (40) ST2 1 (3.2) 1 (4.2) 0 (0) LymphGen Other 9 (29) 8 (33.3) 0 (0) Composite 2 (6.5) 2 (8.3) 0 (0) NA 2 (6.5) 2 (8.3) 0 (0) TOTAL 31 24 5 Table S4: LymphGen classifications of all patients and those with NFKBIZ mutations in Arthur/Schmitz/Chapuy cohorts. -
Phosphoproteomics of Retinoblastoma: a Pilot Study Identifies Aberrant Kinases
molecules Article Phosphoproteomics of Retinoblastoma: A Pilot Study Identifies Aberrant Kinases Lakshmi Dhevi Nagarajha Selvan 1,†, Ravikanth Danda 1,2,†, Anil K. Madugundu 3 ID , Vinuth N. Puttamallesh 3, Gajanan J. Sathe 3,4, Uma Maheswari Krishnan 2, Vikas Khetan 5, Pukhraj Rishi 5, Thottethodi Subrahmanya Keshava Prasad 3,6 ID , Akhilesh Pandey 3,7,8, Subramanian Krishnakumar 1, Harsha Gowda 3,* and Sailaja V. Elchuri 9,* 1 L&T Opthalmic Pathology, Vision Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu 600 006, India; [email protected] (L.D.N.S.); [email protected] (R.D.); [email protected] (S.K.) 2 Centre for Nanotechnology and Advanced Biomaterials, Shanmugha Arts, Science, Technology and Research Academy University, Tanjore, Tamil Nadu 613 401, India; [email protected] 3 Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560 066, India; [email protected] (A.K.M.); [email protected] (V.N.P.); [email protected] (G.J.S.); [email protected] (T.S.K.P.); [email protected] (A.P.) 4 Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576 104, India 5 Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu 600 006, India; [email protected] (V.K.); [email protected] (P.R.) 6 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka 575 108, India 7 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 8 Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 9 Department of Nanotechnology, Vision Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu 600 006, India * Correspondence: [email protected] (H.G.); [email protected] (S.V.E.); Tel.: +91-80-28416140 (H.G.); +91-44-28271616 (S.V.E.) † These authors contributed equally to this work. -
Confirmation of Pathogenic Mechanisms by SARS-Cov-2–Host
Messina et al. Cell Death and Disease (2021) 12:788 https://doi.org/10.1038/s41419-021-03881-8 Cell Death & Disease ARTICLE Open Access Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2–host interactome Francesco Messina 1, Emanuela Giombini1, Chiara Montaldo1, Ashish Arunkumar Sharma2, Antonio Zoccoli3, Rafick-Pierre Sekaly2, Franco Locatelli4, Alimuddin Zumla5, Markus Maeurer6,7, Maria R. Capobianchi1, Francesco Nicola Lauria1 and Giuseppe Ippolito 1 Abstract In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein–protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; clinical outcomes. -
Original Article a Database and Functional Annotation of NF-Κb Target Genes
Int J Clin Exp Med 2016;9(5):7986-7995 www.ijcem.com /ISSN:1940-5901/IJCEM0019172 Original Article A database and functional annotation of NF-κB target genes Yang Yang, Jian Wu, Jinke Wang The State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People’s Republic of China Received November 4, 2015; Accepted February 10, 2016; Epub May 15, 2016; Published May 30, 2016 Abstract: Backgrounds: The previous studies show that the transcription factor NF-κB always be induced by many inducers, and can regulate the expressions of many genes. The aim of the present study is to explore the database and functional annotation of NF-κB target genes. Methods: In this study, we manually collected the most complete listing of all NF-κB target genes identified to date, including the NF-κB microRNA target genes and built the database of NF-κB target genes with the detailed information of each target gene and annotated it by DAVID tools. Results: The NF-κB target genes database was established (http://tfdb.seu.edu.cn/nfkb/). The collected data confirmed that NF-κB maintains multitudinous biological functions and possesses the considerable complexity and diversity in regulation the expression of corresponding target genes set. The data showed that the NF-κB was a central regula- tor of the stress response, immune response and cellular metabolic processes. NF-κB involved in bone disease, immunological disease and cardiovascular disease, various cancers and nervous disease. NF-κB can modulate the expression activity of other transcriptional factors. Inhibition of IKK and IκBα phosphorylation, the decrease of nuclear translocation of p65 and the reduction of intracellular glutathione level determined the up-regulation or down-regulation of expression of NF-κB target genes.