Identification of Transcriptomic Differences Between Lower
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(PI3K(P110 )) Directly Regulates Key Components of the Z-Disc And
THE JOURNAL OF BIOLOGICAL CHEMISTRY VOL. 286, NO. 35, pp. 30837–30846, September 2, 2011 © 2011 by The American Society for Biochemistry and Molecular Biology, Inc. Printed in the U.S.A. Phosphoinositide 3-Kinase (PI3K(p110␣)) Directly Regulates Key Components of the Z-disc and Cardiac Structure*□S Received for publication, June 13, 2011, and in revised form, July 7, 2011 Published, JBC Papers in Press, July 11, 2011, DOI 10.1074/jbc.M111.271684 Ashley J. Waardenberg‡§, Bianca C. Bernardo¶, Dominic C. H. Ngʈ, Peter R. Shepherd**‡‡, Nelly Cemerlang¶, Mauro Sbroggio` §§, Christine A. Wells‡¶¶, Brian P. Dalrymple§, Mara Brancaccio§§, Ruby C. Y. Linʈʈ1, and Julie R. McMullen¶1,2 From the ‡Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, 4111, Australia, §Commonwealth Scientific and Industrial Research Organisation, Food Futures Flagship, Queensland Bioscience Precinct, St. Lucia, Queensland, 4067, Australia, the ¶Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, 8008, Australia, the ʈDepartment of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, 3010, Australia, the **Department of Molecular Medicine, University of Auckland, Grafton, Auckland, 1142, New Zealand, the ‡‡Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Grafton, Auckland, 1142, New Zealand, the §§Department of Genetics, Biology, and Biochemistry, University of Torino, Molecular Biotechnology Center, Torino, 10126, Italy, the ¶¶Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, 4072, Australia, and the ʈʈRamaciotti Centre for Gene Function Analysis and the School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, 2052, Australia Downloaded from Maintenance of cardiac structure and Z-disc signaling are key from PI3K transgenic mice. -
Progressive Increase in Mtdna 3243A>G Heteroplasmy Causes Abrupt
Progressive increase in mtDNA 3243A>G PNAS PLUS heteroplasmy causes abrupt transcriptional reprogramming Martin Picarda, Jiangwen Zhangb, Saege Hancockc, Olga Derbenevaa, Ryan Golhard, Pawel Golike, Sean O’Hearnf, Shawn Levyg, Prasanth Potluria, Maria Lvovaa, Antonio Davilaa, Chun Shi Lina, Juan Carlos Perinh, Eric F. Rappaporth, Hakon Hakonarsonc, Ian A. Trouncei, Vincent Procaccioj, and Douglas C. Wallacea,1 aCenter for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia and the Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104; bSchool of Biological Sciences, The University of Hong Kong, Hong Kong, People’s Republic of China; cTrovagene, San Diego, CA 92130; dCenter for Applied Genomics, Division of Genetics, Department of Pediatrics, and hNucleic Acid/Protein Research Core Facility, Children’s Hospital of Philadelphia, Philadelphia, PA 19104; eInstitute of Genetics and Biotechnology, Warsaw University, 00-927, Warsaw, Poland; fMorton Mower Central Research Laboratory, Sinai Hospital of Baltimore, Baltimore, MD 21215; gGenomics Sevices Laboratory, HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806; iCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia; and jDepartment of Biochemistry and Genetics, National Center for Neurodegenerative and Mitochondrial Diseases, Centre Hospitalier Universitaire d’Angers, 49933 Angers, France Contributed by Douglas C. Wallace, August 1, 2014 (sent for review May -
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. -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
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 -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Anti-Inflammatory Role of Curcumin in LPS Treated A549 Cells at Global Proteome Level and on Mycobacterial Infection
Anti-inflammatory Role of Curcumin in LPS Treated A549 cells at Global Proteome level and on Mycobacterial infection. Suchita Singh1,+, Rakesh Arya2,3,+, Rhishikesh R Bargaje1, Mrinal Kumar Das2,4, Subia Akram2, Hossain Md. Faruquee2,5, Rajendra Kumar Behera3, Ranjan Kumar Nanda2,*, Anurag Agrawal1 1Center of Excellence for Translational Research in Asthma and Lung Disease, CSIR- Institute of Genomics and Integrative Biology, New Delhi, 110025, India. 2Translational Health Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India. 3School of Life Sciences, Sambalpur University, Jyoti Vihar, Sambalpur, Orissa, 768019, India. 4Department of Respiratory Sciences, #211, Maurice Shock Building, University of Leicester, LE1 9HN 5Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia- 7003, Bangladesh. +Contributed equally for this work. S-1 70 G1 S 60 G2/M 50 40 30 % of cells 20 10 0 CURI LPSI LPSCUR Figure S1: Effect of curcumin and/or LPS treatment on A549 cell viability A549 cells were treated with curcumin (10 µM) and/or LPS or 1 µg/ml for the indicated times and after fixation were stained with propidium iodide and Annexin V-FITC. The DNA contents were determined by flow cytometry to calculate percentage of cells present in each phase of the cell cycle (G1, S and G2/M) using Flowing analysis software. S-2 Figure S2: Total proteins identified in all the three experiments and their distribution betwee curcumin and/or LPS treated conditions. The proteins showing differential expressions (log2 fold change≥2) in these experiments were presented in the venn diagram and certain number of proteins are common in all three experiments. -
Mice Carrying a Human GLUD2 Gene Recapitulate Aspects of Human Transcriptome and Metabolome Development
Mice carrying a human GLUD2 gene recapitulate aspects of human transcriptome and metabolome development Qian Lia,b,1, Song Guoa,1, Xi Jianga, Jaroslaw Brykc,2, Ronald Naumannd, Wolfgang Enardc,3, Masaru Tomitae, Masahiro Sugimotoe, Philipp Khaitovicha,c,f,4, and Svante Pääboc,4 aChinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China; bUniversity of Chinese Academy of Sciences, 100049 Beijing, China; cMax Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany; dMax Planck Institute of Molecular Cell Biology and Genetics, D-01307 Dresden, Germany; eInstitute for Advanced Biosciences, Keio University, 997-0035 Tsuruoka, Yamagata, Japan; and fSkolkovo Institute for Science and Technology, 143025 Skolkovo, Russia Edited by Joshua M. Akey, University of Washington, Seattle, WA, and accepted by the Editorial Board April 1, 2016 (received for review September 28, 2015) Whereas all mammals have one glutamate dehydrogenase gene metabolic flux from glucose and glutamine to lipids by way of the (GLUD1), humans and apes carry an additional gene (GLUD2), TCA cycle (12). which encodes an enzyme with distinct biochemical properties. To investigate the physiological role the GLUD2 gene may We inserted a bacterial artificial chromosome containing the human play in human and ape brains, we generated mice transgenic for GLUD2. GLUD2 gene into mice and analyzed the resulting changes in the a genomic region containing human We compared effects transcriptome and metabolome during postnatal brain development. on gene expression and metabolism during postnatal development Effects were most pronounced early postnatally, and predominantly of the frontal cortex of the brain in these mice and their wild-type genes involved in neuronal development were affected. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Large Sex Differences in Chicken Behavior and Brain Gene Expression Coincide with Few Differences in Promoter DNA-Methylation
Large Sex Differences in Chicken Behavior and Brain Gene Expression Coincide with Few Differences in Promoter DNA-Methylation Daniel Na¨tt1,2*, Beatrix Agnvall1, Per Jensen1 1 IFM Biology, AVIAN Behaviour and Genomics group, Linko¨ping University, Linko¨ping, Sweden, 2 Department of Clinical and Experimental Medicine, Laboratory of Integrative and Behavioral Neuroscience, Linko¨ping University, Linko¨ping, Sweden Abstract While behavioral sex differences have repeatedly been reported across taxa, the underlying epigenetic mechanisms in the brain are mostly lacking. Birds have previously shown to have only limited dosage compensation, leading to high sex bias of Z-chromosome gene expression. In chickens, a male hyper-methylated region (MHM) on the Z-chromosome has been associated with a local type of dosage compensation, but a more detailed characterization of the avian methylome is limiting our interpretations. Here we report an analysis of genome wide sex differences in promoter DNA-methylation and gene expression in the brain of three weeks old chickens, and associated sex differences in behavior of Red Junglefowl (ancestor of domestic chickens). Combining DNA-methylation tiling arrays with gene expression microarrays we show that a specific locus of the MHM region, together with the promoter for the zinc finger RNA binding protein (ZFR) gene on chromosome 1, is strongly associated with sex dimorphism in gene expression. Except for this, we found few differences in promoter DNA-methylation, even though hundreds of genes were robustly differentially expressed across distantly related breeds. Several of the differentially expressed genes are known to affect behavior, and as suggested from their functional annotation, we found that female Red Junglefowl are more explorative and fearful in a range of tests performed throughout their lives.