Identification of Neuronal Substrates Implicates Pak5 in Synaptic Vesicle Trafficking
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The Role of P21-Activated Protein Kinase 1 in Metabolic Homeostasis
THE ROLE OF P21-ACTIVATED PROTEIN KINASE 1 IN METABOLIC HOMEOSTASIS by YU-TING CHIANG A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Physiology University of Toronto © Copyright by Yu-ting Chiang 2014 The Role of P21-Activated Protein Kinase 1 in Metabolic Homeostasis Yu-ting Chiang Doctor of Philosophy Department of Physiology University of Toronto 2014 Abstract Our laboratory has demonstrated previously that the proglucagon gene (gcg), which encodes the incretin hormone GLP-1, is among the downstream targets of the Wnt signaling pathway; and that Pak1 mediates the stimulatory effect of insulin on Wnt target gene expression in mouse gut non- endocrine cells. Here, I asked whether Pak1 controls gut gcg expression and GLP-1 production, and whether Pak1 deletion leads to impaired metabolic homeostasis in mice. I detected the expression of Pak1 and two other group I Paks in the gut endocrine L cell line GLUTag, and co- localized Pak1 and GLP-1 in the mouse gut. Insulin was shown to stimulate Pak1 Thr423 and β-cat Ser675 phosphorylation. The stimulation of insulin on β-cat Ser675 phosphorylation, gcg promoter activity and gcg mRNA expression could be attenuated by the Pak inhibitor IPA3. Male Pak1-/- mice showed significant reduction in both gut and brain gcg expression levels, and attenuated elevation of plasma GLP-1 levels in response to oral glucose challenge. Notably, the Pak1-/- mice were intolerant to both intraperitoneal and oral glucose administration. Aged Pak1-/- mice showed a severe defect in response to intraperitoneal pyruvate challenge (IPPTT). -
Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
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). -
Viewer 4.0 Software [73]
BMC Genomics BioMed Central Research Open Access Bioinformatic search of plant microtubule-and cell cycle related serine-threonine protein kinases Pavel A Karpov1, Elena S Nadezhdina2,3,AllaIYemets1, Vadym G Matusov1, Alexey Yu Nyporko1,NadezhdaYuShashina3 and Yaroslav B Blume*1 Addresses: 1Institute of Food Biotechnology and Genomics, National Academy of Sciences of Ukraine, 04123 Kyiv, Ukraine, 2Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russian Federation and 3AN Belozersky Institute of Physical- Chemical Biology, Moscow State University, Leninsky Gory, 119992 Moscow, Russian Federation E-mail: Pavel A Karpov - [email protected]; Elena S Nadezhdina - [email protected]; Alla I Yemets - [email protected]; Vadym G Matusov - [email protected]; Alexey Yu Nyporko - [email protected]; Nadezhda Yu Shashina - [email protected]; Yaroslav B Blume* - [email protected] *Corresponding author from International Workshop on Computational Systems Biology Approaches to Analysis of Genome Complexity and Regulatory Gene Networks Singapore 20-25 November 2008 Published: 10 February 2010 BMC Genomics 2010, 11(Suppl 1):S14 doi: 10.1186/1471-2164-11-S1-S14 This article is available from: http://www.biomedcentral.com/1471-2164/11/S1/S14 Publication of this supplement was made possible with help from the Bioinformatics Agency for Science, Technology and Research of Singapore and the Institute for Mathematical Sciences at the National University of Singapore. © 2010 Karpov et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
The Role of the Rho Gtpases in Neuronal Development
Downloaded from genesdev.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW The role of the Rho GTPases in neuronal development Eve-Ellen Govek,1,2, Sarah E. Newey,1 and Linda Van Aelst1,2,3 1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA; 2Molecular and Cellular Biology Program, State University of New York at Stony Brook, Stony Brook, New York, 11794, USA Our brain serves as a center for cognitive function and and an inactive GDP-bound state. Their activity is de- neurons within the brain relay and store information termined by the ratio of GTP to GDP in the cell and can about our surroundings and experiences. Modulation of be influenced by a number of different regulatory mol- this complex neuronal circuitry allows us to process that ecules. Guanine nucleotide exchange factors (GEFs) ac- information and respond appropriately. Proper develop- tivate GTPases by enhancing the exchange of bound ment of neurons is therefore vital to the mental health of GDP for GTP (Schmidt and Hall 2002); GTPase activat- an individual, and perturbations in their signaling or ing proteins (GAPs) act as negative regulators of GTPases morphology are likely to result in cognitive impairment. by enhancing the intrinsic rate of GTP hydrolysis of a The development of a neuron requires a series of steps GTPase (Bernards 2003; Bernards and Settleman 2004); that begins with migration from its birth place and ini- and guanine nucleotide dissociation inhibitors (GDIs) tiation of process outgrowth, and ultimately leads to dif- prevent exchange of GDP for GTP and also inhibit the ferentiation and the formation of connections that allow intrinsic GTPase activity of GTP-bound GTPases (Zalc- it to communicate with appropriate targets. -
Phosphorylation Sites in Protein Kinases and Phosphatases Regulated by Formyl Peptide Receptor 2 Signaling
International Journal of Molecular Sciences Review Phosphorylation Sites in Protein Kinases and Phosphatases Regulated by Formyl Peptide Receptor 2 Signaling Maria Carmela Annunziata 1, Melania Parisi 1, Gabriella Esposito 2 , Gabriella Fabbrocini 1, Rosario Ammendola 2 and Fabio Cattaneo 2,* 1 Department of Clinical Medicine and Surgery, School of Medicine, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy; [email protected] (M.C.A.); [email protected] (M.P.); [email protected] (G.F.) 2 Department of Molecular Medicine and Medical Biotechnology, School of Medicine,, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy; [email protected] (G.E.); [email protected] (R.A.) * Correspondence: [email protected]; Fax: +39-081-7464-359 Received: 5 May 2020; Accepted: 25 May 2020; Published: 27 May 2020 Abstract: FPR1, FPR2, and FPR3 are members of Formyl Peptides Receptors (FPRs) family belonging to the GPCR superfamily. FPR2 is a low affinity receptor for formyl peptides and it is considered the most promiscuous member of this family. Intracellular signaling cascades triggered by FPRs include the activation of different protein kinases and phosphatase, as well as tyrosine kinase receptors transactivation. Protein kinases and phosphatases act coordinately and any impairment of their activation or regulation represents one of the most common causes of several human diseases. Several phospho-sites has been identified in protein kinases and phosphatases, whose role may be to expand the repertoire of molecular mechanisms of regulation or may be necessary for fine-tuning of switch properties. We previously performed a phospho-proteomic analysis in FPR2-stimulated cells that revealed, among other things, not yet identified phospho-sites on six protein kinases and one protein phosphatase. -
Supplementary Material Contents
Supplementary Material Contents Immune modulating proteins identified from exosomal samples.....................................................................2 Figure S1: Overlap between exosomal and soluble proteomes.................................................................................... 4 Bacterial strains:..............................................................................................................................................4 Figure S2: Variability between subjects of effects of exosomes on BL21-lux growth.................................................... 5 Figure S3: Early effects of exosomes on growth of BL21 E. coli .................................................................................... 5 Figure S4: Exosomal Lysis............................................................................................................................................ 6 Figure S5: Effect of pH on exosomal action.................................................................................................................. 7 Figure S6: Effect of exosomes on growth of UPEC (pH = 6.5) suspended in exosome-depleted urine supernatant ....... 8 Effective exosomal concentration....................................................................................................................8 Figure S7: Sample constitution for luminometry experiments..................................................................................... 8 Figure S8: Determining effective concentration ......................................................................................................... -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7 -
Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology
Behavior Genetics (2018) 48:198–208 https://doi.org/10.1007/s10519-018-9893-3 ORIGINAL RESEARCH Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology David Curtis1,2 · Leda Coelewij1 · Shou‑Hwa Liu1 · Jack Humphrey1,3 · Richard Mott1 Received: 22 November 2017 / Accepted: 13 March 2018 / Published online: 21 March 2018 © The Author(s) 2018 Abstract A previous study of exome-sequenced schizophrenia cases and controls reported an excess of singleton, gene-disruptive vari- ants among cases, concentrated in particular gene sets. The dataset included a number of subjects with a substantial Finnish contribution to ancestry. We have reanalysed the same dataset after removal of these subjects and we have also included non- singleton variants of all types using a weighted burden test which assigns higher weights to variants predicted to have a greater effect on protein function. We investigated the same 31 gene sets as previously and also 1454 GO gene sets. The reduced dataset consisted of 4225 cases and 5834 controls. No individual variants or genes were significantly enriched in cases but 13 out of the 31 gene sets were significant after Bonferroni correction and the “FMRP targets” set produced a signed log p value (SLP) of 7.1. The gene within this set with the highest SLP, equal to 3.4, was FYN, which codes for a tyrosine kinase which phosphorylates glutamate metabotropic receptors and ionotropic NMDA receptors, thus modulating their trafficking, subcellular distribution and function. In the most recent GWAS of schizophrenia it was identified as a “prioritized candidate gene”.