Structural Diversity in the RGS Domain and Its Interaction with Heterotrimeric G Protein ␣-Subunits

Total Page:16

File Type:pdf, Size:1020Kb

Structural Diversity in the RGS Domain and Its Interaction with Heterotrimeric G Protein ␣-Subunits Structural diversity in the RGS domain and its interaction with heterotrimeric G protein ␣-subunits Meera Soundararajan*, Francis S. Willard†, Adam J. Kimple†, Andrew P. Turnbull*, Linda J. Ball‡, Guillaume A. Schoch*, Carina Gileadi*, Oleg Y. Fedorov*, Elizabeth F. Dowler‡, Victoria A. Higman‡, Stephanie Q. Hutsell†, Michael Sundstro¨ m*§, Declan A. Doyle*¶, and David P. Siderovski†ʈ *Structural Genomics Consortium, Oxford University, Oxford OX3 7DQ, United Kingdom; †Department of Pharmacology, University of North Carolina, Chapel Hill, NC 27599; and ‡Leibniz-Institut fur¨ Molekulare Pharmakologie, Robert-Rössle Strasse 10, 13125 Berlin, Germany Communicated by Melvin I. Simon, California Institute of Technology, Pasadena, CA, February 14, 2008 (received for review December 18, 2007) Regulator of G protein signaling (RGS) proteins accelerate GTP RGS domain binding accelerates G␣-mediated GTP hydrolysis. hydrolysis by G␣ subunits and thus facilitate termination of sig- Thirty-seven proteins containing at least one region of homology to naling initiated by G protein-coupled receptors (GPCRs). RGS pro- the archetypal RGS domain fold are encoded by the human teins hold great promise as disease intervention points, given their genome (5), broadly classified into eight subfamilies. The R4 signature role as negative regulators of GPCRs—receptors to which (RGS1, -2, -3, -4, -5, -8, -13, -16, -18, -21) and RZ (RGS17, -19, -20) the largest fraction of approved medications are currently directed. subfamilies generally constitute little more than an RGS domain, RGS proteins share a hallmark RGS domain that interacts most although roles for short N-terminal extensions in membrane- avidly with G␣ when in its transition state for GTP hydrolysis; by targeting and receptor-selective functions have been described (8). binding and stabilizing switch regions I and II of G␣, RGS domain The R7 subfamily of RGS6, -7, -9, and -11 form dimers with G␤5 binding consequently accelerates G␣-mediated GTP hydrolysis. The (9) via a G␥-like domain N-terminal to their RGS domain. The R12 human genome encodes more than three dozen RGS domain- subfamily constitutes RGS10, -12, and -14, with the latter two containing proteins with varied G␣ substrate specificities. To fa- sharing additional domains reflecting unique roles as Ras/Raf/ cilitate their exploitation as drug-discovery targets, we have taken MAPK scaffolds (10). The other four subfamilies are signaling a systematic structural biology approach toward cataloging the regulators that have since become associated with the RGS protein structural diversity present among RGS domains and identifying superfamily (5, 11) upon discovery of more distantly related RGS ␣ molecular determinants of their differential G selectivities. Here, domains within them (i.e., Axin, Axil; RhoGEFs p115-RhoGEF, we determined 14 structures derived from NMR and x-ray crystal- LARG, and PDZ-RhoGEF; the sorting nexins SNX13, -14, and lography of members of the R4, R7, R12, and RZ subfamilies of RGS -25; the GPCR kinases GRK1–7). The RGS domains within the proteins, including 10 uncomplexed RGS domains and 4 RGS RhoGEF and GPCR kinase subfamilies have also been referred to ␣ domain/G complexes. Heterogeneity observed in the structural as rgRGS or RH domains, respectively (12, 13). architecture of the RGS domain, as well as in engagement of switch RGS proteins control the timing and duration of specific ␣ III and the all-helical domain of the G substrate, suggests that physiological processes that involve GPCR signaling. For exam- unique structural determinants specific to particular RGS pro- ␣ ple, RGS2-deficient mice exhibit heightened anxiety (14, 15) and tein/G pairings exist and could be used to achieve selective constitutive hypertension (16), the latter caused by loss of inhibition by small molecules. homeostatic control over vasoconstrictive hormonal signaling via Gq-coupled GPCRs in the vasculature (17). RGS2 is unique GTPase-accelerating proteins ͉ NMR structure ͉ RGS proteins ͉ x-ray among the R4 subfamily in its selectivity for G␣q in vitro (18), crystallography although in the context of intact cells or reconstituted receptor/ heterotrimer complexes, activity of RGS2 on G␣i-mediated protein-coupled receptors (GPCRs) are critical for many signaling is also seen (19, 20). Differences in G␣ selectivity lie, PHARMACOLOGY Gphysiological processes including vision, olfaction, neuro- at least in part, in heterogeneity within the structural determi- transmission, and the actions of many hormones (1). As such, nants of G␣ engagement by the RGS domain; initial evidence of GPCRs are the largest fraction of the ‘‘druggable proteome,’’ heterogeneity was seen in the structures of p115-RhoGEF and and their ligand-binding and signaling properties remain of considerable interest to academia and industry (2). GPCRs catalyze activation of heterotrimeric G proteins comprising a Author contributions: M. Soundararajan, F.S.W., A.J.K., D.A.D., and D.P.S. designed re- guanine nucleotide-binding G␣ subunit and an obligate G␤␥ search; M. Soundararajan, F.S.W., A.J.K., A.P.T., L.J.B., G.A.S., C.G., O.Y.F., E.F.D., V.A.H., dimer (3). Receptor–promoted activation of G␣␤␥ causes ex- and S.Q.H. performed research; F.S.W. and A.J.K. contributed new reagents/analytic tools; ␣ M. Soundararajan, F.S.W., A.J.K., A.P.T., and D.P.S. analyzed data; and M. Soundararajan, change of GDP for GTP by G and resultant dissociation of F.S.W., A.J.K., M. Sundstro¨m, and D.P.S. wrote the paper. ␤␥ ␣ ␤␥ G . GTP-bound G and freed G then regulate intracellular The authors declare no conflict of interest. effectors such as adenylyl cyclase, phospholipase C, ion channels, Data Deposition: The atomic coordinates and structure factors have been deposited in the RhoGEFs, and phosphodiesterases (1, 4). This ‘‘G protein cycle’’ Protein Data Bank, www.pdb.org [PDB ID codes 1ZV4, 2A72, 2AF0, 2BT2, 2BV1, 2ES0, 2GTP, is reset by the intrinsic GTP hydrolysis activity of G␣, producing 2IHB, 2IHD, 2IK8, and 2ODE (crystal structures); 2I59, 2JM5, 2JNU, and 2OWI (NMR struc- G␣⅐GDP that favors heterotrimer reformation and, conse- tures)]. quently, signal termination. Thus, a major determinant of the §Present Address: Novo Nordisk Foundation Center for Protein Research, University of duration and magnitude of GPCR signaling is the lifetime of G␣ Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark. in the GTP-bound state. ¶To whom correspondence may be addressed at: Old Road Campus Research Building, Roosevelt Drive, Oxford University, Oxford OX3 7DQ, United Kingdom. E-mail: Regulators of G protein signaling are GTPase-accelerating pro- [email protected]. teins (GAPs) for G␣ subunits and thus facilitate GPCR signal ʈTo whom correspondence may be addressed at: Department of Pharmacology, University termination (5). GAP activity is conferred by an RGS domain of North Carolina, Campus Box 7365, 1106 Mary Ellen Jones Building, Chapel Hill, NC 27599. present in one or more copies within members of this protein E-mail: [email protected]. superfamily (5). The archetypal RGS domain is composed of nine This article contains supporting information online at www.pnas.org/cgi/content/full/ ␣-helices (6) and binds most avidly to G␣ in the transition state for 0801508105/DCSupplemental. GTP hydrolysis (7); by stabilizing the flexible switch regions of G␣, © 2008 by The National Academy of Sciences of the USA www.pnas.org͞cgi͞doi͞10.1073͞pnas.0801508105 PNAS ͉ April 29, 2008 ͉ vol. 105 ͉ no. 17 ͉ 6457–6462 Downloaded by guest on September 27, 2021 A B C D Fig. 1. Heterogeneity in the ␣V–␣VII regions of R12 subfamily RGS domains versus the canonical RGS domain fold of R4, R7, and RZ subfamily members. (A) Apo-RGS domains of R4 subfamily member RGS8 (green; PDB ID 2IHD), R7 subfamily member RGS9 (orange; PDB ID 1FQI), and RZ subfamily member RGS19 (gray; PDB ID 1CMZ) were aligned along helices ␣IV and ␣V and superimposed by using PyMOL. (B–D) Apo-RGS domains of RGS14 (B) (blue; PDB ID 2JNU), RGS10 from this study (C) (salmon; PDB ID 2I59), and RGS10 from Yokoyama et al. (D) (light purple; PDB ID 2DLR) are presented to highlight differences in the ␣V–␣VI–␣VII region. The heterogeneous ␣VI regions are specifically highlighted in cyan (B), red (C), and magenta (D), respectively. GRK2 bound to G␣13 and G␣q, respectively (12, 13), that exhibit the Protein Data Bank (PDB) by others (Table S2 and refs. 21, unique contacts not observed in the first resolved structures [i.e., 22, 27, and 28). Canonical RGS domains consist of a nine-helix RGS4/G␣i1 and RGS9/G␣t; (6, 21)]. To assess the structural bundle comprising two lobes formed by the ␣I, ␣II, ␣III, ␣VIII, diversity and G␣ selectivities of RGS domains, we have taken a and ␣IX helices and the ␣IV, ␣V, ␣VI, and ␣VII helices, systematic structural biology approach and present 14 structures respectively. The majority of apo-RGS domain structures we of RGS domains from the R4, RZ, R7, and R12 subfamilies, obtained conform to the structural archetype established by including four RGS domain/G␣ complexes, two of which involve RGS4 (6, 28). Crystal structures of RGS6 and RGS7 (PDB IDs ␣ G i3. In an accompanying work, Slep et al. (22) describe two 2ES0 and 2A72) are atypical, domain-swapped dimers. The additional structures of RGS16—uncomplexed and bound to significance of such dimerization is not known and likely arises ␣ G o. Detailed knowledge of RGS protein substrate specificity, because of crystal packing-induced interactions; an NMR solu- and the unique structural determinants underlying such speci- tion structure of RGS7 (PDB ID 2D9J) conforms to the canon- ficity, should greatly facilitate exploitation of these GPCR ical RGS domain structure. signaling regulators as drug targets (23). Substantial differences were observed between the R12 sub- Results and Discussion family RGS domain structures and the prototypical RGS do- mains of R4, R7, and RZ subfamily members (Fig.
Recommended publications
  • Critical Role for Non‑GAP Function of Gαs in RGS1‑Mediated Promotion of Melanoma Progression Through AKT and ERK Phosphorylation
    ONCOLOGY REPORTS 39: 2673-2680, 2018 Critical role for non‑GAP function of Gαs in RGS1‑mediated promotion of melanoma progression through AKT and ERK phosphorylation MENG-YAN SUN1*, YUCHONG WANG2*, JI ZHU2, CHUAN LV2, KAI WU2, XIN-WEI WANG2 and CHUN-YU XUE2 1Resident Standardized Training Center, Changzheng Hospital, The Second Military Medical University, Shanghai 200001; 2Department of Plastic Surgery, Changhai Hospital, The Second Military Medical University, Shanghai 200433, P.R. China Received October 17, 2017; Accepted March 13, 2018 DOI: 10.3892/or.2018.6341 Abstract. Regulator of G-protein signaling 1 (RGS1) has been early gene responsive to several B-cell activation signals (2), found to be a critical factor in melanoma and other malignan- and it has been shown to be related to the regulation of cies. However, the mechanism involved in the RGS1-mediated chemokine-induced signaling in B cells (3). The RGS1 gene promotion of melanoma progression is not clear. We based our resides at 1q31, which is involved in several malignancies by study on samples collected from pathological specimens of gains or amplifications in certain subtypes of melanoma (4), melanoma patients. We found by immunohistochemistry that non-Hodgkin lymphoma (5), retinoblastoma (6), pancreatic RGS1 expression was significantly higher in melanoma than cancer (7) and nasopharyngeal carcinoma (8). RGS1 has that noted in nevus tissue (P<0.05). Kaplan-Meier analysis been shown to be upregulated by gene expression profiling in demonstrated a significant correlation between increased RGS1 several different tumor model systems. For example, RGS1 has expression and reduced disease‑specific survival (P<0.05).
    [Show full text]
  • RGS12 Is a Novel Tumor-Suppressor Gene in African American Prostate
    Published OnlineFirst June 13, 2017; DOI: 10.1158/0008-5472.CAN-17-0669 Cancer Molecular and Cellular Pathobiology Research RGS12 Is a Novel Tumor-Suppressor Gene in African American Prostate Cancer That Represses AKT and MNX1 Expression Yongquan Wang1,2, Jianghua Wang2, Li Zhang2,3, Omer Faruk Karatas2, Longjiang Shao2, Yiqun Zhang4, Patricia Castro2, Chad J. Creighton4,5,and Michael Ittmann2 Abstract African American (AA) men exhibit a relatively high incidence epithelial cells. Notably, RGS12 exhibited potent tumor-suppres- and mortality due to prostate cancer even after adjustment for sor activity in prostate cancer and prostate epithelial cell lines in socioeconomic factors, but the biological basis for this disparity is vitro and in vivo. We found that RGS12 expression correlated unclear. Here, we identify a novel region on chromosome 4p16.3 negatively with the oncogene MNX1 and regulated its expression that is lost selectively in AA prostate cancer. The negative regulator in vitro and in vivo. Further, MNX1 was regulated by AKT activity, of G-protein signaling RGS12 was defined as the target of 4p16.3 and RGS12 expression decreased total and activated AKT levels. deletions, although it has not been implicated previously as a Our findings identify RGS12 as a candidate tumor-suppressor tumor-suppressor gene. RGS12 transcript levels were relatively gene in AA prostate cancer, which acts by decreasing expression of reduced in AA prostate cancer, and prostate cancer cell lines AKT and MNX1, establishing a novel oncogenic axis in this showed decreased RGS12 expression relative to benign prostate disparate disease setting. Cancer Res; 77(16); 1–11.
    [Show full text]
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [Show full text]
  • A Core Program of Gene Expression Characterizes Cancer Metastases
    www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 60), pp: 102161-102175 Research Paper A core program of gene expression characterizes cancer metastases Franz Hartung1, Yunguan Wang2, Bruce Aronow2 and Georg F. Weber1 1University of Cincinnati Academic Health Center, Cincinnati, OH, USA 2Computational Medicine Center, Cincinnati Children’s Hospital, Cincinnati, OH, USA Correspondence to: Georg F. Weber, email: [email protected] Keywords: metastasis; metabolism; vascularization; extracellular matrix; ion homeostasis Received: July 28, 2017 Accepted: August 31, 2017 Published: November 02, 2017 Copyright: Hartung et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT While aberrant expression or splicing of metastasis genes conveys to cancers the ability to break through tissue barriers and disseminate, the genetic basis for organ preference in metastasis formation has remained incompletely understood. Utilizing the gene expression profiles from 653 GEO datasets, we investigate whether the signatures by diverse cancers in various metastatic sites display common features. We corroborate the meta-analysis in a murine model. Metastases are generally characterized by a core program of gene expression that induces the oxidative metabolism, activates vascularization/tissue remodeling, silences extracellular matrix interactions, and alters ion homeostasis. This program distinguishes metastases from their originating primary tumors as well as from their target host tissues. Site- selectivity is accomplished through specific components that adjust to the target micro-environment. The same functional groups of gene expression programs are activated in the metastases of B16-F10 cells to various target organs.
    [Show full text]
  • Emerging Roles for 3 Utrs in Neurons
    International Journal of Molecular Sciences Review 0 Emerging Roles for 3 UTRs in Neurons Bongmin Bae and Pedro Miura * Department of Biology, University of Nevada, Reno, NV 89557, USA; [email protected] * Correspondence: [email protected] Received: 8 April 2020; Accepted: 9 May 2020; Published: 12 May 2020 Abstract: The 30 untranslated regions (30 UTRs) of mRNAs serve as hubs for post-transcriptional control as the targets of microRNAs (miRNAs) and RNA-binding proteins (RBPs). Sequences in 30 UTRs confer alterations in mRNA stability, direct mRNA localization to subcellular regions, and impart translational control. Thousands of mRNAs are localized to subcellular compartments in neurons—including axons, dendrites, and synapses—where they are thought to undergo local translation. Despite an established role for 30 UTR sequences in imparting mRNA localization in neurons, the specific RNA sequences and structural features at play remain poorly understood. The nervous system selectively expresses longer 30 UTR isoforms via alternative polyadenylation (APA). The regulation of APA in neurons and the neuronal functions of longer 30 UTR mRNA isoforms are starting to be uncovered. Surprising roles for 30 UTRs are emerging beyond the regulation of protein synthesis and include roles as RBP delivery scaffolds and regulators of alternative splicing. Evidence is also emerging that 30 UTRs can be cleaved, leading to stable, isolated 30 UTR fragments which are of unknown function. Mutations in 30 UTRs are implicated in several neurological disorders—more studies are needed to uncover how these mutations impact gene regulation and what is their relationship to disease severity. Keywords: 30 UTR; alternative polyadenylation; local translation; RNA-binding protein; RNA-sequencing; post-transcriptional regulation 1.
    [Show full text]
  • Supplemental Material
    Supplemental Table B ARGs in alphabetical order Symbol Title 3 months 6 months 9 months 12 months 23 months ANOVA Direction Category 38597 septin 2 1557 ± 44 1555 ± 44 1579 ± 56 1655 ± 26 1691 ± 31 0.05219 up Intermediate 0610031j06rik kidney predominant protein NCU-G1 491 ± 6 504 ± 14 503 ± 11 527 ± 13 534 ± 12 0.04747 up Early Adult 1G5 vesicle-associated calmodulin-binding protein 662 ± 23 675 ± 17 629 ± 16 617 ± 20 583 ± 26 0.03129 down Intermediate A2m alpha-2-macroglobulin 262 ± 7 272 ± 8 244 ± 6 290 ± 7 353 ± 16 0.00000 up Midlife Aadat aminoadipate aminotransferase (synonym Kat2) 180 ± 5 201 ± 12 223 ± 7 244 ± 14 275 ± 7 0.00000 up Early Adult Abca2 ATP-binding cassette, sub-family A (ABC1), member 2 958 ± 28 1052 ± 58 1086 ± 36 1071 ± 44 1141 ± 41 0.05371 up Early Adult Abcb1a ATP-binding cassette, sub-family B (MDR/TAP), member 1A 136 ± 8 147 ± 6 147 ± 13 155 ± 9 185 ± 13 0.01272 up Midlife Acadl acetyl-Coenzyme A dehydrogenase, long-chain 423 ± 7 456 ± 11 478 ± 14 486 ± 13 512 ± 11 0.00003 up Early Adult Acadvl acyl-Coenzyme A dehydrogenase, very long chain 426 ± 14 414 ± 10 404 ± 13 411 ± 15 461 ± 10 0.01017 up Late Accn1 amiloride-sensitive cation channel 1, neuronal (degenerin) 242 ± 10 250 ± 9 237 ± 11 247 ± 14 212 ± 8 0.04972 down Late Actb actin, beta 12965 ± 310 13382 ± 170 13145 ± 273 13739 ± 303 14187 ± 269 0.01195 up Midlife Acvrinp1 activin receptor interacting protein 1 304 ± 18 285 ± 21 274 ± 13 297 ± 21 341 ± 14 0.03610 up Late Adk adenosine kinase 1828 ± 43 1920 ± 38 1922 ± 22 2048 ± 30 1949 ± 44 0.00797 up Early
    [Show full text]
  • Gene PMID WBS Locus ABR 26603386 AASDH 26603386
    Supplementary material J Med Genet Gene PMID WBS Locus ABR 26603386 AASDH 26603386 ABCA1 21304579 ABCA13 26603386 ABCA3 25501393 ABCA7 25501393 ABCC1 25501393 ABCC3 25501393 ABCG1 25501393 ABHD10 21304579 ABHD11 25501393 yes ABHD2 25501393 ABHD5 21304579 ABLIM1 21304579;26603386 ACOT12 25501393 ACSF2,CHAD 26603386 ACSL4 21304579 ACSM3 26603386 ACTA2 25501393 ACTN1 26603386 ACTN3 26603386;25501393;25501393 ACTN4 21304579 ACTR1B 21304579 ACVR2A 21304579 ACY3 19897463 ACYP1 21304579 ADA 25501393 ADAM12 21304579 ADAM19 25501393 ADAM32 26603386 ADAMTS1 25501393 ADAMTS10 25501393 ADAMTS12 26603386 ADAMTS17 26603386 ADAMTS6 21304579 ADAMTS7 25501393 ADAMTSL1 21304579 ADAMTSL4 25501393 ADAMTSL5 25501393 ADCY3 25501393 ADK 21304579 ADRBK2 25501393 AEBP1 25501393 AES 25501393 AFAP1,LOC84740 26603386 AFAP1L2 26603386 AFG3L1 21304579 AGAP1 26603386 AGAP9 21304579 Codina-Sola M, et al. J Med Genet 2019; 56:801–808. doi: 10.1136/jmedgenet-2019-106080 Supplementary material J Med Genet AGBL5 21304579 AGPAT3 19897463;25501393 AGRN 25501393 AGXT2L2 25501393 AHCY 25501393 AHDC1 26603386 AHNAK 26603386 AHRR 26603386 AIDA 25501393 AIFM2 21304579 AIG1 21304579 AIP 21304579 AK5 21304579 AKAP1 25501393 AKAP6 21304579 AKNA 21304579 AKR1E2 26603386 AKR7A2 21304579 AKR7A3 26603386 AKR7L 26603386 AKT3 21304579 ALDH18A1 25501393;25501393 ALDH1A3 21304579 ALDH1B1 21304579 ALDH6A1 21304579 ALDOC 21304579 ALG10B 26603386 ALG13 21304579 ALKBH7 25501393 ALPK2 21304579 AMPH 21304579 ANG 21304579 ANGPTL2,RALGPS1 26603386 ANGPTL6 26603386 ANK2 21304579 ANKMY1 26603386 ANKMY2
    [Show full text]
  • 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.
    [Show full text]
  • Hypertension and Prolonged Vasoconstrictor Signaling in RGS2- Deficient Mice
    Amendment history: Addendum (April 2003) Hypertension and prolonged vasoconstrictor signaling in RGS2- deficient mice Scott P. Heximer, … , Robert P. Mecham, Kendall J. Blumer J Clin Invest. 2003;111(4):445-452. https://doi.org/10.1172/JCI15598. Article Cardiology Signaling by hormones and neurotransmitters that activate G protein–coupled receptors (GPCRs) maintains blood pressure within the normal range despite large changes in cardiac output that can occur within seconds. This implies that blood pressure regulation requires precise kinetic control of GPCR signaling. To test this hypothesis, we analyzed mice deficient in RGS2, a GTPase-activating protein that greatly accelerates the deactivation rate of heterotrimeric G proteins in vitro. Both rgs2+/– and rgs2–/– mice exhibited a strong hypertensive phenotype, renovascular abnormalities, persistent constriction of the resistance vasculature, and prolonged response of the vasculature to vasoconstrictors in vivo. Analysis of P2Y receptor–mediated Ca2+ signaling in vascular smooth muscle cells in vitro indicated that loss of RGS2 increased agonist potency and efficacy and slowed the kinetics of signal termination. These results establish that abnormally prolonged signaling by G protein–coupled vasoconstrictor receptors can contribute to the onset of hypertension, and they suggest that genetic defects affecting the function or expression of RGS2 may be novel risk factors for development of hypertension in humans. Find the latest version: https://jci.me/15598/pdf Hypertension and prolonged See the related Commentary beginning on page 441. vasoconstrictor signaling in RGS2-deficient mice Scott P. Heximer,1 Russell H. Knutsen,1 Xiaoguang Sun,1 Kevin M. Kaltenbronn,1 Man-Hee Rhee,1 Ning Peng,2 Antonio Oliveira-dos-Santos,3 Josef M.
    [Show full text]
  • Transcriptional Control of Tissue-Resident Memory T Cell Generation
    Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells.
    [Show full text]
  • Ck1δ Over-Expressing Mice Display ADHD-Like Behaviors, Frontostriatal Neuronal Abnormalities and Altered Expressions of ADHD-Candidate Genes
    Molecular Psychiatry (2020) 25:3322–3336 https://doi.org/10.1038/s41380-018-0233-z ARTICLE CK1δ over-expressing mice display ADHD-like behaviors, frontostriatal neuronal abnormalities and altered expressions of ADHD-candidate genes 1 1 1 2 1 1 1 Mingming Zhou ● Jodi Gresack ● Jia Cheng ● Kunihiro Uryu ● Lars Brichta ● Paul Greengard ● Marc Flajolet Received: 8 November 2017 / Revised: 4 July 2018 / Accepted: 18 July 2018 / Published online: 19 October 2018 © Springer Nature Limited 2018 Abstract The cognitive mechanisms underlying attention-deficit hyperactivity disorder (ADHD), a highly heritable disorder with an array of candidate genes and unclear genetic architecture, remain poorly understood. We previously demonstrated that mice overexpressing CK1δ (CK1δ OE) in the forebrain show hyperactivity and ADHD-like pharmacological responses to D- amphetamine. Here, we demonstrate that CK1δ OE mice exhibit impaired visual attention and a lack of D-amphetamine- induced place preference, indicating a disruption of the dopamine-dependent reward pathway. We also demonstrate the presence of abnormalities in the frontostriatal circuitry, differences in synaptic ultra-structures by electron microscopy, as 1234567890();,: 1234567890();,: well as electrophysiological perturbations of both glutamatergic and GABAergic transmission, as observed by altered frequency and amplitude of mEPSCs and mIPSCs. Furthermore, gene expression profiling by next-generation sequencing alone, or in combination with bacTRAP technology to study specifically Drd1a versus Drd2 medium spiny neurons, revealed that developmental CK1δ OE alters transcriptional homeostasis in the striatum, including specific alterations in Drd1a versus Drd2 neurons. These results led us to perform a fine molecular characterization of targeted gene networks and pathway analysis. Importantly, a large fraction of 92 genes identified by GWAS studies as associated with ADHD in humans are significantly altered in our mouse model.
    [Show full text]
  • Characterizing the Mechanisms of Kappa Opioid Receptor Signaling Within Mesolimbic Dopamine Circuitry Katie Reichard a Dissertat
    Characterizing the mechanisms of kappa opioid receptor signaling within mesolimbic dopamine circuitry Katie Reichard A dissertation submitted in partial fulfillment of the degree requirements for the degree of: Doctor of Philosophy University of Washington 2020 Reading Committee: Charles Chavkin, Chair Paul Phillips Larry Zweifel Program Authorized to Confer Degree: Neuroscience Graduate Program TABLE OF CONTENTS Summary/Abstract………………………………………………………………………….……..6 Dedication……………………………………………………………………………….………...9 Chapter 1 The therapeutic potential of the targeting the kappa opioid receptor system in stress- associated mental health disorders……………………………….………………………………10 Section 1.1 Activation of the dynorphin/kappa opioid receptor system is associated with dysphoria, cognitive disruption, and increased preference for drugs of abuse…………………..13 Section 1.2 Contribution of the dyn/KOR system to substance use disorder, anxiety, and depression………………………………………………………………………………………..15 Section 1.3 KORs are expressed on dorsal raphe serotonin neurons and contribute to stress- induced plasticity with serotonin circuitry……………………………………………………….17 Section 1.4 Kappa opioid receptor expression in the VTA contributes to the behavioral response to stress……………………………………………………………………………………....…..19 Section 1.5 Other brain regions contributing to the KOR-mediated response to stress…………23 Section 1.6 G Protein signaling at the KOR …………………………………………………….25 Chapter 2: JNK-Receptor Inactivation Affects D2 Receptor through both agonist action and norBNI-mediated cross-inactivation
    [Show full text]