Research Article Complex and Multidimensional Lipid Raft Alterations in a Murine Model of Alzheimer’S Disease
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Mir-338-3P Functions As a Tumor Suppressor in Gastric Cancer by Targeting PTP1B
Sun et al. Cell Death and Disease DOI 10.1038/s41419-018-0611-0 Cell Death & Disease ARTICLE Open Access miR-338-3p functions as a tumor suppressor in gastric cancer by targeting PTP1B Feng Sun1, Mengchao Yu2,JingYu2, Zhijian Liu1,XinyanZhou2,YanqingLiu2, Xiaolong Ge3,HaidongGao2, Mei Li4, Xiaohong Jiang2,SongLiu1,XiChen2 and Wenxian Guan 1 Abstract Gastric cancer (GC) is one of the most common malignant tumors and peritoneal metastasis is the primary cause for advanced GC’s mortality. Protein-tyrosine phosphatase 1B (PTP1B) functions as an oncogene and involves in carcinogenesis and cancer dissemination. However, the function and regulation of PTP1B in GC remain poorly understood. In this study, we found that PTP1B was upregulated in GC tissues and overexpression of PTP1B in vitro promoted cell migration and prevented apoptosis. Then, we predicted that PTP1B was a target of miR-338-3p and we revealed an inverse correlation between miR-338-3p levels and PTP1B protein levels in GC tissues. Next, we verified that PTP1B was inhibited by miR-338-3p via direct targeting to its 3′-untranslated regions. Moreover, overexpression of miR-338-3p in vitro attenuated GC cell migration and promoted apoptosis, and these effects could be partially reversed by reintroduction of PTP1B. Finally, we established an orthotopic xenograft model and a peritoneal dissemination model of GC to demonstrate that miR-338-3p restrained tumor growth and dissemination in vivo by targeting PTP1B. Taken together, our results highlight that PTP1B is an oncogene and is negatively regulated by miR- 1234567890():,; 1234567890():,; 338-3p in GC, which may provide new insights into novel molecular therapeutic targets for GC. -
Downloaded from the National Database for Autism Research (NDAR)
International Journal of Molecular Sciences Article Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions Elizabeth C. Lee y and Valerie W. Hu * Department of Biochemistry and Molecular Medicine, The George Washington University, School of Medicine and Health Sciences, Washington, DC 20037, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-202-994-8431 Current address: W. Harry Feinstone Department of Molecular Microbiology and Immunology, y Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. Received: 25 August 2020; Accepted: 17 September 2020; Published: 19 September 2020 Abstract: Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group. -
Aquaporin Channels in the Heart—Physiology and Pathophysiology
International Journal of Molecular Sciences Review Aquaporin Channels in the Heart—Physiology and Pathophysiology Arie O. Verkerk 1,2,* , Elisabeth M. Lodder 2 and Ronald Wilders 1 1 Department of Medical Biology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] 2 Department of Experimental Cardiology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] * Correspondence: [email protected]; Tel.: +31-20-5664670 Received: 29 March 2019; Accepted: 23 April 2019; Published: 25 April 2019 Abstract: Mammalian aquaporins (AQPs) are transmembrane channels expressed in a large variety of cells and tissues throughout the body. They are known as water channels, but they also facilitate the transport of small solutes, gasses, and monovalent cations. To date, 13 different AQPs, encoded by the genes AQP0–AQP12, have been identified in mammals, which regulate various important biological functions in kidney, brain, lung, digestive system, eye, and skin. Consequently, dysfunction of AQPs is involved in a wide variety of disorders. AQPs are also present in the heart, even with a specific distribution pattern in cardiomyocytes, but whether their presence is essential for proper (electro)physiological cardiac function has not intensively been studied. This review summarizes recent findings and highlights the involvement of AQPs in normal and pathological cardiac function. We conclude that AQPs are at least implicated in proper cardiac water homeostasis and energy balance as well as heart failure and arsenic cardiotoxicity. However, this review also demonstrates that many effects of cardiac AQPs, especially on excitation-contraction coupling processes, are virtually unexplored. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Oup Radres Rrz001 289..297 ++
Journal of Radiation Research, Vol. 60, No. 3, 2019, pp. 289–297 doi: 10.1093/jrr/rrz001 Advance Access Publication: 26 February 2019 Ionizing radiation affects the composition of the proteome of extracellular vesicles released by head-and-neck cancer cells in vitro Agata Abramowicz1, Anna Wojakowska1, Lukasz Marczak2, Malgorzata Lysek-Gladysinska3, Mateusz Smolarz1, Michael D. Story4, Joanna Polanska5, Piotr Widlak1 and Monika Pietrowska1,* 1Center for Translational Research and Molecular Biology of Cancer, Maria Sklodowska–Curie Institute–Oncology Center, Gliwice Branch, ul. Wybrzeze Armii Krajowej 15, 44-101 Gliwice, Poland 2Institute of Bioorganic Chemistry, Polish Academy of Sciences, ul. Noskowskiego 12/14, 61-704 Poznan, Poland 3The Jan Kochanowski University in Kielce, Institute of Biology, Department of Cell Biology and Electron Microscopy, ul. Swietokrzyska 15, 25-406 Kielce, Poland 4University of Texas Southwestern Medical Center, Department of Radiation Oncology, Division of Molecular Radiation Biology, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA 5Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland *Corresponding author. Center for Translational Research and Molecular Biology of Cancer, Maria Sklodowska–Curie Institute–Oncology Center, Gliwice Branch, ul. Wybrzeze Armii Krajowej 15, 44-101 Gliwice, Poland. Tel: +0048-32-278-9627; Fax: +0048-32-278-9840; Email: [email protected] (Received 29 August 2018; revised 7 November 2018; editorial decision 8 January 2019) ABSTRACT Exosomes and other extracellular vesicles are key players in cell-to-cell communication, and it has been proposed that they are involved in different aspects of the response to ionizing radiation, including transmitting the radiation-induced bystander effect and mediating radioresistance. -
Gpr161 Anchoring of PKA Consolidates GPCR and Camp Signaling
Gpr161 anchoring of PKA consolidates GPCR and cAMP signaling Verena A. Bachmanna,1, Johanna E. Mayrhofera,1, Ronit Ilouzb, Philipp Tschaiknerc, Philipp Raffeinera, Ruth Röcka, Mathieu Courcellesd,e, Federico Apeltf, Tsan-Wen Lub,g, George S. Baillieh, Pierre Thibaultd,i, Pia Aanstadc, Ulrich Stelzlf,j, Susan S. Taylorb,g,2, and Eduard Stefana,2 aInstitute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, 6020 Innsbruck, Austria; bDepartment of Chemistry and Biochemistry, University of California, San Diego, CA 92093; cInstitute of Molecular Biology, University of Innsbruck, 6020 Innsbruck, Austria; dInstitute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada H3C 3J7; eDépartement de Biochimie, Université de Montréal, Montreal, QC, Canada H3C 3J7; fOtto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; gDepartment of Pharmacology, University of California, San Diego, CA 92093; hInstitute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; iDepartment of Chemistry, Université de Montréal, Montreal, QC, Canada H3C 3J7; and jInstitute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, 8010 Graz, Austria Contributed by Susan S. Taylor, May 24, 2016 (sent for review February 18, 2016; reviewed by John J. G. Tesmer and Mark von Zastrow) Scaffolding proteins organize the information flow from activated G accounts for nanomolar binding affinities to PKA R subunit dimers protein-coupled receptors (GPCRs) to intracellular effector cascades (12, 13). Moreover, additional components of the cAMP signaling both spatially and temporally. By this means, signaling scaffolds, such machinery, such as GPCRs, adenylyl cyclases, and phosphodiester- as A-kinase anchoring proteins (AKAPs), compartmentalize kinase ases, physically interact with AKAPs (1, 5, 11, 14). -
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. -
Supplementary Table 3 Complete List of RNA-Sequencing Analysis of Gene Expression Changed by ≥ Tenfold Between Xenograft and Cells Cultured in 10%O2
Supplementary Table 3 Complete list of RNA-Sequencing analysis of gene expression changed by ≥ tenfold between xenograft and cells cultured in 10%O2 Expr Log2 Ratio Symbol Entrez Gene Name (culture/xenograft) -7.182 PGM5 phosphoglucomutase 5 -6.883 GPBAR1 G protein-coupled bile acid receptor 1 -6.683 CPVL carboxypeptidase, vitellogenic like -6.398 MTMR9LP myotubularin related protein 9-like, pseudogene -6.131 SCN7A sodium voltage-gated channel alpha subunit 7 -6.115 POPDC2 popeye domain containing 2 -6.014 LGI1 leucine rich glioma inactivated 1 -5.86 SCN1A sodium voltage-gated channel alpha subunit 1 -5.713 C6 complement C6 -5.365 ANGPTL1 angiopoietin like 1 -5.327 TNN tenascin N -5.228 DHRS2 dehydrogenase/reductase 2 leucine rich repeat and fibronectin type III domain -5.115 LRFN2 containing 2 -5.076 FOXO6 forkhead box O6 -5.035 ETNPPL ethanolamine-phosphate phospho-lyase -4.993 MYO15A myosin XVA -4.972 IGF1 insulin like growth factor 1 -4.956 DLG2 discs large MAGUK scaffold protein 2 -4.86 SCML4 sex comb on midleg like 4 (Drosophila) Src homology 2 domain containing transforming -4.816 SHD protein D -4.764 PLP1 proteolipid protein 1 -4.764 TSPAN32 tetraspanin 32 -4.713 N4BP3 NEDD4 binding protein 3 -4.705 MYOC myocilin -4.646 CLEC3B C-type lectin domain family 3 member B -4.646 C7 complement C7 -4.62 TGM2 transglutaminase 2 -4.562 COL9A1 collagen type IX alpha 1 chain -4.55 SOSTDC1 sclerostin domain containing 1 -4.55 OGN osteoglycin -4.505 DAPL1 death associated protein like 1 -4.491 C10orf105 chromosome 10 open reading frame 105 -4.491 -
Identification and Characterization of RHOA-Interacting Proteins in Bovine Spermatozoa1
BIOLOGY OF REPRODUCTION 78, 184–192 (2008) Published online before print 10 October 2007. DOI 10.1095/biolreprod.107.062943 Identification and Characterization of RHOA-Interacting Proteins in Bovine Spermatozoa1 Sarah E. Fiedler, Malini Bajpai, and Daniel W. Carr2 Department of Medicine, Oregon Health & Sciences University and Veterans Affairs Medical Center, Portland, Oregon 97239 ABSTRACT Guanine nucleotide exchange factors (GEFs) catalyze the GDP for GTP exchange [2]. Activation is negatively regulated by In somatic cells, RHOA mediates actin dynamics through a both guanine nucleotide dissociation inhibitors (RHO GDIs) GNA13-mediated signaling cascade involving RHO kinase and GTPase-activating proteins (GAPs) [1, 2]. Endogenous (ROCK), LIM kinase (LIMK), and cofilin. RHOA can be RHO can be inactivated via C3 exoenzyme ADP-ribosylation, negatively regulated by protein kinase A (PRKA), and it and studies have demonstrated RHO involvement in actin-based interacts with members of the A-kinase anchoring (AKAP) cytoskeletal response to extracellular signals, including lyso- family via intermediary proteins. In spermatozoa, actin poly- merization precedes the acrosome reaction, which is necessary phosphatidic acid (LPA) [2–4]. LPA is known to signal through for normal fertility. The present study was undertaken to G-protein-coupled receptors (GPCRs) [4, 5]; specifically, LPA- determine whether the GNA13-mediated RHOA signaling activated GNA13 (formerly Ga13) promotes RHO activation pathway may be involved in acrosome reaction in bovine through GEFs [4, 6]. Activated RHO-GTP then signals RHO caudal sperm, and whether AKAPs may be involved in its kinase (ROCK), resulting in the phosphorylation and activation targeting and regulation. GNA13, RHOA, ROCK2, LIMK2, and of LIM-kinase (LIMK), which in turn phosphorylates and cofilin were all detected by Western blot in bovine caudal inactivates cofilin, an actin depolymerizer, the end result being sperm. -
A SARS-Cov-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing Supplementary Information Supplementary Discussion All SARS-CoV-2 protein and gene functions described in the subnetwork appendices, including the text below and the text found in the individual bait subnetworks, are based on the functions of homologous genes from other coronavirus species. These are mainly from SARS-CoV and MERS-CoV, but when available and applicable other related viruses were used to provide insight into function. The SARS-CoV-2 proteins and genes listed here were designed and researched based on the gene alignments provided by Chan et. al. 1 2020 . Though we are reasonably sure the genes here are well annotated, we want to note that not every protein has been verified to be expressed or functional during SARS-CoV-2 infections, either in vitro or in vivo. In an effort to be as comprehensive and transparent as possible, we are reporting the sub-networks of these functionally unverified proteins along with the other SARS-CoV-2 proteins. In such cases, we have made notes within the text below, and on the corresponding subnetwork figures, and would advise that more caution be taken when examining these proteins and their molecular interactions. Due to practical limits in our sample preparation and data collection process, we were unable to generate data for proteins corresponding to Nsp3, Orf7b, and Nsp16. Therefore these three genes have been left out of the following literature review of the SARS-CoV-2 proteins and the protein-protein interactions (PPIs) identified in this study. -
Genome-Wide Association and Transcriptome Studies Identify Candidate Genes and Pathways for Feed Conversion Ratio in Pigs
Miao et al. BMC Genomics (2021) 22:294 https://doi.org/10.1186/s12864-021-07570-w RESEARCH ARTICLE Open Access Genome-wide association and transcriptome studies identify candidate genes and pathways for feed conversion ratio in pigs Yuanxin Miao1,2,3, Quanshun Mei1,2, Chuanke Fu1,2, Mingxing Liao1,2,4, Yan Liu1,2, Xuewen Xu1,2, Xinyun Li1,2, Shuhong Zhao1,2 and Tao Xiang1,2* Abstract Background: The feed conversion ratio (FCR) is an important productive trait that greatly affects profits in the pig industry. Elucidating the genetic mechanisms underpinning FCR may promote more efficient improvement of FCR through artificial selection. In this study, we integrated a genome-wide association study (GWAS) with transcriptome analyses of different tissues in Yorkshire pigs (YY) with the aim of identifying key genes and signalling pathways associated with FCR. Results: A total of 61 significant single nucleotide polymorphisms (SNPs) were detected by GWAS in YY. All of these SNPs were located on porcine chromosome (SSC) 5, and the covered region was considered a quantitative trait locus (QTL) region for FCR. Some genes distributed around these significant SNPs were considered as candidates for regulating FCR, including TPH2, FAR2, IRAK3, YARS2, GRIP1, FRS2, CNOT2 and TRHDE. According to transcriptome analyses in the hypothalamus, TPH2 exhibits the potential to regulate intestinal motility through serotonergic synapse and oxytocin signalling pathways. In addition, GRIP1 may be involved in glutamatergic and GABAergic signalling pathways, which regulate FCR by affecting appetite in pigs. Moreover, GRIP1, FRS2, CNOT2,andTRHDE may regulate metabolism in various tissues through a thyroid hormone signalling pathway. -
Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) Through Multikinase Inhibition Jessica D
Published OnlineFirst February 9, 2018; DOI: 10.1158/1078-0432.CCR-17-1928 Cancer Therapy: Preclinical Clinical Cancer Research Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) through Multikinase Inhibition Jessica D. Lang1,William P.D. Hendricks1, Krystal A. Orlando2, Hongwei Yin1, Jeffrey Kiefer1, Pilar Ramos1, Ritin Sharma3, Patrick Pirrotte3, Elizabeth A. Raupach1,3, Chris Sereduk1, Nanyun Tang1, Winnie S. Liang1, Megan Washington1, Salvatore J. Facista1, Victoria L. Zismann1, Emily M. Cousins4, Michael B. Major4, Yemin Wang5, Anthony N. Karnezis5, Aleksandar Sekulic1,6, Ralf Hass7, Barbara C. Vanderhyden8, Praveen Nair9, Bernard E. Weissman2, David G. Huntsman5,10, and Jeffrey M. Trent1 Abstract Purpose: Small cell carcinoma of the ovary, hypercalcemic type three SWI/SNF wild-type ovarian cancer cell lines. We further (SCCOHT) is a rare, aggressive ovarian cancer in young women identified ponatinib as the most effective clinically approved that is universally driven by loss of the SWI/SNF ATPase subunits RTK inhibitor. Reexpression of SMARCA4 was shown to confer SMARCA4 and SMARCA2. A great need exists for effective targeted a 1.7-fold increase in resistance to ponatinib. Subsequent therapies for SCCOHT. proteomic assessment of ponatinib target modulation in Experimental Design: To identify underlying therapeutic vul- SCCOHT cell models confirmed inhibition of nine known nerabilities in SCCOHT, we conducted high-throughput siRNA ponatinib target kinases alongside 77 noncanonical ponatinib and drug screens. Complementary proteomics approaches pro- targets in SCCOHT. Finally, ponatinib delayed tumor dou- filed kinases inhibited by ponatinib. Ponatinib was tested for bling time 4-fold in SCCOHT-1 xenografts while reducing efficacy in two patient-derived xenograft (PDX) models and one final tumor volumes in SCCOHT PDX models by 58.6% and cell-line xenograft model of SCCOHT.