Expression Profiling of Human Milk Derived Exosomal Micrornas And
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Nuclear TARBP2 Drives Oncogenic Dysregulation of RNA Splicing and Decay
bioRxiv preprint doi: https://doi.org/10.1101/389213; this version posted August 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Nuclear TARBP2 drives oncogenic dysregulation of RNA splicing and decay Lisa Fish1,2,3, Hoang C.B. Nguyen4, Steven Zhang1,2,3, Myles Hochman1,2,3, Brian D. Dill5, Henrik Molina5, Hamed S. Najafabadi6,7, Claudio Alarcon8,9, Hani Goodarzi1,2,3* 1 Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA 2 Department of Urology, University of California, San Francisco, San Francisco, CA 94158, USA 3 Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA 4 Laboratory of Systems Cancer Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA 5 Proteome Resource Center, The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA 6 Department of Human Genetics, McGill University, Montreal, QC, Canada, H3A 0C7 7 McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada, H3A 0G1 8 Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA 9 Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA *Corresponding author: Hani Goodarzi 600 16th St. San Francisco, CA 94158 Phone: 415-230-5189 Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/389213; this version posted August 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
The Malignant Phenotype in Breast Cancer Is Driven by Eif4a1-Mediated Changes in the Translational Landscape
Citation: Cell Death and Disease (2015) 6, e1603; doi:10.1038/cddis.2014.542 OPEN & 2015 Macmillan Publishers Limited All rights reserved 2041-4889/15 www.nature.com/cddis The malignant phenotype in breast cancer is driven by eIF4A1-mediated changes in the translational landscape A Modelska1, E Turro1,2, R Russell1, J Beaton1, T Sbarrato3, K Spriggs4, J Miller1, S Gräf1,2,5, E Provenzano6,7, F Blows8, P Pharoah6,8, C Caldas1,6 and J Le Quesne*,1,3 Human mRNA DeXD/H-box helicases are ubiquitous molecular motors that are required for the majority of cellular processes that involve RNA metabolism. One of the most abundant is eIF4A, which is required during the initiation phase of protein synthesis to unwind regions of highly structured mRNA that would otherwise impede the scanning ribosome. Dysregulation of protein synthesis is associated with tumorigenesis, but little is known about the detailed relationships between RNA helicase function and the malignant phenotype in solid malignancies. Therefore, immunohistochemical analysis was performed on over 3000 breast tumors to investigate the relationship among expression of eIF4A1, the helicase-modulating proteins eIF4B, eIF4E and PDCD4, and clinical outcome. We found eIF4A1, eIF4B and eIF4E to be independent predictors of poor outcome in ER-negative disease, while in contrast, the eIF4A1 inhibitor PDCD4 was related to improved outcome in ER-positive breast cancer. Consistent with these data, modulation of eIF4A1, eIF4B and PCDC4 expression in cultured MCF7 cells all restricted breast cancer cell growth and cycling. The eIF4A1-dependent translatome of MCF7 cells was defined by polysome profiling, and was shown to be highly enriched for several classes of oncogenic genes, including G-protein constituents, cyclins and protein kinases, and for mRNAs with G/C-rich 5′UTRs with potential to form G-quadruplexes and with 3′UTRs containing microRNA target sites. -
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. -
Effects of Rapamycin on Social Interaction Deficits and Gene
Kotajima-Murakami et al. Molecular Brain (2019) 12:3 https://doi.org/10.1186/s13041-018-0423-2 RESEARCH Open Access Effects of rapamycin on social interaction deficits and gene expression in mice exposed to valproic acid in utero Hiroko Kotajima-Murakami1,2, Toshiyuki Kobayashi3, Hirofumi Kashii1,4, Atsushi Sato1,5, Yoko Hagino1, Miho Tanaka1,6, Yasumasa Nishito7, Yukio Takamatsu7, Shigeo Uchino1,2 and Kazutaka Ikeda1* Abstract The mammalian target of rapamycin (mTOR) signaling pathway plays a crucial role in cell metabolism, growth, and proliferation. The overactivation of mTOR has been implicated in the pathogenesis of syndromic autism spectrum disorder (ASD), such as tuberous sclerosis complex (TSC). Treatment with the mTOR inhibitor rapamycin improved social interaction deficits in mouse models of TSC. Prenatal exposure to valproic acid (VPA) increases the incidence of ASD. Rodent pups that are exposed to VPA in utero have been used as an animal model of ASD. Activation of the mTOR signaling pathway was recently observed in rodents that were exposed to VPA in utero, and rapamycin ameliorated social interaction deficits. The present study investigated the effect of rapamycin on social interaction deficits in both adolescence and adulthood, and gene expressions in mice that were exposed to VPA in utero. We subcutaneously injected 600 mg/kg VPA in pregnant mice on gestational day 12.5 and used the pups as a model of ASD. The pups were intraperitoneally injected with rapamycin or an equal volume of vehicle once daily for 2 consecutive days. The social interaction test was conducted in the offspring after the last rapamycin administration at 5–6 weeks of ages (adolescence) or 10–11 weeks of age (adulthood). -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
Serum Albumin OS=Homo Sapiens
Protein Name Cluster of Glial fibrillary acidic protein OS=Homo sapiens GN=GFAP PE=1 SV=1 (P14136) Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 Cluster of Isoform 3 of Plectin OS=Homo sapiens GN=PLEC (Q15149-3) Cluster of Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 (P68871) Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 Cluster of Tubulin beta-3 chain OS=Homo sapiens GN=TUBB3 PE=1 SV=2 (Q13509) Cluster of Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 (P60709) Cluster of Tubulin alpha-1B chain OS=Homo sapiens GN=TUBA1B PE=1 SV=1 (P68363) Cluster of Isoform 2 of Spectrin alpha chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTAN1 (Q13813-2) Hemoglobin subunit alpha OS=Homo sapiens GN=HBA1 PE=1 SV=2 Cluster of Spectrin beta chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTBN1 PE=1 SV=2 (Q01082) Cluster of Pyruvate kinase isozymes M1/M2 OS=Homo sapiens GN=PKM PE=1 SV=4 (P14618) Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 Clathrin heavy chain 1 OS=Homo sapiens GN=CLTC PE=1 SV=5 Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 Cytoplasmic dynein 1 heavy chain 1 OS=Homo sapiens GN=DYNC1H1 PE=1 SV=5 Cluster of ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide OS=Homo sapiens GN=ATP1A2 PE=3 SV=1 (B1AKY9) Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 Dihydropyrimidinase-related protein 2 OS=Homo sapiens GN=DPYSL2 PE=1 SV=1 Cluster of Alpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=2 (P12814) 60 kDa heat shock protein, mitochondrial OS=Homo -
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. -
Genome-Wide Recommendation of RNA–Protein Interactions Gianluca Corrado1,*, Toma Tebaldi2, Fabrizio Costa3, Paolo Frasconi4 and Andrea Passerini1,*
Bioinformatics, 32(23), 2016, 3627–3634 doi: 10.1093/bioinformatics/btw517 Advance Access Publication Date: 8 August 2016 Original Paper Data and text mining RNAcommender: genome-wide recommendation of RNA–protein interactions Gianluca Corrado1,*, Toma Tebaldi2, Fabrizio Costa3, Paolo Frasconi4 and Andrea Passerini1,* 1Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy, 2Centre for Integrative Biology, University of Trento, Trento 38123, Italy, 3Department of Computer Science, Albert- Ludwigs-Universitaet Freiburg, Freiburg 79110, Germany and 4Dipartimento di Ingegneria dell’Informazione, University of Florence, Florence 50139, Italy Associate Editor: Ivo Hofacker *To whom correspondence should be addressed. Received on March 17, 2016; revised on July 29, 2016; accepted on August 2, 2016 Abstract Motivation: Information about RNA–protein interactions is a vital pre-requisite to tackle the dissec- tion of RNA regulatory processes. Despite the recent advances of the experimental techniques, the currently available RNA interactome involves a small portion of the known RNA binding proteins. The importance of determining RNA–protein interactions, coupled with the scarcity of the available information, calls for in silico prediction of such interactions. Results: We present RNAcommender, a recommender system capable of suggesting RNA targets to unexplored RNA binding proteins, by propagating the available interaction information taking into account the protein domain composition and the RNA predicted secondary structure. Our re- sults show that RNAcommender is able to successfully suggest RNA interactors for RNA binding proteins using little or no interaction evidence. RNAcommender was tested on a large dataset of human RBP-RNA interactions, showing a good ranking performance (average AUC ROC of 0.75) and significant enrichment of correct recommendations for 75% of the tested RBPs. -
List of Down-Regulated Transcripts
Supplementary Table 1: List of down-regulated transcripts (Fold Change>1.7 and p<0.05). List of the transcipts downregulated by Tpr-Met in P27 cachectic gastrocnemius muscles. Shown are the transcripts gene annotation: Mus Musculus Entrez Gene ID, official symbol from NCBI, average (AVG) Log2 Ratio derived from Illumina microarrays (3 replicates/condition), fold change and p-value computations, Log2 ratio over AVG Log2 controls and full definition of the genes. All genes modulated with a fold change > 1.7 and p-value<0.05 are included. AVG Entrez Gene Fold Symbol Log2 T-test Ctrl Ctrl Ctrl TM TM TM Definition ID Change Ratio - - - - 26908 Eif2s3y 3.598 12.1 0.000 0.146 0.248 0.102 3.712 3.571 -3.510 eukaryotic translation initiation factor 2, subunit 3, structural gene Y-linked (Eif2s3y), mRNA. - - - 13628 Eef1a2 1.333 2.5 0.011 -0.444 0.261 0.183 1.503 0.950 -1.546 eukaryotic translation elongation factor 1 alpha 2 (Eef1a2), mRNA. - - - 13628 Eef1a2 1.298 2.5 0.031 -0.383 0.275 0.108 1.632 0.605 -1.656 eukaryotic translation elongation factor 1 alpha 2 (Eef1a2), mRNA. - - - 56222 Cited4 1.268 2.4 0.029 -0.466 0.035 0.431 1.109 0.889 -1.805 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 4 (Cited4), mRNA. - - - 18810 Plec1 1.246 2.4 0.022 -0.440 0.327 0.113 1.303 0.779 -1.657 plectin 1 (Plec1), transcript variant 10, mRNA. - - - 20741 Spnb1 1.238 2.4 0.016 -0.384 0.314 0.071 1.129 0.902 -1.684 spectrin beta 1 (Spnb1), mRNA. -
A Mode of Cell Adhesion and Migration Facilitated by CD44-Dependent Microtentacles
A mode of cell adhesion and migration facilitated by CD44-dependent microtentacles Kayla J. Wolfa,b, Poojan Shuklab, Kelsey Springerb, Stacey Leea,b, Jason D. Coombesb,c, Caleb J. Choyd, Samuel J. Kennye,KeXue,f, and Sanjay Kumara,b,g,1 aUniversity of California, Berkeley–University of California San Francisco Graduate Program in Bioengineering, Department of Bioengineering, University of California, Berkeley, CA 94720; bDepartment of Bioengineering, University of California, Berkeley, CA, 94720; cInflammation Biology, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom, SE5 9NU; dDepartment of Molecular and Cell Biology, University of California, Berkeley, CA 94720; eDepartment of Chemistry, University of California, Berkeley, CA 94720; fDivision of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720; and gDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720 Edited by David A. Weitz, Harvard University, Cambridge, MA, and approved March 25, 2020 (received for review August 19, 2019) The structure and mechanics of many connective tissues are grafted C6 gliomas (14). Furthermore, knockdown (KD) of dictated by a collagen-rich extracellular matrix (ECM), where col- CD44 in human GBM tumors slows tumor growth and sensitizes lagen fibers provide topological cues that direct cell migration. tumors to cytotoxic agents (15). CD44 is also a marker of glioma However, comparatively little is known about how cells navigate stem cells (GSCs) (also known as tumor-initiating cells) and the hyaluronic acid (HA)-rich, nanoporous ECM of the brain, a contributes to maintaining stemness (16). Our laboratory has problem with fundamental implications for development, inflam- shown that CD44 is necessary for adhesion and migration on mation, and tumor invasion. -
Distinct Roles of Different Fragments of PDCD4 in Regulating the Metastatic Behavior of B16 Melanoma Cells
INTERNATIONAL JOURNAL OF ONCOLOGY 42: 1725-1733, 2013 Distinct roles of different fragments of PDCD4 in regulating the metastatic behavior of B16 melanoma cells DI WANG, SHU GUO, SI-YUAN HAN, NAN XU, JIA-YAN GUO and QING SUN Department of Plastic Surgery, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China Received December 12, 2012; Accepted January 29, 2013 DOI: 10.3892/ijo.2013.1841 Abstract. Melanoma is an aggressive cutaneous malignancy. In mice (7,8). As shown in Fig. 1, full length human PDCD4 this study, we demonstrated that the levels of the programmed contains an N-terminal putative RNA-binding domain cell death 4 (PDCD4) protein and mRNA were lower in tumor (residues 1-140) and two tandem MA3 domains, designated tissues compared with normal tissues. In order to further as nMA3 (residues 164-275) and cMA3 (residues 327-440) investigate the effects of PDCD4 and its fragments in B16 (9). The two MA3 domains have very similar structure and melanoma cells, we established B16 clones with expression of eIF4A-binding surfaces (10). The molecular basis for PDCD4- different PDCD4 fragments. Intact PDCD4, PDCD4∆164-469 mediated suppression has been linked to its high affinity and PDCD4∆327-440 expression, respectively, decreased MA-3 domains (11,12). NMR binding analysis has shown proliferation and migration and increased apoptosis in B16 that both MA3 domains of PDCD4 interacted with eIF4A cells in vitro. We found that intact PDCD4, PDCD4∆164-469 and prevented translation (10,11). However, another study has or PDCD4∆327-440 can inhibit the activity of MMP-2 and the demonstrated that the cMA3 domain alone is sufficient for expression of CXCR4. -
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