1
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Wang T, Song D, Li X, Luo Y, Yang D, Liu X, Kong X, Xing Y, Bi S, Zhang Y, Hu T, Zhang Y, Dai S, Shao Z, Chen D, Hou J, Ballestar E, Cai J, Zheng F, Yang JY. MiR-574-5p activates human TLR8 to promote autoimmune signaling and lupus. Cell Commun Signal 2024; 22:220. [PMID: 38589923 PMCID: PMC11000404 DOI: 10.1186/s12964-024-01601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
Endosomal single-stranded RNA-sensing Toll-like receptor-7/8 (TLR7/8) plays a pivotal role in inflammation and immune responses and autoimmune diseases. However, the mechanisms underlying the initiation of the TLR7/8-mediated autoimmune signaling remain to be fully elucidated. Here, we demonstrate that miR-574-5p is aberrantly upregulated in tissues of lupus prone mice and in the plasma of lupus patients, with its expression levels correlating with the disease activity. miR-574-5p binds to and activates human hTLR8 or its murine ortholog mTlr7 to elicit a series of MyD88-dependent immune and inflammatory responses. These responses include the overproduction of cytokines and interferons, the activation of STAT1 signaling and B lymphocytes, and the production of autoantigens. In a transgenic mouse model, the induction of miR-574-5p overexpression is associated with increased secretion of antinuclear and anti-dsDNA antibodies, increased IgG and C3 deposit in the kidney, elevated expression of inflammatory genes in the spleen. In lupus-prone mice, lentivirus-mediated silencing of miR-574-5p significantly ameliorates major symptoms associated with lupus and lupus nephritis. Collectively, these results suggest that the miR-574-5p-hTLR8/mTlr7 signaling is an important axis of immune and inflammatory responses, contributing significantly to the development of lupus and lupus nephritis.
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Affiliation(s)
- Tao Wang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
- The Key Laboratory of Urinary Tract Tumors and Calculi, Department of Urology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, 361003, China
| | - Dan Song
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Xuejuan Li
- Wuhu Hospital of East China Normal University, Wuhu, Anhui, 241000, China
- Kidney Health Institute, Health Science Center, East China Normal University, Minhang, Shanghai, 200241, China
- Department of Nephrology, The Second Hospital, Dalian Medical University, Dalian, 116144, China
| | - Yu Luo
- School of Nursing, The Third Military Medical University, Chongqing, 400038, China
| | - Dianqiang Yang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Xiaoyan Liu
- Department of Nephrology, The Second Hospital, Dalian Medical University, Dalian, 116144, China
| | - Xiaodan Kong
- Department of Rheumatology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China
| | - Yida Xing
- Department of Rheumatology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China
| | - Shulin Bi
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Yan Zhang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Tao Hu
- College of Medicine, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Yunyun Zhang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Shuang Dai
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Zhiqiang Shao
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Dahan Chen
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Jinpao Hou
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China
| | - Esteban Ballestar
- Wuhu Hospital of East China Normal University, Wuhu, Anhui, 241000, China
- Kidney Health Institute, Health Science Center, East China Normal University, Minhang, Shanghai, 200241, China
- Epigenetics and Immune Disease Group, Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, 08916, Spain
| | - Jianchun Cai
- Department of Gastrointestinal Surgery, Institute of Gastrointestinal Oncology, Zhongshan Hospital of Xiamen University, Medical College of Xiamen University, Xiamen, Fujian, 361005, China.
| | - Feng Zheng
- Wuhu Hospital of East China Normal University, Wuhu, Anhui, 241000, China.
- Kidney Health Institute, Health Science Center, East China Normal University, Minhang, Shanghai, 200241, China.
- Department of Nephrology, The Second Hospital, Dalian Medical University, Dalian, 116144, China.
- The Advanced Institute for Molecular Medicine, Dalian Medical University, Dalian, 116144, China.
| | - James Y Yang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiang'an, Xiamen, 361102, China.
- Wuhu Hospital of East China Normal University, Wuhu, Anhui, 241000, China.
- Kidney Health Institute, Health Science Center, East China Normal University, Minhang, Shanghai, 200241, China.
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2
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Custer SK, Gilson T, Astroski JW, Nanguneri SR, Iurillo AM, Androphy EJ. COPI coatomer subunit α-COP interacts with the RNA binding protein Nucleolin via a C-terminal dilysine motif. Hum Mol Genet 2023; 32:3263-3275. [PMID: 37658769 PMCID: PMC10656708 DOI: 10.1093/hmg/ddad140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/07/2023] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
The COPI coatomer subunit α-COP has been shown to co-precipitate mRNA in multiple settings, but it was unclear whether the interaction with mRNA was direct or mediated by interaction with an adapter protein. The COPI complex often interacts with proteins via C-terminal dilysine domains. A search for candidate RNA binding proteins with C-terminal dilysine motifs yielded Nucleolin, which terminates in a KKxKxx sequence. This protein was an especially intriguing candidate as it has been identified as an interacting partner for Survival Motor Neuron protein (SMN). Loss of SMN causes the neurodegenerative disease Spinal Muscular Atrophy. We have previously shown that SMN and α-COP interact and co-migrate in axons, and that overexpression of α-COP reduced phenotypic severity in cell culture and animal models of SMA. We show here that in an mRNA independent manner, endogenous Nucleolin co-precipitates endogenous α-COP and ε-COP but not β-COP which may reflect an interaction with the so-called B-subcomplex rather a complete COPI heptamer. The ability of Nucleolin to bind to α-COP requires the presence of the C-terminal KKxKxx domain of Nucleolin. Furthermore, we have generated a point mutant in the WD40 domain of α-COP which eliminates its ability to co-precipitate Nucleolin but does not interfere with precipitation of partners mediated by non-KKxKxx motifs such as the kainate receptor subunit 2. We propose that via interaction between the C-terminal dilysine motif of Nucleolin and the WD40 domain of α-COP, Nucleolin acts an adaptor to allow α-COP to interact with a population of mRNA.
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Affiliation(s)
- Sara K Custer
- Dermatology, Indiana University School of Medicine, 545 Barnhill Drive, Emerson Hall 139, Indianapolis, IN 46202, United States
| | - Timra Gilson
- Dermatology, Indiana University School of Medicine, 545 Barnhill Drive, Emerson Hall 139, Indianapolis, IN 46202, United States
| | - Jacob W Astroski
- Dermatology, Indiana University School of Medicine, 545 Barnhill Drive, Emerson Hall 139, Indianapolis, IN 46202, United States
| | - Siddarth R Nanguneri
- Dermatology, Indiana University School of Medicine, 545 Barnhill Drive, Emerson Hall 139, Indianapolis, IN 46202, United States
| | - Alyssa M Iurillo
- Indiana University School of Medicine, 340 West 10 St, Indianapolis, IN 46202, United States
| | - Elliot J Androphy
- Dermatology, Indiana University School of Medicine, 545 Barnhill Drive, Emerson Hall 139, Indianapolis, IN 46202, United States
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3
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Inhibition of the DAPKs-L13a axis prevents a GAIT-like motif-mediated HuR insufficiency in melanoma cells. Biochem Biophys Res Commun 2022; 626:21-29. [DOI: 10.1016/j.bbrc.2022.07.086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/20/2022]
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4
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Chowdhury R, Wang Y, Campbell M, Goderie SK, Doyle F, Tenenbaum SA, Kusek G, Kiehl TR, Ansari SA, Boles NC, Temple S. STAU2 binds a complex RNA cargo that changes temporally with production of diverse intermediate progenitor cells during mouse corticogenesis. Development 2021; 148:271165. [PMID: 34345913 DOI: 10.1242/dev.199376] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 07/05/2021] [Indexed: 12/18/2022]
Abstract
STAU2 is a double-stranded RNA-binding protein enriched in the nervous system. During asymmetric divisions in the developing mouse cortex, STAU2 preferentially distributes into the intermediate progenitor cell (IPC), delivering RNA molecules that can impact IPC behavior. Corticogenesis occurs on a precise time schedule, raising the hypothesis that the cargo STAU2 delivers into IPCs changes over time. To test this, we combine RNA-immunoprecipitation with sequencing (RIP-seq) over four stages of mouse cortical development, generating a comprehensive cargo profile for STAU2. A subset of the cargo was 'stable', present at all stages, and involved in chromosome organization, macromolecule localization, translation and DNA repair. Another subset was 'dynamic', changing with cortical stage, and involved in neurogenesis, cell projection organization, neurite outgrowth, and included cortical layer markers. Notably, the dynamic STAU2 cargo included determinants of IPC versus neuronal fates and genes contributing to abnormal corticogenesis. Knockdown of one STAU2 target, Taf13, previously linked to microcephaly and impaired myelination, reduced oligodendrogenesis in vitro. We conclude that STAU2 contributes to the timing of corticogenesis by binding and delivering complex and temporally regulated RNA cargo into IPCs.
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Affiliation(s)
- Rebecca Chowdhury
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Yue Wang
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Melissa Campbell
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Susan K Goderie
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Francis Doyle
- Nanobioscience Constellation, College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY 12203, USA
| | - Scott A Tenenbaum
- Nanobioscience Constellation, College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY 12203, USA
| | - Gretchen Kusek
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Thomas R Kiehl
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Suraiya A Ansari
- Department of Biochemistry and Molecular Biology, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 17666, Al Ain, United Arab Emirates
| | - Nathan C Boles
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
| | - Sally Temple
- Neural Stem Cell Institute, Regenerative Research Foundation, Rensselaer, NY 12144, USA
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5
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Liu J, An Z, Luo J, Li J, Li F, Zhang Z. Episo: quantitative estimation of RNA 5-methylcytosine at isoform level by high-throughput sequencing of RNA treated with bisulfite. Bioinformatics 2020; 36:2033-2039. [PMID: 31794005 PMCID: PMC7141862 DOI: 10.1093/bioinformatics/btz900] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/07/2019] [Accepted: 11/29/2019] [Indexed: 01/17/2023] Open
Abstract
Motivation RNA 5-methylcytosine (m5C) is a type of post-transcriptional modification that may be involved in numerous biological processes and tumorigenesis. RNA m5C can be profiled at single-nucleotide resolution by high-throughput sequencing of RNA treated with bisulfite (RNA-BisSeq). However, the exploration of transcriptome-wide profile and potential function of m5C in splicing remains to be elucidated due to lack of isoform level m5C quantification tool. Results We developed a computational package to quantify Epitranscriptomal RNA m5C at the transcript isoform level (named Episo). Episo consists of three tools: mapper, quant and Bisulfitefq, for mapping, quantifying and simulating RNA-BisSeq data, respectively. The high accuracy of Episo was validated using an improved m5C-specific methylated RNA immunoprecipitation (meRIP) protocol, as well as a set of in silico experiments. By applying Episo to public human and mouse RNA-BisSeq data, we found that the RNA m5C is not evenly distributed among the transcript isoforms, implying the m5C may subject to be regulated at isoform level. Availability and implementation Episo is released under the GNU GPLv3+ license. The resource code Episo is freely accessible from https://github.com/liujunfengtop/Episo (with Tophat/cufflink) and https://github.com/liujunfengtop/Episo/tree/master/Episo_Kallisto (with Kallisto). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Junfeng Liu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Ziyang An
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,School of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianjun Luo
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jing Li
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Feifei Li
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,School of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
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6
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Tomassoni-Ardori F, Fulgenzi G, Becker J, Barrick C, Palko ME, Kuhn S, Koparde V, Cam M, Yanpallewar S, Oberdoerffer S, Tessarollo L. Rbfox1 up-regulation impairs BDNF-dependent hippocampal LTP by dysregulating TrkB isoform expression levels. eLife 2019; 8:49673. [PMID: 31429825 PMCID: PMC6715404 DOI: 10.7554/elife.49673] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 07/25/2019] [Indexed: 12/19/2022] Open
Abstract
Brain-derived neurotrophic factor (BDNF) is a potent modulator of brain synaptic plasticity. Signaling defects caused by dysregulation of its Ntrk2 (TrkB) kinase (TrkB.FL) and truncated receptors (TrkB.T1) have been linked to the pathophysiology of several neurological and neurodegenerative disorders. We found that upregulation of Rbfox1, an RNA binding protein associated with intellectual disability, epilepsy and autism, increases selectively hippocampal TrkB.T1 isoform expression. Physiologically, increased Rbfox1 impairs BDNF-dependent LTP which can be rescued by genetically restoring TrkB.T1 levels. RNA-seq analysis of hippocampi with upregulation of Rbfox1 in conjunction with the specific increase of TrkB.T1 isoform expression also shows that the genes affected by Rbfox1 gain of function are surprisingly different from those influenced by Rbfox1 deletion. These findings not only identify TrkB as a major target of Rbfox1 pathophysiology but also suggest that gain or loss of function of Rbfox1 regulate different genetic landscapes.
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Affiliation(s)
- Francesco Tomassoni-Ardori
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
| | - Gianluca Fulgenzi
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
| | - Jodi Becker
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
| | - Colleen Barrick
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
| | - Mary Ellen Palko
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
| | - Skyler Kuhn
- CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, United States
| | - Vishal Koparde
- CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, United States
| | - Maggie Cam
- CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, United States
| | - Sudhirkumar Yanpallewar
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
| | - Shalini Oberdoerffer
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, United States
| | - Lino Tessarollo
- Neural Development Section, Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, United States
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7
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Xia H, Chen D, Wu Q, Wu G, Zhou Y, Zhang Y, Zhang L. CELF1 preferentially binds to exon-intron boundary and regulates alternative splicing in HeLa cells. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2017; 1860:911-921. [DOI: 10.1016/j.bbagrm.2017.07.004] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 06/30/2017] [Accepted: 07/17/2017] [Indexed: 12/21/2022]
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8
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Doyle F, Lapsia S, Spadaro S, Wurz ZE, Bhaduri-McIntosh S, Tenenbaum SA. Engineering Structurally Interacting RNA (sxRNA). Sci Rep 2017; 7:45393. [PMID: 28350000 PMCID: PMC5368982 DOI: 10.1038/srep45393] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 02/22/2017] [Indexed: 01/01/2023] Open
Abstract
RNA-based three-way junctions (3WJs) are naturally occurring structures found in many functional RNA molecules including rRNA, tRNA, snRNA and ribozymes. 3WJs are typically characterized as resulting from an RNA molecule folding back on itself in cis but could also form in trans when one RNA, for instance a microRNA binds to a second structured RNA, such as a mRNA. Trans-3WJs can influence the final shape of one or both of the RNA molecules and can thus provide a means for modulating the availability of regulatory motifs including potential protein or microRNA binding sites. Regulatory 3WJs generated in trans represent a newly identified regulatory category that we call structurally interacting RNA or sxRNA for convenience. Here we show that they can be rationally designed using familiar cis-3WJ examples as a guide. We demonstrate that an sxRNA "bait" sequence can be designed to interact with a specific microRNA "trigger" sequence, creating a regulatable RNA-binding protein motif that retains its functional activity. Further, we show that when placed downstream of a coding sequence, sxRNA can be used to switch "ON" translation of that sequence in the presence of the trigger microRNA and the amount of translation corresponded with the amount of microRNA present.
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Affiliation(s)
- Francis Doyle
- Nanobioscience Constellation, College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY New York 12203, USA
| | - Sameer Lapsia
- Department of Pediatrics, Stony Brook University School of Medicine, Stony Brook, NY 11794, USA
| | - Salvatore Spadaro
- Department of Pediatrics, Stony Brook University School of Medicine, Stony Brook, NY 11794, USA
| | - Zachary E. Wurz
- HocusLocus, LLC, 253 Fuller Road, Nanofab North, Albany NY 12203, USA
| | - Sumita Bhaduri-McIntosh
- Pediatric Infectious Diseases, Departments of Pediatrics and Molecular Genetics and Microbiology, Stony Brook University School of Medicine, Stony Brook, NY 11794, USA
| | - Scott A. Tenenbaum
- Nanobioscience Constellation, College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY New York 12203, USA
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9
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Shigunov P, Dallagiovanna B. Stem Cell Ribonomics: RNA-Binding Proteins and Gene Networks in Stem Cell Differentiation. Front Mol Biosci 2015; 2:74. [PMID: 26734617 PMCID: PMC4686646 DOI: 10.3389/fmolb.2015.00074] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 12/07/2015] [Indexed: 12/21/2022] Open
Abstract
Stem cells are undifferentiated cells with the ability to self-renew and the potential to differentiate into all body cell types. Stem cells follow a developmental genetic program and are able to respond to alterations in the environment through various signaling pathways. The mechanisms that control these processes involve the activation of transcription followed by a series of post-transcriptional events. These post-transcriptional steps are mediated by the interaction of RNA-binding proteins (RBPs) with defined subpopulations of RNAs creating a regulatory gene network. Characterizing these RNA-protein networks is essential to understanding the regulatory mechanisms underlying the control of stem cell fate. Ribonomics is the combination of classical biochemical purification protocols with the high-throughput identification of transcripts applied to the functional characterization of RNA-protein complexes. Here, we describe the different approaches that can be used in a ribonomic approach and how they have contributed to understanding the function of several RBPs with central roles in stem cell biology.
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Affiliation(s)
- Patrícia Shigunov
- Stem Cells Basic Biology Laboratory, Carlos Chagas Institute, Oswaldo Cruz Foundation Curitiba, Brazil
| | - Bruno Dallagiovanna
- Stem Cells Basic Biology Laboratory, Carlos Chagas Institute, Oswaldo Cruz Foundation Curitiba, Brazil
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10
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Zhang X, Joehanes R, Chen BH, Huan T, Ying S, Munson PJ, Johnson AD, Levy D, O'Donnell CJ. Identification of common genetic variants controlling transcript isoform variation in human whole blood. Nat Genet 2015; 47:345-52. [PMID: 25685889 DOI: 10.1038/ng.3220] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 01/20/2015] [Indexed: 12/17/2022]
Abstract
An understanding of the genetic variation underlying transcript splicing is essential to dissect the molecular mechanisms of common disease. The available evidence from splicing quantitative trait locus (sQTL) studies has been limited to small samples. We performed genome-wide screening to identify SNPs that might control mRNA splicing in whole blood collected from 5,257 Framingham Heart Study participants. We identified 572,333 cis sQTLs involving 2,650 unique genes. Many sQTL-associated genes (40%) undergo alternative splicing. Using the National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) catalog, we determined that 528 unique sQTLs were significantly enriched for 8,845 SNPs associated with traits in previous GWAS. In particular, we found 395 (4.5%) GWAS SNPs with evidence of cis sQTLs but not gene-level cis expression quantitative trait loci (eQTLs), suggesting that sQTL analysis could provide additional insights into the functional mechanism underlying GWAS results. Our findings provide an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms relevant to common diseases.
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Affiliation(s)
- Xiaoling Zhang
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Roby Joehanes
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA. [3] Mathematical and Statistical Computing Laboratory, Center for Information Technology, US National Institutes of Health, Bethesda, Maryland, USA
| | - Brian H Chen
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Tianxiao Huan
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Saixia Ying
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, US National Institutes of Health, Bethesda, Maryland, USA
| | - Peter J Munson
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, US National Institutes of Health, Bethesda, Maryland, USA
| | - Andrew D Johnson
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Daniel Levy
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Christopher J O'Donnell
- 1] Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. [2] National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA. [3] Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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11
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Nie Z, Zhou F, Li D, Lv Z, Chen J, Liu Y, Shu J, Sheng Q, Yu W, Zhang W, Jiang C, Yao Y, Yao J, Jin Y, Zhang Y. RIP-seq of BmAgo2-associated small RNAs reveal various types of small non-coding RNAs in the silkworm, Bombyx mori. BMC Genomics 2013; 14:661. [PMID: 24074203 PMCID: PMC3849828 DOI: 10.1186/1471-2164-14-661] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2013] [Accepted: 09/26/2013] [Indexed: 12/21/2022] Open
Abstract
Background Small non-coding RNAs (ncRNAs) are important regulators of gene expression in eukaryotes. Previously, only microRNAs (miRNAs) and piRNAs have been identified in the silkworm, Bombyx mori. Furthermore, only ncRNAs (50-500nt) of intermediate size have been systematically identified in the silkworm. Results Here, we performed a systematic identification and analysis of small RNAs (18-50nt) associated with the Bombyx mori argonaute2 (BmAgo2) protein. Using RIP-seq, we identified various types of small ncRNAs associated with BmAGO2. These ncRNAs showed a multimodal length distribution, with three peaks at ~20nt, ~27nt and ~33nt, which included tRNA-, transposable element (TE)-, rRNA-, snoRNA- and snRNA-derived small RNAs as well as miRNAs and piRNAs. The tRNA-derived fragments (tRFs) were found at an extremely high abundance and accounted for 69.90% of the BmAgo2-associated small RNAs. Northern blotting confirmed that many tRFs were expressed or up-regulated only in the BmNPV-infected cells, implying that the tRFs play a prominent role by binding to BmAgo2 during BmNPV infection. Additional evidence suggested that there are potential cleavage sites on the D, anti-codon and TψC loops of the tRNAs. TE-derived small RNAs and piRNAs also accounted for a significant proportion of the BmAgo2-associated small RNAs, suggesting that BmAgo2 could be involved in the maintenance of genome stability by suppressing the activities of transposons guided by these small RNAs. Finally, Northern blotting was also used to confirm the Bombyx 5.8 s rRNA-derived small RNAs, demonstrating that various novel small RNAs exist in the silkworm. Conclusions Using an RIP-seq method in combination with Northern blotting, we identified various types of small RNAs associated with the BmAgo2 protein, including tRNA-, TE-, rRNA-, snoRNA- and snRNA-derived small RNAs as well as miRNAs and piRNAs. Our findings provide new clues for future functional studies of the role of small RNAs in insect development and evolution.
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Affiliation(s)
- Zuoming Nie
- College of Life Sciences, Zhejiang Sci-Tech University, Hanghzou 310018, China.
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Lin Y, Li Z, Ozsolak F, Kim SW, Arango-Argoty G, Liu TT, Tenenbaum SA, Bailey T, Monaghan AP, Milos PM, John B. An in-depth map of polyadenylation sites in cancer. Nucleic Acids Res 2012; 40:8460-71. [PMID: 22753024 PMCID: PMC3458571 DOI: 10.1093/nar/gks637] [Citation(s) in RCA: 115] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Revised: 05/16/2012] [Accepted: 06/06/2012] [Indexed: 12/22/2022] Open
Abstract
We present a comprehensive map of over 1 million polyadenylation sites and quantify their usage in major cancers and tumor cell lines using direct RNA sequencing. We built the Expression and Polyadenylation Database to enable the visualization of the polyadenylation maps in various cancers and to facilitate the discovery of novel genes and gene isoforms that are potentially important to tumorigenesis. Analyses of polyadenylation sites indicate that a large fraction (∼30%) of mRNAs contain alternative polyadenylation sites in their 3' untranslated regions, independent of the cell type. The shortest 3' untranslated region isoforms are preferentially upregulated in cancer tissues, genome-wide. Candidate targets of alternative polyadenylation-mediated upregulation of short isoforms include POLR2K, and signaling cascades of cell-cell and cell-extracellular matrix contact, particularly involving regulators of Rho GTPases. Polyadenylation maps also helped to improve 3' untranslated region annotations and identify candidate regulatory marks such as sequence motifs, H3K36Me3 and Pabpc1 that are isoform dependent and occur in a position-specific manner. In summary, these results highlight the need to go beyond monitoring only the cumulative transcript levels for a gene, to separately analysing the expression of its RNA isoforms.
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Affiliation(s)
- Yuefeng Lin
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Zhihua Li
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Fatih Ozsolak
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Sang Woo Kim
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Gustavo Arango-Argoty
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Teresa T. Liu
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Scott A. Tenenbaum
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Timothy Bailey
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - A. Paula Monaghan
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Patrice M. Milos
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
| | - Bino John
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA 02139, College of Nanoscale Science and Engineering, University at Albany-Suny, Albany, NY, USA, Institute for Molecular Bioscience, the University of Queensland, Queensland, Australia and Department of Neurobiology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA
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Burdach J, O'Connell MR, Mackay JP, Crossley M. Two-timing zinc finger transcription factors liaising with RNA. Trends Biochem Sci 2012; 37:199-205. [PMID: 22405571 DOI: 10.1016/j.tibs.2012.02.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2011] [Revised: 01/16/2012] [Accepted: 02/02/2012] [Indexed: 02/01/2023]
Abstract
Classical zinc fingers (ZFs) are one of the most common protein domains in higher eukaryotes and have been known for almost 30 years to act as sequence-specific DNA-binding domains. This knowledge has come, however, from the study of a small number of archetypal proteins, and a larger picture is beginning to emerge that ZF functions are far more diverse than originally suspected. Here, we review the evidence that a subset of ZF proteins live double lives, binding to both DNA and RNA targets and frequenting both the cytoplasm and the nucleus. This duality can create an important additional level of gene regulation that serves to connect transcriptional and post-transcriptional control.
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Affiliation(s)
- Jon Burdach
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW 2052, Australia
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