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initial spliceosome assembly, \u003Cjats:styled-content style=\"fixed-case\">SF\u003C\u002Fjats:styled-content>1 binds to intron branch points and interacts with \u003Cjats:styled-content style=\"fixed-case\">U\u003C\u002Fjats:styled-content>2 sn\u003Cjats:styled-content style=\"fixed-case\">RNP\u003C\u002Fjats:styled-content> auxiliary factor 65 (\u003Cjats:styled-content style=\"fixed-case\">U\u003C\u002Fjats:styled-content>2\u003Cjats:styled-content style=\"fixed-case\">AF\u003C\u002Fjats:styled-content>65). 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A mutant allele of \u003Cjats:italic>AtSF1\u003C\u002Fjats:italic> (At5g51300) that contains a \u003Cjats:styled-content style=\"fixed-case\">T–DNA\u003C\u002Fjats:styled-content> insertion conferred pleiotropic developmental defects, including early flowering and abnormal sensitivity to abscisic acid. An \u003Cjats:italic>AtSF1\u003C\u002Fjats:italic> promoter‐driven \u003Cjats:styled-content style=\"fixed-case\">GUS\u003C\u002Fjats:styled-content> reporter assay showed that \u003Cjats:italic>AtSF1\u003C\u002Fjats:italic> promoter activity was temporally and spatially altered, and that full \u003Cjats:italic>AtSF1\u003C\u002Fjats:italic> promoter activity required a significant proportion of the coding region. \u003Cjats:styled-content style=\"fixed-case\">DNA\u003C\u002Fjats:styled-content> chip analyses showed that only a small proportion of the transcriptome was altered by more than twofold in either direction in the \u003Cjats:italic>AtSF1\u003C\u002Fjats:italic> mutant. Expression of the m\u003Cjats:styled-content style=\"fixed-case\">RNA\u003C\u002Fjats:styled-content>s of many heat shock proteins was more than fourfold higher in the mutant strain; these m\u003Cjats:styled-content style=\"fixed-case\">RNA\u003C\u002Fjats:styled-content>s were among those whose expression was increased most in the mutant strain. An \u003Cjats:styled-content style=\"fixed-case\">RT–PCR\u003C\u002Fjats:styled-content> assay revealed an altered alternative splicing pattern for heat shock transcription factor \u003Cjats:styled-content style=\"fixed-case\">H\u003C\u002Fjats:styled-content>sf\u003Cjats:styled-content style=\"fixed-case\">A\u003C\u002Fjats:styled-content>2 (At2g26150) in the mutant; this altered splicing is probably responsible for the increased expression of the target genes induced by \u003Cjats:styled-content style=\"fixed-case\">H\u003C\u002Fjats:styled-content>sf\u003Cjats:styled-content style=\"fixed-case\">A\u003C\u002Fjats:styled-content>2. Altered alternative splicing patterns were also detected for the transcripts of other genes in the mutant strain. 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In addition to their biochemical function, it is known that several \u003Cjats:styled-content style=\"fixed-case\">TAF\u003C\u002Fjats:styled-content>s are involved in the regulation of developmental processes. In this study, we found that \u003Cjats:styled-content style=\"fixed-case\">TAF\u003C\u002Fjats:styled-content>15b affects flowering time, especially through the autonomous pathway (\u003Cjats:styled-content style=\"fixed-case\">AP\u003C\u002Fjats:styled-content>) in Arabidopsis. The mutant \u003Cjats:italic>taf15b\u003C\u002Fjats:italic> shows late flowering compared with the wild type plant during both long and short days, and vernalization accelerates the flowering time of \u003Cjats:italic>taf15b\u003C\u002Fjats:italic>. In addition, \u003Cjats:italic>taf15b\u003C\u002Fjats:italic> shows strong upregulation of \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">FLOWERING LOCUS\u003C\u002Fjats:styled-content> C\u003C\u002Fjats:italic> (\u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">FLC\u003C\u002Fjats:styled-content>\u003C\u002Fjats:italic>), a flowering repressor in Arabidopsis, and the \u003Cjats:italic>flc taf15b\u003C\u002Fjats:italic> double mutant completely offsets the late flowering of \u003Cjats:italic>taf15b\u003C\u002Fjats:italic>, indicating that \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">TAF\u003C\u002Fjats:styled-content>15b\u003C\u002Fjats:italic> is a typical \u003Cjats:styled-content style=\"fixed-case\">AP\u003C\u002Fjats:styled-content> gene. The \u003Cjats:italic>taf15b\u003C\u002Fjats:italic> mutant also shows increased transcript levels of \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">COOLAIR\u003C\u002Fjats:styled-content>\u003C\u002Fjats:italic>, an antisense transcript of \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">FLC\u003C\u002Fjats:styled-content>\u003C\u002Fjats:italic>. Consistently, chromatin immunoprecipitation (Ch\u003Cjats:styled-content style=\"fixed-case\">IP\u003C\u002Fjats:styled-content>) analyses showed that the \u003Cjats:styled-content style=\"fixed-case\">TAF\u003C\u002Fjats:styled-content>15b protein is enriched around both sense and antisense transcription start sites of the \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">FLC\u003C\u002Fjats:styled-content>\u003C\u002Fjats:italic> locus. In addition, co‐immunoprecipitation showed that \u003Cjats:styled-content style=\"fixed-case\">TAF\u003C\u002Fjats:styled-content>15b interacts with \u003Cjats:styled-content style=\"fixed-case\">RNA\u003C\u002Fjats:styled-content> polymerase \u003Cjats:styled-content style=\"fixed-case\">II\u003C\u002Fjats:styled-content> (Pol \u003Cjats:styled-content style=\"fixed-case\">II\u003C\u002Fjats:styled-content>), while Ch\u003Cjats:styled-content style=\"fixed-case\">IP\u003C\u002Fjats:styled-content> showed increased enrichment of the phosphorylated forms, both serine 2 (Ser2) and Ser5, of the C‐terminal domain of Pol \u003Cjats:styled-content style=\"fixed-case\">II\u003C\u002Fjats:styled-content> at the \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">FLC\u003C\u002Fjats:styled-content>\u003C\u002Fjats:italic> locus, which is indicative of transcriptional elongation. Finally, \u003Cjats:italic>taf15b\u003C\u002Fjats:italic> showed higher enrichment of the active histone marker, H3K4me3, on \u003Cjats:italic>\u003Cjats:styled-content style=\"fixed-case\">FLC\u003C\u002Fjats:styled-content>\u003C\u002Fjats:italic> chromatin. 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(2018) SUMOylation: re‐wiring the plant nucleus during stress and development. Curr. Opin. Plant Biol. 45, 143–154.\nBarber, K.W., Muir, P., Szeligowski, R.V., Rogulina, S., Gerstein, M., Sampson, J.R., Isaacs, F.J. and Rinehart, J. (2018) Encoding human serine phosphopeptides in bacteria for proteome‐wide identification of phosphorylation‐dependent interactions. Nat. Biotechnol. 36, 638–644.\nBateman, A., Smart, A., Luciani, A. et al. (2018) The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432.\nBeltrao, P., Bork, P., Krogan, N.J. and van Noort, V. (2013) Evolution and functional cross‐talk of protein post‐translational modifications. Mol. Syst. Biol. 9, 714.\nBenschop, J.J., Mohammed, S., O'Flaherty, M., Heck, A.J.R., Slijper, M. and Menke, F.L.H. (2007) Quantitative phosphoproteomics of early elicitor signaling in Arabidopsis. Mol. Cell Proteomics, 6, 1198–1214.\nBerardini, T.Z., Reiser, L., Li, D., Mezheritsky, Y., Muller, R., Strait, E. and Huala, E. (2015) The Arabidopsis information resource: making and mining the “gold standard” annotated reference plant genome. Genesis, 53, 474–485.\nBhaskara, G.B., Wen, T.‐N., Nguyen, T.T. and Verslues, P.E. (2017) Protein phosphatase 2Cs and microtubule‐associated stress protein 1 control microtubule stability, plant growth, and drought response. Plant Cell, 29, 169–191.\nBreitkreutz, B.‐J., Stark, C., Dolinski, K. et al. (2013) The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database, 2013, bat026.\nChen, Y. and Hoehenwarter, W. (2015) Changes in the phosphoproteome and metabolome link early signaling events to rearrangement of photosynthesis and central metabolism in salinity and oxidative stress response in Arabidopsis. Plant Physiol. 169, 3021–3033.\nCho, H.‐Y., Shih, M.‐C., Wang, Y.‐T. and Wen, T.‐N. (2016) Quantitative phosphoproteomics of protein kinase SnRK1 regulated protein phosphorylation in Arabidopsis under submergence. J. Exp. Bot. 67, 2745–2760.\nClark, T.J. and Lu, Y. (2015) Analysis of loss‐of‐function mutants in aspartate kinase and homoserine dehydrogenase genes points to complexity in the regulation of aspartate‐derived amino acid contents. Plant Physiol. 168, 1512–1526.\nCoon, J.J., Ueberheide, B., Syka, J.E.P., Dryhurst, D.D., Ausio, J., Shabanowitz, J. and Hunt, D.F. (2005) Protein identification using sequential ion\u002Fion reactions and tandem mass spectrometry. Proc. Natl Acad. Sci. USA, 93, 9463–9468.\nGaravelli, J.S. (2004) The RESID database of protein modifications as a resource and annotation tool. Proteomics, 4, 1527–1533.\nGaudelli, N.M., Komor, A.C., Rees, H.A., Packer, M.S., Badran, A.H., Bryson, D.I. and Liu, D.R. (2017) Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature, 551, 464–471.\nGnad, F., Gunawardena, J. and Mann, M. (2010) PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res. 39, D253–D260.\nGuo, J., Wang, J., Lee, J.S. and Schultz, P.G. (2008) Site‐specific incorporation of methyl‐ and acetyl‐lysine analogues into recombinant proteins. Angew. Chem. Int. Ed. Engl. 47, 6399–6401.\nHemsley, P.A., Weimar, T., Lilley, K.S., Dupree, P. and Grierson, C.S. (2013) A proteomic approach identifies many novel palmitoylated proteins in Arabidopsis. New Phytol. 197, 805–814.\nHu, J., Huang, X., Chen, L., Sun, X., Lu, C., Zhang, L., Wang, Y. and Zuo, J. (2015) Site‐specific nitrosoproteomic identification of endogenously S‐nitrosylated proteins in Arabidopsis. Plant Physiol. 167, 1731–1746.\nHuang, X., Huang, L., Peng, H. et al. (2013) ISPTM: an iterative search algorithm for systematic identification of post‐translational modifications from complex proteome mixtures. J. Proteome Res. 12, 3831–3842.\nHuang, H., Arighi, C.N., Ross, K.E., Ren, J., Li, G., Chen, S.‐C., Wang, Q., Cowart, J., Vijay‐Shanker, K. and Wu, C.H. (2017) iPTMnet: an integrated resource for protein post‐translational modification network discovery. Nucleic Acids Res. 46, D542–D550.\nJohnson, A. and Vert, G. (2016) Unraveling K63 polyubiquitination networks by sensor‐based proteomics. Plant Physiol. 171, 1808–1820.\nKao, H.‐J., Jhong, J.‐H., Huang, K.‐Y., Cheng, K.‐H., Su, M.‐G., Hsieh, Y.‐C., Lee, T.‐Y. and Huang, H.‐D. (2015) dbPTM 2016: 10‐year anniversary of a resource for post‐translational modification of proteins. Nucleic Acids Res. 44, D435–D446.\nKim, W., Bennett, E.J., Huttlin, E.L. et al. (2011) Systematic and quantitative assessment of the ubiquitin‐modified proteome. Mol. Cell 44, 325–340.\nKim, D.‐Y., Scalf, M., Smith, L.M. and Vierstra, R.D. (2013) Advanced proteomic analyses yield a deep catalog of ubiquitylation targets in Arabidopsis. Plant Cell, 25, 1523–1540.\nKomor, A.C., Kim, Y.B., Packer, M.S., Zuris, J.A. and Liu, D.R. (2016) Programmable editing of a target base in genomic DNA without double‐stranded DNA cleavage. Nature, 533, 420–424.\nKoskela, M.M., Brünje, A., Ivanauskaite, A. et al. (2018) Chloroplast acetyltransferase NSI is required for state transitions in Arabidopsis thaliana. Plant Cell, 30, 1695–1709.\nKühn, C. (2016) Review: post‐translational cross‐talk between brassinosteroid and sucrose signaling. Plant Sci. 248, 75–81.\nvan der Laarse, S.A.M., Leney, A.C. and Heck, A.J.R. (2018) Crosstalk between phosphorylation and O‐GlcNAcylation: friend or foe. FEBS J. 285, 3152–3167.\nLarsen, M.R., Thingholm, T.E., Jensen, O.N., Roepstorff, P. and Jørgensen, T.J.D. (2005) Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol. Cell Proteomics, 4, 873–886.\nLiu, S., Yu, F., Yang, Z., Wang, T., Xiong, H., Chang, C., Yu, W. and Li, N. (2018) Establishment of dimethyl labeling‐based quantitative acetylproteomics in Arabidopsis. Mol. Cell Proteomics, 17, 1010–1027.\nMajeran, W., Le Caer, J.‐P., Ponnala, L., Meinnel, T. and Giglione, C. (2018) Targeted profiling of Arabidopsis thaliana subproteomes illuminates co‐ and posttranslationally N‐terminal myristoylated proteins. Plant Cell, 30, 543–562.\nMiller, M.J., Barrett‐Wilt, G.A., Hua, Z. and Vierstra, R.D. (2010) Proteomic analyses identify a diverse array of nuclear processes affected by small ubiquitin‐like modifier conjugation in Arabidopsis. Proc. Natl Acad. Sci. USA, 107, 16512–16517.\nMiller, M.J., Scalf, M., Rytz, T.C., Hubler, S.L., Smith, L.M. and Vierstra, R.D. (2013) Quantitative proteomics reveals factors regulating RNA biology as dynamic targets of stress‐induced SUMOylation in Arabidopsis. Mol. Cell Proteomics, 12, 449–463.\nMinkoff, B.B., Stecker, K.E. and Sussman, M.R. (2015) Rapid phosphoproteomic effects of abscisic acid (ABA) on wild‐type and ABA receptor‐deficient A. thaliana mutants. Mol. Cell Proteomics, 14, 1169–1182.\nMithoe, S.C., Boersema, P.J., Berke, L., Snel, B., Heck, A.J.R. and Menke, F.L.H. (2012) Targeted Quantitative Phosphoproteomics Approach for the Detection of Phospho‐tyrosine Signaling in Plants. J. Proteome Res. 11, 438–448.\nMueller, L.A., Zhang, P. and Rhee, S.Y. (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol. 132, 453–460.\nNühse, T.S., Bottrill, A.R., Jones, A.M.E. and Peck, S.C. (2007) Quantitative phosphoproteomic analysis of plasma membrane proteins reveals regulatory mechanisms of plant innate immune responses. Plant J. 51, 931–940.\nPirman, N.L., Barber, K.W., Aerni, H.R., Ma, N.J., Haimovich, A.D., Rogulina, S., Isaacs, F.J. and Rinehart, J. (2015) A flexible codon in genomically recoded Escherichia coli permits programmable protein phosphorylation. Nat. Commun. 6, 8130.\nRoitinger, E., Hofer, M., Köcher, T., Pichler, P., Novatchkova, M., Yang, J., Schlögelhofer, P. and Mechtler, K. (2015) Quantitative phosphoproteomics of the ataxia telangiectasia‐mutated (ATM) and ataxia telangiectasia‐mutated and Rad3‐related (ATR) dependent DNA damage response in Arabidopsis thaliana. Mol. Cell Proteomics, 14, 556–571.\nRosen, B.D., Cheng, C.‐Y., Town, C.D. et al. (2014) Araport: the Arabidopsis information portal. Nucleic Acids Res. 43, D1003–D1009.\nRoy, K.R., Smith, J.D., Vonesch, S.C. et al. (2018) Multiplexed precision genome editing with trackable genomic barcodes in yeast. Nat. Biotechnol. 36, 512–520.\nRytz, T.C., Miller, M.J., McLoughlin, F., Augustine, R.C., Marshall, R.S., Juan, Y., Charng, Y., Scalf, M., Smith, L.M. and Vierstra, R.D. (2018) SUMOylome profiling reveals a diverse array of nuclear targets modified by the SUMO ligase SIZ1 during heat stress. Plant Cell, 30, 1077–1099.\nSchläpfer, P., Zhang, P., Wang, C. et al. (2017) Genome‐wide prediction of metabolic enzymes, pathways, and gene clusters in plants. Plant Physiol. 173, 2041–2059.\nStrasser, R. (2016) Plant protein glycosylation. Glycobiology, 26, 926–939.\nSvozil, J., Hirsch‐Hoffmann, M., Dudler, R., Gruissem, W. and Baerenfaller, K. (2014) Protein abundance changes and ubiquitylation targets identified after inhibition of the proteasome with syringolin A. Mol. Cell Proteomics, 13, 1523–1536.\nTomita, T., Hirayama, S., Sakurai, Y., Ohte, Y., Yoshihara, H., Saeki, Y., Hamazaki, J. and Murata, S. (2019) Specific modification of aged proteasomes revealed by tag‐exchangeable knock‐in mice. Mol. Cell. Biol. 39, e00426–18.\nTsur, D., Tanner, S., Zandi, E., Bafna, V. and Pevzner, P.A. (2005) Identification of post‐translational modifications by blind search of mass spectra. Nat. Biotechnol. 23, 1562–1567.\nTurnbull, D. and Hemsley, P.A. (2017) Fats and function: protein lipid modifications in plant cell signalling. Curr. Opin. Plant Biol. 40, 63–70.\nVia, A., Gould, C.M., Chica, C., Dinkel, H., Jensen, L.J., Diella, F. and Gibson, T.J. (2010) Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res. 39, D261–D267.\nWalton, A., Stes, E., Cybulski, N. et al. (2016) It's time for some “site”‐seeing: novel tools to monitor the ubiquitin landscape in Arabidopsis thaliana. Plant Cell, 28, 6–16.\nWang, K., Yang, Z., Qing, D. et al. (2018) Quantitative and functional posttranslational modification proteomics reveals that TREPH1 plays a role in plant touch‐delayed bolting. Proc. Natl Acad. Sci. USA, 115, E10265–E10274.\nWithers, J. and Dong, X. (2017) Post‐translational regulation of plant immunity. Curr. Opin. Plant Biol. 38, 124–132.\nXu, G., Paige, J.S. and Jaffrey, S.R. (2010) Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling. Nat. Biotechnol. 28, 868–873.\nXu, S.‐L., Medzihradszky, K.F., Wang, Z.‐Y., Burlingame, A.L. and Chalkley, R.J. (2016) N‐glycopeptide profiling in arabidopsis inflorescence. Mol. Cell Proteomics, 15, 2048–2054.\nXu, S.‐L., Chalkley, R.J., Maynard, J.C. et al. (2017) Proteomic analysis reveals O‐GlcNAc modification on proteins with key regulatory functions in Arabidopsis. Proc. Natl Acad. Sci. USA, 114, E1536–E1543.\nYao, Q., Ge, H., Wu, S., Zhang, N., Chen, W., Xu, C., Gao, J., Thelen, J.J. and Xu, D. (2013) P3DB 3.0: from plant phosphorylation sites to protein networks. Nucleic Acids Res. 42, D1206–D1213.\nZeng, W., Ford, K.L., Bacic, A. and Heazlewood, J.L. (2018) N‐linked glycan micro‐heterogeneity in glycoproteins of arabidopsis. Mol. Cell Proteomics, 17, 413–421.\nZhang, H., Zhou, H., Berke, L., Heck, A.J.R., Mohammed, S., Scheres, B. and Menke, F.L.H. (2013) Quantitative phosphoproteomics after auxin‐stimulated lateral root induction identifies an SNX1 protein phosphorylation site required for growth. Mol. Cell Proteomics, 12, 1158–1169.\nZhang, C., Guo, X., Xie, H. et al. (2018) Quantitative phosphoproteomics of lectin receptor‐like kinase VI.4 dependent abscisic acid response in Arabidopsis thaliana. Physiol. Plant. 165, 728–745.\nZulawski, M., Braginets, R. and Schulze, W.X. (2012) PhosPhAt goes kinases—searchable protein kinase target information in the plant phosphorylation site database PhosPhAt. Nucleic Acids Res. 41, D1176–D1184.",{"EN":3384},"Summary Post‐translational modifications (PTMs) are critical regulators of protein function, and nearly 200 different types of PTM have been identified. Advances in high‐resolution mass spectrometry have led to the identification of an unprecedented number of PTM sites in numerous organisms, potentially facilitating a more complete understanding of how PTMs regulate cellular behavior. While databases have been created to house the resulting data, most of these resources focus on individual types of PTM, do not consider quantitative PTM analyses or do not provide tools for the visualization and analysis of PTM data. Here, we describe the Functional Analysis Tools for Post‐Translational Modifications (FAT‐PTM) database (https:\u002F\u002Fbioinformatics.cse.unr.edu\u002Ffat-ptm\u002F), which currently supports eight different types of PTM and over 49 000 PTM sites identified in large‐scale proteomic surveys of the model organism Arabidopsis thaliana. The FAT‐PTM database currently supports tools to visualize protein‐centric PTM networks, quantitative phosphorylation site data from over 10 different quantitative phosphoproteomic studies, PTM information displayed in protein‐centric metabolic pathways and groups of proteins that are co‐modified by multiple PTMs. Overall, the FAT‐PTM database provides users with a robust platform to share and visualize experimentally supported PTM data, develop hypotheses related to target proteins or identify emergent patterns in PTM data for signaling and metabolic pathways.",{"EN":3386},"Functional analysis tools for post‐translational modification: a post‐translational modification database for analysis of proteins and metabolic pathways",{"VOID":3376},"2025-01-17T12:14:20.701+00:00","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1111\u002Ftpj.14372",[3391,3407,3422,3437],{"id":3392,"sortIndex":25,"researcher":24,"roles":3393,"affiliations":3395,"properties":3404,"displayName":3406,"givenName":24,"familyName":24},"5152b769-9f93-403b-bd4a-c3a965febfaf",[3394],"AUTHOR",[3396],{"id":3397,"sortIndex":25,"affiliation":3398,"properties":24},"8f014f64-d0ed-4cd9-9b10-8449e8c4ede0",{"id":3397,"createTime":24,"updateTime":24,"relativeEntities":3399,"slug":24,"properties":3400,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3403,"statistic":24},[],{"title":3401},{"VI":3402},"Department of Chemistry University of Nevada, Reno Reno NV 89557 USA",[],{"title":3405},{"VI":3406},"Edward R. Cruz",{"id":3408,"sortIndex":109,"researcher":24,"roles":3409,"affiliations":3410,"properties":3417,"displayName":3419,"givenName":24,"familyName":24},"73abe5fd-2fed-402a-be2b-ca59b01639e5",[3394],[3411],{"id":3397,"sortIndex":25,"affiliation":3412,"properties":24},{"id":3397,"createTime":24,"updateTime":24,"relativeEntities":3413,"slug":24,"properties":3414,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3416,"statistic":24},[],{"title":3415},{"VI":3402},[],{"title":3418,"gsAuthor":3420},{"VI":3419},"Hung Nguyen",{"VOID":3421},"mURzVOAAAAAJ",{"id":3423,"sortIndex":111,"researcher":24,"roles":3424,"affiliations":3425,"properties":3432,"displayName":3434,"givenName":24,"familyName":24},"bd010a5f-bdd7-4db8-bb98-4b24711df284",[3394],[3426],{"id":3397,"sortIndex":25,"affiliation":3427,"properties":24},{"id":3397,"createTime":24,"updateTime":24,"relativeEntities":3428,"slug":24,"properties":3429,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3431,"statistic":24},[],{"title":3430},{"VI":3402},[],{"title":3433,"gsAuthor":3435},{"VI":3434},"Tin Nguyen",{"VOID":3436},"aUjOGMEAAAAJ",{"id":3438,"sortIndex":110,"researcher":24,"roles":3439,"affiliations":3440,"properties":3447,"displayName":3449,"givenName":24,"familyName":24},"6d61bb29-1574-46b2-a5a4-9c32a39df3dd",[3394],[3441],{"id":3397,"sortIndex":25,"affiliation":3442,"properties":24},{"id":3397,"createTime":24,"updateTime":24,"relativeEntities":3443,"slug":24,"properties":3444,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3446,"statistic":24},[],{"title":3445},{"VI":3402},[],{"title":3448,"gsAuthor":3450},{"VI":3449},"Ian S. Wallace",{"VOID":3451},"H9q29coAAAAJ",{"url":3389,"publisher":3453,"properties":3504},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":3454,"slug":10,"properties":3455,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":3460,"manageAffiliations":3473,"indexDatabases":3484,"url":97,"thumbnailPath":24,"statistic":3499,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":3456,"eissn":3457,"issn":3458,"title":3459},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[3461,3465,3469],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":3462,"label":3463,"description":3464,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},{"id":34,"createTime":24,"updateTime":24,"relativeEntities":3466,"label":3467,"description":3468,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":37},{},{"id":40,"createTime":24,"updateTime":24,"relativeEntities":3470,"label":3471,"description":3472,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":43},{},[3474,3479],{"id":47,"createTime":24,"updateTime":24,"relativeEntities":3475,"slug":24,"properties":3476,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3478,"statistic":24},[],{"title":3477},{"EN":51},[],{"id":54,"createTime":24,"updateTime":24,"relativeEntities":3480,"slug":24,"properties":3481,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3483,"statistic":24},[],{"title":3482},{"EN":58},[],[3485,3492],{"id":62,"indexDatabase":3486,"url":73,"indexYears":74,"academicFieldIds":3491,"indexDatabaseRanking":79},{"id":64,"createTime":24,"updateTime":24,"relativeEntities":3487,"label":3488,"description":3489,"key":70,"publicationTags":3490,"standard":24},[],{"EN":67,"VI":67},{"EN":67,"VI":69},[72],[76,77,78],{"id":81,"indexDatabase":3493,"url":94,"indexYears":24,"academicFieldIds":3498,"indexDatabaseRanking":24},{"id":83,"createTime":24,"updateTime":24,"relativeEntities":3494,"label":3495,"description":3496,"key":90,"publicationTags":3497,"standard":24},[],{"EN":86,"VI":86},{"EN":88,"VI":89},[92,93],[96],{"impactFactor":25,"impactFactorByYear":3500,"i10Index":106,"i10IndexLast5Year":107,"totalPublication":106,"totalPublicationByYear":3501,"totalCitation":112,"totalCitationByYear":3502,"totalCitationPerPublication":120,"totalCitationPerPublicationByYear":3503,"hindexLast5Year":106,"hindex":106},{"2015":100,"2016":101,"2020":102,"2021":103,"2022":104,"2023":105},{"2002":109,"2008":109,"2009":109,"2014":102,"2019":110,"2021":111},{"2002":114,"2008":115,"2009":116,"2014":117,"2019":118,"2021":119},{"2002":114,"2008":115,"2009":116,"2014":122,"2019":123,"2021":124},{"issue":3505,"pages":3507,"volume":3509},{"VOID":3506},"5",{"VOID":3508},"1003-1013",{"VOID":3510},"99",{"total":25,"publishYear":3512,"statisticByYear":3513},2019,{},"2019-09-01","2026-04-22T11:31:28.050+00:00",[92,79],{"id":3518,"createTime":3519,"updateTime":3520,"relativeEntities":3521,"slug":3522,"properties":3523,"entityType":150,"verifyStatus":151,"verifyTime":3519,"verifyNote":152,"languages":3538,"translateLanguages":24,"viewCount":25,"primaryUrl":3539,"fullTextUrl":24,"authors":3540,"publicationType":280,"publisherRelationship":3693,"citationCount":25,"citationInfo":3751,"publishDate":3754,"publishYear":3752,"citationAnalyzeStatus":23,"lastCitationAnalyze":3520,"indexDatabases":3755,"openAccess":24,"references":3756,"isForceReanalyzing":497},"676036d5-549a-4d81-bf7a-65ca8fdda1cb","2024-12-25T22:20:12.098+00:00","2026-04-12T16:08:49.465+00:00",[],"Functional-characterization-of-a-glycine-rich-RNA-binding-protein-2-in-i-Arabidopsis-thaliana-i-under-abiotic-stress-conditions",{"mag":3524,"gsPaper":3526,"openalex":3528,"abstract":3530,"title":3532,"pm":3534,"doi":3536},{"VOID":3525},"2141706638",{"VOID":3527},"[\"10963236511299029086\"]",{"VOID":3529},"W2141706638",{"EN":3531},"\u003Cjats:title>Summary\u003C\u002Fjats:title>\u003Cjats:p>Although glycine‐rich RNA‐binding protein 2 (GRP2) has been implicated in plant responses to environmental stresses, the function and importance of GRP2 in stress responses are largely unknown. Here, we examined the functional roles of GRP2 in \u003Cjats:italic>Arabidopsis thaliana\u003C\u002Fjats:italic> under high‐salinity, cold or osmotic stress. GRP2 affects seed germination of Arabidopsis plants under salt stress, but does not influence seed germination and seedling growth of Arabidopsis plants under osmotic stress. GRP2 accelerates seed germination and seedling growth in Arabidopsis plants under cold stress, and contributes to enhancement of cold and freezing tolerance in Arabidopsis plants. No differences in germination between the wild‐type and transgenic plants were observed following addition of abscisic acid (ABA) or glucose, implying that GRP2 affects germination through an ABA‐independent pathway. GRP2 complements the cold sensitivity of an \u003Cjats:italic>Escherichia coli\u003C\u002Fjats:italic> BX04 mutant and exhibits transcription anti‐termination activity, suggesting that it has an RNA chaperone activity during the cold adaptation process. Mitochondrial respiration and catalase and peroxidase activities were affected by expression of mitochondrial‐localized GRP2 in Arabidopsis plants under cold stress. Proteome analysis revealed that expression of several mitochondrial‐encoded genes was modulated by GRP2 under cold stress. These results provide new evidence indicating that GRP2 plays important roles in seed germination, seedling growth and freezing tolerance of Arabidopsis under stress conditions, and that GRP2 exerts its function by modulating the expression and activity of various classes of genes.\u003C\u002Fjats:p>",{"EN":3533},"Functional characterization of a glycine‐rich RNA‐binding protein 2 in \u003Ci>Arabidopsis thaliana\u003C\u002Fi> under abiotic stress conditions",{"VOID":3535},"17376161",{"VOID":3537},"10.1111\u002Fj.1365-313x.2007.03057.x",[154],"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1111\u002Fj.1365-313X.2007.03057.x",[3541,3562,3579,3596,3613,3628,3643,3662,3679],{"id":3542,"sortIndex":25,"researcher":24,"roles":3543,"affiliations":3544,"properties":3553,"displayName":3557,"givenName":24,"familyName":24},"cbf3ef3b-d74e-4c03-9120-dbe955491cb7",[],[3545],{"id":3546,"sortIndex":25,"affiliation":3547,"properties":24},"2d2a8b37-1c86-4d83-88ca-255e77bcff46",{"id":3546,"createTime":24,"updateTime":24,"relativeEntities":3548,"slug":24,"properties":3549,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3552,"statistic":24},[],{"title":3550},{"EN":3551},"Department of Plant Biotechnology, Agricultural Plant Stress Research Center and Biotechnology Research Institute, College of Agriculture and Life Sciences, Chonam National University, Gwangju 500-757, Korea.",[],{"orcid":3554,"title":3556,"gsAuthor":3558,"openalex":3560},{"VOID":3555},"https:\u002F\u002Forcid.org\u002F0000-0001-8828-7488",{"EN":3557},"Joo Yong Kim",{"VOID":3559},"U9QR01MAAAAJ",{"VOID":3561},"A5101834892",{"id":3563,"sortIndex":109,"researcher":24,"roles":3564,"affiliations":3565,"properties":3574,"displayName":3576,"givenName":24,"familyName":24},"762cedbf-aa88-4e73-a697-5323bbf5d919",[],[3566],{"id":3567,"sortIndex":25,"affiliation":3568,"properties":24},"56e34a76-4984-4883-985e-31ad8496efaf",{"id":3567,"createTime":24,"updateTime":24,"relativeEntities":3569,"slug":24,"properties":3570,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":3573,"statistic":24},[],{"title":3571},{"EN":3572},"Department of Plant Biotechnology, Agricultural Plant Stress Research Center and Biotechnology Research Institute, College of Agriculture and Life Sciences,",[],{"title":3575,"openalex":3577},{"EN":3576},"Su J. 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