Seasonal and water restriction-related changes in Eucalyptus grandis leaf proteins: Shedding light on the dark proteome

Current Plant Biology - Tập 34 - Trang 100286 - 2023
Gabriel L. Jorge1,2, Rinaldo C. de Paula1, Brian Mooney2,3, Jay J. Thelen2,3, Tiago S. Balbuena1
1School of Agriculture and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
2Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
3Department of Biochemistry, University of Missouri, Columbia, MO, United States

Tài liệu tham khảo

Freeman, 2013, Stability of quantitative trait loci for growth and wood properties across multiple pedigrees and environments in Eucalyptus globulus, N. Phytol., 198, 1121, 10.1111/nph.12237 IBÁ - Indústria Brasileira de Árvores, Annual Report, Brasília, 2020. 〈https://iba.org/datafiles/publicacoes/relatorios/relatorio-iba-2020.pdf〉 (accessed November 7, 2021). Li, 2020, Physiological and differential proteomic analyses of imitation drought stress response in Sorghum bicolor root at the seedling stage, Int J. Mol. Sci., 21, 1, 10.3390/ijms21239174 Easterling, 2000, Climate extremes: observations, Model., Impacts Shao, 2009, Understanding water deficit stress-induced changes in the basic metabolism of higher plants-biotechnologically and sustainably improving agriculture and the ecoenvironment in arid regions of the globe, Crit. Rev. Biotechnol., 29, 131, 10.1080/07388550902869792 Krasensky, 2012, Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks, J. Exp. Bot., 63, 1593, 10.1093/jxb/err460 Feist, 2015, Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples, Int J. Mol. Sci., 16, 3537, 10.3390/ijms16023537 Ghirardo, 2021, Protein expression plasticity contributes to heat and drought tolerance of date palm, Oecologia, 197, 903, 10.1007/s00442-021-04907-w Zadražnik, 2013, Differential proteomic analysis of drought stress response in leaves of common bean (Phaseolus vulgaris L.), J. Proteom., 78, 254, 10.1016/j.jprot.2012.09.021 Zadražnik, 2019, Chloroplast proteins involved in drought stress response in selected cultivars of common bean (Phaseolus vulgaris L.), 3 Biotech, 9, 10.1007/s13205-019-1862-x J.K. Eng, A.L. Mccormack, J.R. Yates, An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database, 1994. Jaffe, 2004, Proteogenomic mapping as a complementary method to perform genome annotation, Proteomics, 4, 59, 10.1002/pmic.200300511 Nesvizhskii, 2014, Proteogenomics: concepts, applications and computational strategies, Nat. Methods, 11, 1114, 10.1038/nmeth.3144 Li, 2011, Work. Var. Pept. Detect. Shotgun Proteom. * □ S Technol. Innov. Resour. Kim, 2021, A temperature-sensitive FERONIA mutant allele that alters root hair growth, Plant Physiol., 185, 405, 10.1093/plphys/kiaa051 Kucukkal, 2015, Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins, CurrOpin Struct. Biol., 32, 18, 10.1016/j.sbi.2015.01.003 Shi, 2016, Advances in targeted proteomics and applications to biomedical research, Proteomics, 16, 2160, 10.1002/pmic.201500449 Martínez-Márquez, 2013, Development and validation of MRM methods to quantify protein isoforms of polyphenol oxidase in loquat fruits, J. Proteome Res, 12, 5709, 10.1021/pr4006712 Percy, 2013, Multiplexed MRM-based quantitation of candidate cancer biomarker proteins in undepleted and non-enriched human plasma, Proteomics, 13, 2202, 10.1002/pmic.201200316 Stevenson, 2012, Environmental effects on allergen levels in commercially grown non-genetically modified soybeans: assessing variation across North America, Front Plant Sci., 3, 10.3389/fpls.2012.00196 Houston, 2011, Quantitation of soybean allergens using tandem mass spectrometry, J. Proteome Res, 10, 763, 10.1021/pr100913w Wilson, 2018, In vivo quantitative monitoring of subunit stoichiometry for metabolic complexes, J. Proteome Res, 17, 1773, 10.1021/acs.jproteome.7b00756 Binkley, 2017, The interactions of climate, spacing and genetics on clonal Eucalyptus plantations across Brazil and Uruguay, Ecol. Manag., 405, 271, 10.1016/j.foreco.2017.09.050 Alvares, 2013, Köppen’s climate classification map for Brazil, MeteorologischeZeitschrift, 22, 711, 10.1127/0941-2948/2013/0507 Shevchenko, 2006, In-gel digestion for mass spectrometric characterization of proteins and proteomes, Nat. Protoc., 1, 2856, 10.1038/nprot.2006.468 Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol., 26, 1367, 10.1038/nbt.1511 Tyanova, 2016, The MaxQuant computational platform for mass spectrometry-based shotgun proteomics, Nat. Protoc., 11, 2301, 10.1038/nprot.2016.136 Cox, 2011, Andromeda: a peptide search engine integrated into the MaxQuant environment, J. Proteome Res, 10, 1794, 10.1021/pr101065j Goodstein, 2012, Phytozome: a comparative platform for green plant genomics, Nucleic Acids Res, 40, 10.1093/nar/gkr944 Jorge, 2021, Identification of novel protein-coding sequences in Eucalyptus grandis plants by high-resolution mass spectrometry, Biochim. Acta Proteins Proteom., 1869 Cox, 2014, Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ* □ S technological innovation and resources, Mol. Cell. Proteom., 13, 2513, 10.1074/mcp.M113.031591 Perez-Riverol, 2022, The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences, Nucleic Acids Res, 50, D543, 10.1093/nar/gkab1038 Zhang, 2012, PEAKS DB: De novo sequencing assisted database search for sensitive and accurate peptide identification, Mol. Cell. Proteom., 11, 10.1074/mcp.M111.010587 Y. Han, B. Ma, K. Zhang, SPIDER: Software for Protein Identification from Sequence Tags with De Novo Sequencing Error, 2004. Tyanova, 2016, The Perseus computational platform for comprehensive analysis of (prote)omics data, Nat. Methods, 13, 731, 10.1038/nmeth.3901 M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K. Dolinski, S.S. Dwight, J.T. Eppig, M.A. Harris, D.P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J.C. Matese, J.E. Richardson, M. Ringwald, G.M. Rubin, G. Sherlock, Gene Ontology: tool for the unification of biology The Gene Ontology Consortium*, 2000. 〈http://www.flybase.bio.indiana.edu〉. M. Kanehisa, S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, 2000. 〈http://www.genome.ad.jp/kegg/〉. Cantalapiedra, 2021, eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale, Mol. Biol. Evol., 38, 5825, 10.1093/molbev/msab293 R.L. Tatusov, N.D. Fedorova, J.D. Jackson, A.R. Jacobs, B. Kiryutin, E.V. Koonin, D.M. Krylov, R. Mazumder, S.L. Mekhedov, A.N. Nikolskaya, B.S. Rao, S. Smirnov, A.V. Sverdlov, S. Vasudevan, Y.I. Wolf, J.J. Yin, D.A. Natale, The COG database: an updated version includes eukaryotes, 2003. 〈http://www.biomedcentral.com/1471–2105/4/41〉. Steinegger, 2017, MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets, Nat. Biotechnol., 35, 1026, 10.1038/nbt.3988 Berman, 2000, Protein Data Bank Chen, 2020, PremPS: Predicting the impact of missense mutations on protein stability, Plos. Biol., 16 Pires, 2014, MCSM: predicting the effects of mutations in proteins using graph-based signatures, Bioinformatics, 30, 335, 10.1093/bioinformatics/btt691 Cheng, 2006, Prediction of protein stability changes for single-site mutations using support vector machines, Protein.: Struct., Funct. Genet., 62, 1125, 10.1002/prot.20810 MacLean, 2010, Skyline: an open source document editor for creating and analyzing targeted proteomics experiments, Bioinformatics, 26, 966, 10.1093/bioinformatics/btq054 Bereman, 2012, The development of selected reaction monitoring methods for targeted proteomics via empirical refinement, Proteomics, 12, 1134, 10.1002/pmic.201200042 R. Ribeiro, E. Machado, M. Santos, R. Oliveira, Photosynthesis and water relations of well-watered orange plants as affected by winter and summer conditions, 2009. Correia, 2016, Integrated proteomics and metabolomics to unlock global and clonal responses of Eucalyptus globulus recovery from water deficit, Metabolomics, 12, 10.1007/s11306-016-1088-4 de, 2020, Proteomic analyses unraveling water stress response in two Eucalyptus species originating from contrasting environments for aridity, Mol. Biol. Rep., 47, 5191, 10.1007/s11033-020-05594-1 Xu, 2020, Effects of salt and drought stresses on rhizosphere soil bacterial community structure and peanut yield, Ying Yong Sheng Tai Xue Bao, 31, 1305 Du, 2019, Response of proteome and morphological structure to short-term drought and subsequent recovery in Cucumis sativus leaves, Physiol. Plant, 167, 676, 10.1111/ppl.12926 Wang, 2004, Role of plant heat-shock proteins and molecular chaperones in the abiotic stress response, Trends Plant Sci., 9, 244, 10.1016/j.tplants.2004.03.006 A. Coxon, K. Maundrell’, S.E. Kearsey, Fission yeast cdc2l+ belongs to a family of proteins involved in an early step of chromosome replication, 1992. Coutinho, 2003, Why are there so many carbohydrate-active enzyme-related genes in plants?, Trends Plant Sci., 8, 563, 10.1016/j.tplants.2003.10.002 de Souza Rolim, 2016, Camargo, Köppen and Thornthwaite climate classification systems in defining climatical regions of the state of São Paulo, Brazil, Int. J. Climatol., 36, 636, 10.1002/joc.4372 Zhang, 2018, Dynamics and function of DNA methylation in plants, Nat. Rev. Mol. Cell Biol., 19, 489, 10.1038/s41580-018-0016-z Robertson, 2005, DNA methylation and human disease, Nat. Rev. Genet, 6, 597, 10.1038/nrg1655 R. Yasuda, H. Noji, M. Yoshida, K. Kinosita Jr, H. Itoh, Resolution of distinct rotational substeps by submillisecond kinetic analysis of F 1-ATPase, 2001. 〈www.nature.com〉. Perdomo, 2017, Rubisco and rubisco activase play an important role in the biochemical limitations of photosynthesis in rice, wheat, and maize under high temperature and water deficit, Front Plant Sci., 8, 10.3389/fpls.2017.00490 de, 2020, Proteomic analyses unraveling water stress response in two Eucalyptus species originating from contrasting environments for aridity, Mol. Biol. Rep., 47, 5191, 10.1007/s11033-020-05594-1 Floryszak-Wieczorek, 2017, The multifunctional face of plant carbonic anhydrase, Plant Physiol. Biochem., 112, 362, 10.1016/j.plaphy.2017.01.007 Katam, 2016, Comparative leaf proteomics of drought-tolerant and -susceptible peanut in response to water stress, J. Proteom., 143, 209, 10.1016/j.jprot.2016.05.031 Chai, 2016, Regulated deficit irrigation for crop production under drought stress. A review, Agron. Sustain Dev., 36, 1, 10.1007/s13593-015-0338-6 Ghirardo, 2021, Protein expression plasticity contributes to heat and drought tolerance of date palm, Oecologia, 197, 903, 10.1007/s00442-021-04907-w Abraham, 2015, Integrating mRNA and protein sequencing enables the detection and quantitative profiling of natural protein sequence variants of populus trichocarpa, J. Proteome Res, 14, 5318, 10.1021/acs.jproteome.5b00823 Koomen, 2021, Amino acid substitutions in ribosomal protein RpsU enable switching between high fitness and multiple-stress resistance in Listeria monocytogenes, Int J. Food Microbiol, 351, 10.1016/j.ijfoodmicro.2021.109269 Teng, 2010, Structural assessment of the effects of amino acid substitutions on protein stability and protein-protein interaction, Int J. Comput. Biol. Drug Des., 3, 334, 10.1504/IJCBDD.2010.038396 Marais, 2014, Variation in MPK12 affects water use efficiency in Arabidopsis and reveals a pleiotropic link between guard cell size and ABA response, Proc. Natl. Acad. Sci. USA, 111, 2836, 10.1073/pnas.1321429111 Nelson, 2017 DePristo, 2005, Missense meanderings in sequence space: a biophysical view of protein evolution, Nat. Rev. Genet, 6, 678, 10.1038/nrg1672 Somera, 1995, PROTEINS Temp. Bull, 2000, Big-benefit mutations in a bacteriophage inhibited with heat, Mol. Biol. Evol., 17, 942, 10.1093/oxfordjournals.molbev.a026375 Wilson, 1992, Structural and thermodynamic analysis of compensating mutations within the core of chicken egg white lysozyme, J. Biol. Chem., 267, 10842, 10.1016/S0021-9258(19)50095-3 Beadle, 2002, Structural bases of stability-function tradeoffs in enzymes, J. Mol. Biol., 321, 285, 10.1016/S0022-2836(02)00599-5 B.K. Shoichet, W.A. Baase, R. Kurokit, B.W. Matrhewst, A relationship between protein stability and protein function, 1995. Freue, 2012, Multiple Reaction Monitoring (MRM): principles and application to coronary artery disease, Circ. Cardiovasc Genet, 5 Annesley, 2003, Ion suppression in mass spectrometry, Clin. Chem., 49, 1041, 10.1373/49.7.1041