OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články, přehledy
Grantová podpora
075-15-2019-1881
Ministry of Science and Higher Education of the Russian Federation
426557363
Deutsche Forschungsgemeinschaft
Germany´s Excellence Strategy - EXC-2048/1 - project ID 390686111
Deutsche Forschungsgemeinschaft
CZ.02.1.01./0.0/0.0/16_019/0000827, SPP 813103381
Grantová Agentura České Republiky
1201973
Chilean National Fund for Scientific and Technological Development
20-16-00115
Russian Science Foundation
17-14-01363
Russian Science Foundation
NSERC
Natural Sciences and Engineering Research Council of Canada
PubMed
34371624
PubMed Central
PMC8309286
DOI
10.3390/plants10071423
PII: plants10071423
Knihovny.cz E-zdroje
- Klíčová slova
- epigenetics, epigenomics, genome sequencing, genomic prediction, omics, plant microbiome, site-directed mutagenesis, transcriptome,
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.
Department of Biological Sciences University of Lethbridge Lethbridge AB T1K 3M4 Canada
Faculty of Engineering and Natural Sciences Sabanci University 34956 Istanbul Turkey
Institute of Biological Sciences University of Talca 1 Poniente 1141 Talca 3460000 Chile
KWS SAAT SE and Co KGaA Grimsehlstr 31 37555 Einbeck Germany
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World Health Organization World Hunger Is Still Not Going Down after Three Years and Obesity Is Still Growing. [(accessed on 25 April 2021)]; Available online: https://www.who.int/news/item/15-07-2019-world-hunger-is-still-not-going-down-after-three-years-and-obesity-is-still-growing-un-report.
Watson J.D., Crick F.H. Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature. 1953;171:737–738. doi: 10.1038/171737a0. PubMed DOI
Sanger F., Tuppy H. The amino-acid sequence in the phenylalanyl chain of insulin. I. The identification of lower peptides from partial hydrolysates. Biochem. J. 1951;49:463–481. doi: 10.1042/bj0490463. PubMed DOI PMC
Sanger F., Tuppy H. The amino-acid sequence in the phenylalanyl chain of insulin. 2. The investigation of peptides from enzymic hydrolysates. Biochem. J. 1951;49:481–490. doi: 10.1042/bj0490481. PubMed DOI PMC
Giani A.M., Gallo G.R., Gianfranceschi L., Formenti G. Long walk to genomics: History and current approaches to genome sequencing and assembly. Comput. Struct. Biotechnol. J. 2020;18:9–19. doi: 10.1016/j.csbj.2019.11.002. PubMed DOI PMC
Holley R.W., Apgar J., Everett G.A., Madison J.T., Marquisee M., Merrill S.H., Penswick J.R., Zamir A. Structure of a Ribonucleic Acid. Science. 1965;147:1462–1465. doi: 10.1126/science.147.3664.1462. PubMed DOI
Sanger F., Coulson A.R. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J. Mol. Biol. 1975;94:441–448. doi: 10.1016/0022-2836(75)90213-2. PubMed DOI
Maxam A.M., Gilbert W. A new method for sequencing DNA. Proc. Natl. Acad. Sci. USA. 1977;74:560–564. doi: 10.1073/pnas.74.2.560. PubMed DOI PMC
Sanger F., Nicklen S., Coulson A.R. DNA sequencing with chain-terminating inhibitors. Proc. Natl. Acad. Sci. USA. 1977;74:5463–5467. doi: 10.1073/pnas.74.12.5463. PubMed DOI PMC
Swerdlow H., Gesteland R. Capillary gel electrophoresis for rapid, high resolution DNA sequencing. Nucleic Acids Res. 1990;18:1415–1419. doi: 10.1093/nar/18.6.1415. PubMed DOI PMC
Gut I.G. New sequencing technologies. Clin. Transl. Oncol. 2013;15:879–881. doi: 10.1007/s12094-013-1073-6. PubMed DOI
Kumar K.R., Cowley M.J., Davis R.L. Next-Generation Sequencing and Emerging Technologies. Semin. Thromb. Hemost. 2019;45:661–673. doi: 10.1055/s-0039-1688446. PubMed DOI
Slatko B.E., Gardner A.F., Ausubel F.M. Overview of Next-Generation Sequencing Technologies. Curr. Protoc. Mol. Biol. 2018;122:e59. doi: 10.1002/cpmb.59. PubMed DOI PMC
Heather J.M., Chain B. The sequence of sequencers: The history of sequencing DNA. Genomics. 2016;107:1–8. doi: 10.1016/j.ygeno.2015.11.003. PubMed DOI PMC
Lee H., Gurtowski J., Yoo S., Nattestad M., Marcus S., Goodwin S., McCombie W.R., Schatz M.C. Third-generation sequencing and the future of genomics. bioRxiv. 2016:048603. doi: 10.1101/048603. DOI
Eid J., Fehr A., Gray J., Luong K., Lyle J., Otto G., Peluso P., Rank D., Baybayan P., Bettman B., et al. Real-time DNA sequencing from single polymerase molecules. Science. 2009;323:133–138. doi: 10.1126/science.1162986. PubMed DOI
Jung H., Jeon M.S., Hodgett M., Waterhouse P., Eyun S.I. Comparative Evaluation of Genome Assemblers from Long-Read Sequencing for Plants and Crops. J. Agric. Food Chem. 2020;68:7670–7677. doi: 10.1021/acs.jafc.0c01647. PubMed DOI
Wee Y., Bhyan S.B., Liu Y., Lu J., Li X., Zhao M. The bioinformatics tools for the genome assembly and analysis based on third-generation sequencing. Brief. Funct. Genom. 2019;18:1–12. doi: 10.1093/bfgp/ely037. PubMed DOI
Lappalainen T., Scott A.J., Brandt M., Hall I.M. Genomic Analysis in the Age of Human Genome Sequencing. Cell. 2019;177:70–84. doi: 10.1016/j.cell.2019.02.032. PubMed DOI PMC
Goodwin S., McPherson J.D., McCombie W.R. Coming of age: Ten years of next-generation sequencing technologies. Nat. Rev. Genet. 2016;17:333–351. doi: 10.1038/nrg.2016.49. PubMed DOI PMC
Kersey P.J. Plant genome sequences: Past, present, future. Curr. Opin. Plant Biol. 2019;48:1–8. doi: 10.1016/j.pbi.2018.11.001. PubMed DOI
Blanc G., Wolfe K.H. Widespread paleopolyploidy in model plant species inferred from age distributions of duplicate genes. Plant Cell. 2004;16:1667–1678. doi: 10.1105/tpc.021345. PubMed DOI PMC
The International Wheat Genome Sequencing Consortium (IWGSC) Appels R., Eversole K., Stein N., Feuillet C., Keller B., Rogers J., Pozniak C.J., Choulet F., Distelfeld A., et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. 2018;361 doi: 10.1126/science.aar7191. PubMed DOI
Bolger M., Schwacke R., Gundlach H., Schmutzer T., Chen J., Arend D., Oppermann M., Weise S., Lange M., Fiorani F., et al. From plant genomes to phenotypes. J. Biotechnol. 2017;261:46–52. doi: 10.1016/j.jbiotec.2017.06.003. PubMed DOI
Akpinar B.A., Lucas S.J., Vrana J., Dolezel J., Budak H. Sequencing chromosome 5D of Aegilops tauschii and comparison with its allopolyploid descendant bread wheat (Triticum aestivum) Plant Biotechnol. J. 2015;13:740–752. doi: 10.1111/pbi.12302. PubMed DOI
Wendel J.F., Jackson S.A., Meyers B.C., Wing R.A. Evolution of plant genome architecture. Genome Biol. 2016;17:37. doi: 10.1186/s13059-016-0908-1. PubMed DOI PMC
Zimin A.V., Puiu D., Hall R., Kingan S., Clavijo B.J., Salzberg S.L. The first near-complete assembly of the hexaploid bread wheat genome, Triticum aestivum. Gigascience. 2017;6:1–7. doi: 10.1093/gigascience/gix097. PubMed DOI PMC
GenBank and WGS Statistics. [(accessed on 4 February 2021)]; Available online: https://www.ncbi.nlm.nih.gov/genbank/statistics/
Cagirici H.B., Sen T.Z., Budak H. mirMachine: A One-Stop Shop for Plant miRNA Annotation. J. Vis. Exp. 2021;171 doi: 10.3791/62430. PubMed DOI
Leroy P., Guilhot N., Sakai H., Bernard A., Choulet F., Theil S., Reboux S., Amano N., Flutre T., Pelegrin C., et al. TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes. Front. Plant Sci. 2012;3:5. doi: 10.3389/fpls.2012.00005. PubMed DOI PMC
Coletta R.D., Qiu Y., Ou S., Hufford M.B., Hirsch C.N. How the pan-genome is changing crop genomics and improvement. Genome Biol. 2021;22:3. doi: 10.1186/s13059-020-02224-8. PubMed DOI PMC
Bayer P.E., Golicz A.A., Scheben A., Batley J., Edwards D. Plant pan-genomes are the new reference. Nat. Plants. 2020;6:914–920. doi: 10.1038/s41477-020-0733-0. PubMed DOI
Tettelin H., Masignani V., Cieslewicz M.J., Donati C., Medini D., Ward N.L., Angiuoli S.V., Crabtree J., Jones A.L., Durkin A.S., et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial “pan-genome”. Proc. Natl. Acad. Sci. USA. 2005;102:13950–13955. doi: 10.1073/pnas.0506758102. PubMed DOI PMC
Danilevicz M.F., Fernandez C.G.T., Marsh J.I., Bayer P.E., Edwards D. Plant pangenomics: Approaches, applications and advancements. Curr. Opin. Plant Biol. 2020;54:18–25. doi: 10.1016/j.pbi.2019.12.005. PubMed DOI
Paux E., Faure S., Choulet F., Roger D., Gauthier V., Martinant J.P., Sourdille P., Balfourier F., Le Paslier M.C., Chauveau A., et al. Insertion site-based polymorphism markers open new perspectives for genome saturation and marker-assisted selection in wheat. Plant Biotechnol. J. 2010;8:196–210. doi: 10.1111/j.1467-7652.2009.00477.x. PubMed DOI
Ray S., Satya P. Next generation sequencing technologies for next generation plant breeding. Front. Plant Sci. 2014;5:367. doi: 10.3389/fpls.2014.00367. PubMed DOI PMC
He J., Zhao X., Laroche A., Lu Z.X., Liu H., Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front. Plant Sci. 2014;5:484. doi: 10.3389/fpls.2014.00484. PubMed DOI PMC
Pont C., Wagner S., Kremer A., Orlando L., Plomion C., Salse J. Paleogenomics: Reconstruction of plant evolutionary trajectories from modern and ancient DNA. Genome Biol. 2019;20:29. doi: 10.1186/s13059-019-1627-1. PubMed DOI PMC
Mendes R., Garbeva P., Raaijmakers J.M. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 2013;37:634–663. doi: 10.1111/1574-6976.12028. PubMed DOI
Daniel R. The metagenomics of soil. Nat. Rev. Microbiol. 2005;3:470–478. doi: 10.1038/nrmicro1160. PubMed DOI
Thompson L.R., Sanders J.G., McDonald D., Amir A., Ladau J., Locey K.J., Prill R.J., Tripathi A., Gibbons S.M., Ackermann G., et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–463. doi: 10.1038/nature24621. PubMed DOI PMC
Winogradsky H. Contribution to the study of microflora nitrification of wastewater and; Resistance of germs to unfavorable conditions. Ann. Inst. Pasteur. 1949;76:35–42. PubMed
Sanguin H., Remenant B., Dechesne A., Thioulouse J., Vogel T.M., Nesme X., Moenne-Loccoz Y., Grundmann G.L. Potential of a 16S rRNA-based taxonomic microarray for analyzing the rhizosphere effects of maize on Agrobacterium spp. and bacterial communities. Appl. Environ. Microbiol. 2006;72:4302–4312. doi: 10.1128/AEM.02686-05. PubMed DOI PMC
DeAngelis K.M., Brodie E.L., DeSantis T.Z., Andersen G.L., Lindow S.E., Firestone M.K. Selective progressive response of soil microbial community to wild oat roots. ISME J. 2009;3:168–178. doi: 10.1038/ismej.2008.103. PubMed DOI
Roesch L.F., Fulthorpe R.R., Riva A., Casella G., Hadwin A.K., Kent A.D., Daroub S.H., Camargo F.A., Farmerie W.G., Triplett E.W. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 2007;1:283–290. doi: 10.1038/ismej.2007.53. PubMed DOI PMC
Fierer N., Breitbart M., Nulton J., Salamon P., Lozupone C., Jones R., Robeson M., Edwards R.A., Felts B., Rayhawk S., et al. Metagenomic and small-subunit rRNA analyses reveal the genetic diversity of bacteria, archaea, fungi, and viruses in soil. Appl. Environ. Microbiol. 2007;73:7059–7066. doi: 10.1128/AEM.00358-07. PubMed DOI PMC
Uroz S., Buee M., Murat C., Frey-Klett P., Martin F. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2010;2:281–288. doi: 10.1111/j.1758-2229.2009.00117.x. PubMed DOI
Edwards R.A., Rodriguez-Brito B., Wegley L., Haynes M., Breitbart M., Peterson D.M., Saar M.O., Alexander S., Alexander E.C., Jr., Rohwer F. Using pyrosequencing to shed light on deep mine microbial ecology. BMC Genom. 2006;7:57. doi: 10.1186/1471-2164-7-57. PubMed DOI PMC
Rausch P., Ruhlemann M., Hermes B.M., Doms S., Dagan T., Dierking K., Domin H., Fraune S., von Frieling J., Hentschel U., et al. Comparative analysis of amplicon and metagenomic sequencing methods reveals key features in the evolution of animal metaorganisms. Microbiome. 2019;7:133. doi: 10.1186/s40168-019-0743-1. PubMed DOI PMC
Anderson I.C., Cairney J.W. Diversity and ecology of soil fungal communities: Increased understanding through the application of molecular techniques. Environ. Microbiol. 2004;6:769–779. doi: 10.1111/j.1462-2920.2004.00675.x. PubMed DOI
Schoch C.L., Seifert K.A., Huhndorf S., Robert V., Spouge J.L., Levesque C.A., Chen W. Fungal Barcoding Consortium. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl. Acad. Sci. USA. 2012;109:6241–6246. doi: 10.1073/pnas.1117018109. PubMed DOI PMC
Blaalid R., Kumar S., Nilsson R.H., Abarenkov K., Kirk P.M., Kauserud H. ITS1 versus ITS2 as DNA metabarcodes for fungi. Mol. Ecol. Resour. 2013;13:218–224. doi: 10.1111/1755-0998.12065. PubMed DOI
Tremblay J., Singh K., Fern A., Kirton E.S., He S., Woyke T., Lee J., Chen F., Dangl J.L., Tringe S.G. Primer and platform effects on 16S rRNA tag sequencing. Front. Microbiol. 2015;6:771. doi: 10.3389/fmicb.2015.00771. PubMed DOI PMC
Bahram M., Anslan S., Hildebrand F., Bork P., Tedersoo L. Newly designed 16S rRNA metabarcoding primers amplify diverse and novel archaeal taxa from the environment. Environ. Microbiol. Rep. 2019;11:487–494. doi: 10.1111/1758-2229.12684. PubMed DOI PMC
Shakya M., Lo C.C., Chain P.S.G. Advances and Challenges in Metatranscriptomic Analysis. Front. Genet. 2019;10:904. doi: 10.3389/fgene.2019.00904. PubMed DOI PMC
Escobar-Zepeda A., de Leon A.V.-P., Sanchez-Flores A. The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics. Front. Genet. 2015;6:348. doi: 10.3389/fgene.2015.00348. PubMed DOI PMC
Schloss P.D., Handelsman J. Metagenomics for studying unculturable microorganisms: Cutting the Gordian knot. Genome Biol. 2005;6:229. doi: 10.1186/gb-2005-6-8-229. PubMed DOI PMC
Laudadio I., Fulci V., Palone F., Stronati L., Cucchiara S., Carissimi C. Quantitative Assessment of Shotgun Metagenomics and 16S rDNA Amplicon Sequencing in the Study of Human Gut Microbiome. OMICS. 2018;22:248–254. doi: 10.1089/omi.2018.0013. PubMed DOI
Jovel J., Patterson J., Wang W., Hotte N., O’Keefe S., Mitchel T., Perry T., Kao D., Mason A.L., Madsen K.L., et al. Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics. Front. Microbiol. 2016;7:459. doi: 10.3389/fmicb.2016.00459. PubMed DOI PMC
Ranjan R., Rani A., Metwally A., McGee H.S., Perkins D.L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 2016;469:967–977. doi: 10.1016/j.bbrc.2015.12.083. PubMed DOI PMC
Xu J., Zhang Y., Zhang P., Trivedi P., Riera N., Wang Y., Liu X., Fan G., Tang J., Coletta-Filho H.D., et al. The structure and function of the global citrus rhizosphere microbiome. Nat. Commun. 2018;9:4894. doi: 10.1038/s41467-018-07343-2. PubMed DOI PMC
Ma Y., Marais A., Lefebvre M., Theil S., Svanella-Dumas L., Faure C., Candresse T. Phytovirome Analysis of Wild Plant Populations: Comparison of Double-Stranded RNA and Virion-Associated Nucleic Acid Metagenomic Approaches. J. Virol. 2019;94 doi: 10.1128/JVI.01462-19. PubMed DOI PMC
Keegan K.P., Glass E.M., Meyer F. MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function. Methods Mol. Biol. 2016;1399:207–233. doi: 10.1007/978-1-4939-3369-3_13. PubMed DOI
Quince C., Walker A.W., Simpson J.T., Loman N.J., Segata N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 2017;35:833–844. doi: 10.1038/nbt.3935. PubMed DOI
Douglas G.M., Maffei V.J., Zaneveld J.R., Yurgel S.N., Brown J.R., Taylor C.M., Huttenhower C., Langille M.G.I. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 2020;38:685–688. doi: 10.1038/s41587-020-0548-6. PubMed DOI PMC
Nguyen M., Wemheuer B., Laffy P.W., Webster N.S., Thomas T. Taxonomic, functional and expression analysis of viral communities associated with marine sponges. PeerJ. 2021;9:e10715. doi: 10.7717/peerj.10715. PubMed DOI PMC
Murali A., Bhargava A., Wright E.S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome. 2018;6:140. doi: 10.1186/s40168-018-0521-5. PubMed DOI PMC
Young J.P.W., Moeskjaer S., Afonin A., Rahi P., Maluk M., James E.K., Cavassim M.I.A., Rashid M.H., Aserse A.A., Perry B.J., et al. Defining the Rhizobium leguminosarum Species Complex. Genes. 2021;12:111. doi: 10.3390/genes12010111. PubMed DOI PMC
Balvociute M., Huson D.H. SILVA, RDP, Greengenes, NCBI and OTT—How do these taxonomies compare? BMC Genom. 2017;18:114. doi: 10.1186/s12864-017-3501-4. PubMed DOI PMC
Xu J. Fungal DNA barcoding. Genome. 2016;59:913–932. doi: 10.1139/gen-2016-0046. PubMed DOI
Segata N., Waldron L., Ballarini A., Narasimhan V., Jousson O., Huttenhower C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods. 2012;9:811–814. doi: 10.1038/nmeth.2066. PubMed DOI PMC
Franzosa E.A., McIver L.J., Rahnavard G., Thompson L.R., Schirmer M., Weingart G., Lipson K.S., Knight R., Caporaso J.G., Segata N., et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods. 2018;15:962–968. doi: 10.1038/s41592-018-0176-y. PubMed DOI PMC
Liu F., Hewezi T., Lebeis S.L., Pantalone V., Grewal P.S., Staton M.E. Soil indigenous microbiome and plant genotypes cooperatively modify soybean rhizosphere microbiome assembly. BMC Microbiol. 2019;19:201. doi: 10.1186/s12866-019-1572-x. PubMed DOI PMC
Murray A.E., Freudenstein J., Gribaldo S., Hatzenpichler R., Hugenholtz P., Kampfer P., Konstantinidis K.T., Lane C.E., Papke R.T., Parks D.H., et al. Roadmap for naming uncultivated Archaea and Bacteria. Nat. Microbiol. 2020;5:987–994. doi: 10.1038/s41564-020-0733-x. PubMed DOI PMC
Schulz T., Stoye J., Doerr D. GraphTeams: A method for discovering spatial gene clusters in Hi-C sequencing data. BMC Genom. 2018;19:308. doi: 10.1186/s12864-018-4622-0. PubMed DOI PMC
Berendsen R.L., Pieterse C.M., Bakker P.A. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–486. doi: 10.1016/j.tplants.2012.04.001. PubMed DOI
Ciccazzo S., Esposito A., Rolli E., Zerbe S., Daffonchio D., Brusetti L. Different pioneer plant species select specific rhizosphere bacterial communities in a high mountain environment. Springerplus. 2014;3:391. doi: 10.1186/2193-1801-3-391. PubMed DOI PMC
Lundberg D.S., Lebeis S.L., Paredes S.H., Yourstone S., Gehring J., Malfatti S., Tremblay J., Engelbrektson A., Kunin V., Del Rio T.G., et al. Defining the core Arabidopsis thaliana root microbiome. Nature. 2012;488:86–90. doi: 10.1038/nature11237. PubMed DOI PMC
Santoyo G., Moreno-Hagelsieb G., Mdel C.O.-M., Glick B.R. Plant growth-promoting bacterial endophytes. Microbiol. Res. 2016;183:92–99. doi: 10.1016/j.micres.2015.11.008. PubMed DOI
Turner T.R., James E.K., Poole P.S. The plant microbiome. Genome Biol. 2013;14:209. doi: 10.1186/gb-2013-14-6-209. PubMed DOI PMC
Bulgarelli D., Rott M., Schlaeppi K., van Themaat E.V.L., Ahmadinejad N., Assenza F., Rauf P., Huettel B., Reinhardt R., Schmelzer E., et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature. 2012;488:91–95. doi: 10.1038/nature11336. PubMed DOI
Mendes R., Kruijt M., de Bruijn I., Dekkers E., van der Voort M., Schneider J.H., Piceno Y.M., DeSantis T.Z., Andersen G.L., Bakker P.A., et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science. 2011;332:1097–1100. doi: 10.1126/science.1203980. PubMed DOI
Xu T., Cao L., Zeng J., Franco C.M.M., Yang Y., Hu X., Liu Y., Wang X., Gao Y., Bu Z., et al. The antifungal action mode of the rice endophyte Streptomyces hygroscopicus OsiSh-2 as a potential biocontrol agent against the rice blast pathogen. Pestic. Biochem. Physiol. 2019;160:58–69. doi: 10.1016/j.pestbp.2019.06.015. PubMed DOI
Teplitski M., Barak J.D., Schneider K.R. Human enteric pathogens in produce: Un-answered ecological questions with direct implications for food safety. Curr. Opin. Biotechnol. 2009;20:166–171. doi: 10.1016/j.copbio.2009.03.002. PubMed DOI
Marin S., Ramos A.J., Cano-Sancho G., Sanchis V. Mycotoxins: Occurrence, toxicology, and exposure assessment. Food Chem. Toxicol. 2013;60:218–237. doi: 10.1016/j.fct.2013.07.047. PubMed DOI
Melotto M., Brandl M.T., Jacob C., Jay-Russell M.T., Micallef S.A., Warburton M.L., Van Deynze A. Breeding Crops for Enhanced Food Safety. Front. Plant Sci. 2020;11:428. doi: 10.3389/fpls.2020.00428. PubMed DOI PMC
Gorshkov V., Osipova E., Ponomareva M., Ponomarev S., Gogoleva N., Petrova O., Gogoleva O., Meshcherov A., Balkin A., Vetchinkina E., et al. Rye Snow Mold-Associated Microdochium nivale Strains Inhabiting a Common Area: Variability in Genetics, Morphotype, Extracellular Enzymatic Activities, and Virulence. J. Fungi. 2020;6:335. doi: 10.3390/jof6040335. PubMed DOI PMC
Chiu C.Y., Miller S.A. Clinical metagenomics. Nat. Rev. Genet. 2019;20:341–355. doi: 10.1038/s41576-019-0113-7. PubMed DOI PMC
Jain M., Olsen H.E., Paten B., Akeson M. The Oxford Nanopore MinION: Delivery of nanopore sequencing to the genomics community. Genome Biol. 2016;17:239. doi: 10.1186/s13059-016-1103-0. PubMed DOI PMC
Leggett R.M., Clark M.D. A world of opportunities with nanopore sequencing. J. Exp. Bot. 2017;68:5419–5429. doi: 10.1093/jxb/erx289. PubMed DOI
Rang F.J., Kloosterman W.P., de Ridder J. From squiggle to basepair: Computational approaches for improving nanopore sequencing read accuracy. Genome Biol. 2018;19:90. doi: 10.1186/s13059-018-1462-9. PubMed DOI PMC
Oxford Nanopore Technologies Nanopore Sequencing Accuracy. [(accessed on 10 July 2021)]; Available online: https://nanoporetech.com/accuracy.
Sevim V., Lee J., Egan R., Clum A., Hundley H., Lee J., Everroad R.C., Detweiler A.M., Bebout B.M., Pett-Ridge J., et al. Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies. Sci. Data. 2019;6:285. doi: 10.1038/s41597-019-0287-z. PubMed DOI PMC
Jongman M., Carmichael P.C., Bill M. Technological Advances in Phytopathogen Detection and Metagenome Profiling Techniques. Curr. Microbiol. 2020;77:675–681. doi: 10.1007/s00284-020-01881-z. PubMed DOI
Llontop M.E.M., Sharma P., Flores M.A., Yang S., Pollock J., Tian L., Huang C., Rideout S., Heath L.S., Li S., et al. Strain-Level Identification of Bacterial Tomato Pathogens Directly from Metagenomic Sequences. Phytopathology. 2020;110:768–779. doi: 10.1094/PHYTO-09-19-0351-R. PubMed DOI
Chalupowicz L., Dombrovsky A., Gaba V., Luria N., Reuven M., Beerman A., Lachman O., Dror O., Nissan G., Manulis-Sasson S. Diagnosis of plant diseases using the Nanopore sequencing platform. Plant Pathol. 2019;68:229–238. doi: 10.1111/ppa.12957. DOI
Ciuffreda L., Rodriguez-Perez H., Flores C. Nanopore sequencing and its application to the study of microbial communities. Comput. Struct. Biotechnol. J. 2021;19:1497–1511. doi: 10.1016/j.csbj.2021.02.020. PubMed DOI PMC
Schlaeppi K., Bulgarelli D. The plant microbiome at work. Mol. Plant Microbe Interact. 2015;28:212–217. doi: 10.1094/MPMI-10-14-0334-FI. PubMed DOI
Bulgarelli D., Schlaeppi K., Spaepen S., van Themaat E.V.L., Schulze-Lefert P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 2013;64:807–838. doi: 10.1146/annurev-arplant-050312-120106. PubMed DOI
Dangl J.L., Horvath D.M., Staskawicz B.J. Pivoting the plant immune system from dissection to deployment. Science. 2013;341:746–751. doi: 10.1126/science.1236011. PubMed DOI PMC
Stark R., Grzelak M., Hadfield J. RNA sequencing: The teenage years. Nat. Rev. Genet. 2019;20:631–656. doi: 10.1038/s41576-019-0150-2. PubMed DOI
Rani B., Sharma V. Transcriptome profiling: Methods and applications—A review. Agric. Rev. 2017;38:271–281. doi: 10.18805/ag.R-1549. DOI
Velculescu V.E., Zhang L., Vogelstein B., Kinzler K.W. Serial analysis of gene expression. Science. 1995;270:484–487. doi: 10.1126/science.270.5235.484. PubMed DOI
Schena M., Shalon D., Davis R.W., Brown P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467–470. doi: 10.1126/science.270.5235.467. PubMed DOI
Klepikova A.V., Logacheva M.D., Dmitriev S.E., Penin A.A. RNA-seq analysis of an apical meristem time series reveals a critical point in Arabidopsis thaliana flower initiation. BMC Genom. 2015;16:466. doi: 10.1186/s12864-015-1688-9. PubMed DOI PMC
Gorshkov O., Mokshina N., Gorshkov V., Chemikosova S., Gogolev Y., Gorshkova T. Transcriptome portrait of cellulose-enriched flax fibres at advanced stage of specialization. Plant Mol. Biol. 2017;93:431–449. doi: 10.1007/s11103-016-0571-7. PubMed DOI
Hu R., Xu Y., Yu C., He K., Tang Q., Jia C., He G., Wang X., Kong Y., Zhou G. Transcriptome analysis of genes involved in secondary cell wall biosynthesis in developing internodes of Miscanthus lutarioriparius. Sci. Rep. 2017;7:9034. doi: 10.1038/s41598-017-08690-8. PubMed DOI PMC
Kozlova L.V., Nazipova A.R., Gorshkov O.V., Petrova A.A., Gorshkova T.A. Elongating maize root: Zone-specific combinations of polysaccharides from type I and type II primary cell walls. Sci. Rep. 2020;10:10956. doi: 10.1038/s41598-020-67782-0. PubMed DOI PMC
Malviya M.K., Li C.N., Solanki M.K., Singh R.K., Htun R., Singh P., Verma K.K., Yang L.T., Li Y.R. Comparative analysis of sugarcane root transcriptome in response to the plant growth-promoting Burkholderia anthina MYSP113. PLoS ONE. 2020;15:e0231206. doi: 10.1371/journal.pone.0231206. PubMed DOI PMC
Castandet B., Hotto A.M., Strickler S.R., Stern D.B. ChloroSeq, an Optimized Chloroplast RNA-Seq Bioinformatic Pipeline, Reveals Remodeling of the Organellar Transcriptome Under Heat Stress. G3 Genes Genomes Genet. 2016;6:2817–2827. doi: 10.1534/g3.116.030783. PubMed DOI PMC
Xiong B., Qiu X., Huang S., Wang X., Zhang X., Dong T., Wang T., Li S., Sun G., Zhu J., et al. Physiological and transcriptome analyses of photosynthesis and chlorophyll metabolism in variegated Citrus (Shiranuhi and Huangguogan) seedlings. Sci. Rep. 2019;9:15670. doi: 10.1038/s41598-019-52276-5. PubMed DOI PMC
Romanowski A., Schlaen R.G., Perez-Santangelo S., Mancini E., Yanovsky M.J. Global transcriptome analysis reveals circadian control of splicing events in Arabidopsis thaliana. Plant J. 2020;103:889–902. doi: 10.1111/tpj.14776. PubMed DOI
Li Y., Guo G., Zhou L., Chen Y., Zong Y., Huang J., Lu R., Liu C. Transcriptome Analysis Identifies Candidate Genes and Functional Pathways Controlling the Response of Two Contrasting Barley Varieties to Powdery Mildew Infection. Int. J. Mol. Sci. 2019;21:151. doi: 10.3390/ijms21010151. PubMed DOI PMC
Tsers I., Gorshkov V., Gogoleva N., Parfirova O., Petrova O., Gogolev Y. Plant Soft Rot Development and Regulation from the Viewpoint of Transcriptomic Profiling. Plants. 2020;9:1176. doi: 10.3390/plants9091176. PubMed DOI PMC
Duan Y., Duan S., Armstrong M.R., Xu J., Zheng J., Hu J., Chen X., Hein I., Li G., Jin L. Comparative Transcriptome Profiling Reveals Compatible and Incompatible Patterns of Potato Toward Phytophthora infestans. G3 Genes Genomes Genet. 2020;10:623–634. doi: 10.1534/g3.119.400818. PubMed DOI PMC
Liu Y., Liu Y., Spetz C., Li L., Wang X. Comparative transcriptome analysis in Triticum aestivum infecting wheat dwarf virus reveals the effects of viral infection on phytohormone and photosynthesis metabolism pathways. Phytopathol. Res. 2020;2:1–13. doi: 10.1186/s42483-019-0042-6. DOI
Kang W.H., Sim Y.M., Koo N., Nam J.Y., Lee J., Kim N., Jang H., Kim Y.M., Yeom S.I. Transcriptome profiling of abiotic responses to heat, cold, salt, and osmotic stress of Capsicum annuum L. Sci. Data. 2020;7:17. doi: 10.1038/s41597-020-0352-7. PubMed DOI PMC
Huang J., Zhao X., Chory J. The Arabidopsis Transcriptome Responds Specifically and Dynamically to High Light Stress. Cell Rep. 2019;29:4186–4199. doi: 10.1016/j.celrep.2019.11.051. PubMed DOI PMC
Qiao D., Zhang Y., Xiong X., Li M., Cai K., Luo H., Zeng B. Transcriptome analysis on responses of orchardgrass (Dactylis glomerata L.) leaves to a short term flooding. Hereditas. 2020;157:20. doi: 10.1186/s41065-020-00134-0. PubMed DOI PMC
Safavi-Rizi V., Herde M., Stohr C. RNA-Seq reveals novel genes and pathways associated with hypoxia duration and tolerance in tomato root. Sci. Rep. 2020;10:1692. doi: 10.1038/s41598-020-57884-0. PubMed DOI PMC
Mokshina N., Gorshkov O., Galinousky D., Gorshkova T. Genes with bast fiber-specific expression in flax plants—Molecular keys for targeted fiber crop improvement. Ind. Crop. Prod. 2020;152 doi: 10.1016/j.indcrop.2020.112549. DOI
Galinousky D., Mokshina N., Padvitski T., Ageeva M., Bogdan V., Kilchevsky A., Gorshkova T. The Toolbox for Fiber Flax Breeding: A Pipeline From Gene Expression to Fiber Quality. Front. Genet. 2020;11:589881. doi: 10.3389/fgene.2020.589881. PubMed DOI PMC
Brandt R., Mascher M., Thiel J. Laser Capture Microdissection-Based RNA-Seq of Barley Grain Tissues. Methods Mol. Biol. 2018;1723:397–409. doi: 10.1007/978-1-4939-7558-7_23. PubMed DOI
Gorshkova T., Chernova T., Mokshina N., Gorshkov V., Kozlova L., Gorshkov O. Transcriptome Analysis of Intrusively Growing Flax Fibers Isolated by Laser Microdissection. Sci. Rep. 2018;8:14570. doi: 10.1038/s41598-018-32869-2. PubMed DOI PMC
Shulse C.N., Cole B.J., Ciobanu D., Lin J., Yoshinaga Y., Gouran M., Turco G.M., Zhu Y., O’Malley R.C., Brady S.M., et al. High-Throughput Single-Cell Transcriptome Profiling of Plant Cell Types. Cell Rep. 2019;27:2241–2247. e2244. doi: 10.1016/j.celrep.2019.04.054. PubMed DOI PMC
Shaw R., Tian X., Xu J. Single-Cell Transcriptome Analysis in Plants: Advances and Challenges. Mol. Plant. 2021;14:115–126. doi: 10.1016/j.molp.2020.10.012. PubMed DOI
Xu J., Chen Z., Wang F., Jia W., Xu Z. Combined transcriptomic and metabolomic analyses uncover rearranged gene expression and metabolite metabolism in tobacco during cold acclimation. Sci. Rep. 2020;10:5242. doi: 10.1038/s41598-020-62111-x. PubMed DOI PMC
Gao W., Sun H.X., Xiao H., Cui G., Hillwig M.L., Jackson A., Wang X., Shen Y., Zhao N., Zhang L., et al. Combining metabolomics and transcriptomics to characterize tanshinone biosynthesis in Salvia miltiorrhiza. BMC Genom. 2014;15:73. doi: 10.1186/1471-2164-15-73. PubMed DOI PMC
Wang R., Liu P., Fan J., Li L. Comparative transcriptome analysis two genotypes of Acer truncatum Bunge seeds reveals candidate genes that influences seed VLCFAs accumulation. Sci. Rep. 2018;8:15504. doi: 10.1038/s41598-018-33999-3. PubMed DOI PMC
Murat F., Van de Peer Y., Salse J. Decoding plant and animal genome plasticity from differential paleo-evolutionary patterns and processes. Genome Biol. Evol. 2012;4:917–928. doi: 10.1093/gbe/evs066. PubMed DOI PMC
Das S., McClain C.J., Rai S.N. Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges. Entropy. 2020;22:427. doi: 10.3390/e22040427. PubMed DOI PMC
Hill D.P., Smith B., McAndrews-Hill M.S., Blake J.A. Gene Ontology annotations: What they mean and where they come from. BMC Bioinform. 2008;9:S2. doi: 10.1186/1471-2105-9-S5-S2. PubMed DOI PMC
Gotz S., Garcia-Gomez J.M., Terol J., Williams T.D., Nagaraj S.H., Nueda M.J., Robles M., Talon M., Dopazo J., Conesa A. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420–3435. doi: 10.1093/nar/gkn176. PubMed DOI PMC
Kanehisa M., Sato Y., Morishima K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. J. Mol. Biol. 2016;428:726–731. doi: 10.1016/j.jmb.2015.11.006. PubMed DOI
Huerta-Cepas J., Szklarczyk D., Heller D., Hernandez-Plaza A., Forslund S.K., Cook H., Mende D.R., Letunic I., Rattei T., Jensen L.J., et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314. doi: 10.1093/nar/gky1085. PubMed DOI PMC
Schwacke R., Ponce-Soto G.Y., Krause K., Bolger A.M., Arsova B., Hallab A., Gruden K., Stitt M., Bolger M.E., Usadel B. MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. Mol. Plant. 2019;12:879–892. doi: 10.1016/j.molp.2019.01.003. PubMed DOI
Huang D.W., Sherman B.T., Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. PubMed DOI
Thomas P.D., Kejariwal A., Campbell M.J., Mi H., Diemer K., Guo N., Ladunga I., Ulitsky-Lazareva B., Muruganujan A., Rabkin S., et al. PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 2003;31:334–341. doi: 10.1093/nar/gkg115. PubMed DOI PMC
Caspi R., Billington R., Fulcher C.A., Keseler I.M., Kothari A., Krummenacker M., Latendresse M., Midford P.E., Ong Q., Ong W.K., et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46:D633–D639. doi: 10.1093/nar/gkx935. PubMed DOI PMC
Asp M., Bergenstråhle J., Lundeberg J. Spatially resolved transcriptomes—Next generation tools for tissue exploration. BioEssays. 2020;42:1–19. doi: 10.1002/bies.201900221. PubMed DOI
Moses L., Pachter L. Museum of Spatial Transcriptomics. [(accessed on 1 April 2021)]; Available online: https://bookdown.org/lambdamoses/museumst/
Kerk N.M., Ceserani T., Tausta S.L., Sussex I.M., Nelson T.M. Laser capture microdissection of cells from plant tissues. Plant Physiol. 2003;132:27–35. doi: 10.1104/pp.102.018127. PubMed DOI PMC
Gautam V., Singh A., Singh S., Sarkar A.K. An Efficient LCM-Based Method for Tissue Specific Expression Analysis of Genes and miRNAs. Sci. Rep. 2016;6:21577. doi: 10.1038/srep21577. PubMed DOI PMC
Reuper H., Amari K., Krenz B. Analyzing the G3BP-like gene family of Arabidopsis thaliana in early turnip mosaic virus infection. Sci. Rep. 2021;11:1–11. doi: 10.1038/s41598-021-81276-7. PubMed DOI PMC
Nelson T., Tausta S.L., Gandotra N., Liu T. Laser microdissection of plant tissue: What you see is what you get. Annu. Rev. Plant Biol. 2006;57:181–201. doi: 10.1146/annurev.arplant.56.032604.144138. PubMed DOI
Liew L.C., Wang Y., Peirats-Llobet M., Berkowitz O., Whelan J., Lewsey M.G. Laser-Capture Microdissection RNA-sequencing for spatial and temporal tissue-specific gene expression analysis in plants. J. Vis. Exp. 2020;162 doi: 10.3791/61517. PubMed DOI
Shibutani M., Uneyama C., Miyazaki K., Toyoda K., Hirose M. Methacarn fixation: A novel tool for analysis of gene expressions in paraffin-embedded tissue specimens. Lab. Investig. 2000;80:199–208. doi: 10.1038/labinvest.3780023. PubMed DOI
Serova T.A., Tikhonovich I.A., Tsyganov V.E. Analysis of nodule senescence in pea (Pisum sativum L.) using laser microdissection, real-time PCR, and ACC immunolocalization. J. Plant. Physiol. 2017;212:29–44. doi: 10.1016/j.jplph.2017.01.012. PubMed DOI
Schrader J., Nilsson J., Mellerowicz E., Berglund A., Nilsson P., Hertzberg M., Sandberg G. A high-resolution transcript profile across the wood-forming meristem of poplar identifies potential regulators of cambial stem cell identity. Plant Cell. 2004;16:2278–2292. doi: 10.1105/tpc.104.024190. PubMed DOI PMC
Immanen J., Nieminen K., Smolander O.-P., Kojima M., Alonso Serra J., Koskinen P., Zhang J., Elo A., Mähönen A.P., Street N., et al. Cytokinin and auxin display distinct but interconnected distribution and signaling profiles to stimulate cambial activity. Curr. Biol. 2016;26:1990–1997. doi: 10.1016/j.cub.2016.05.053. PubMed DOI
Sundell D., Street N.R., Kumar M., Mellerowicz E.J., Kucukoglu M., Johnsson C., Kumar V., Mannapperuma C., Delhomme N., Nilsson O., et al. AspWood: High-spatial-resolution transcriptome profiles reveal uncharacterized modularity of wood formation in Populus tremula. Plant Cell. 2017;29:1585–1604. doi: 10.1105/tpc.17.00153. PubMed DOI PMC
Angerer L.M., Angerer R.C. Detection of poly A+ RNA in sea urchin eggs and embryos by quantitative in situ hybridization. Nucleic Acids Res. 1981;9:2819–2840. doi: 10.1093/nar/9.12.2819. PubMed DOI PMC
Dietrich R.A., Maslyar D.J., Heupel R.C., Harada J.J. Spatial patterns of gene expression in Brassica napus seedlings: Identification of a cortex-specific gene and localization of mRNAs encoding isocitrate lyase and a polypeptide homologous to proteinases. Plant Cell. 1989;1:73–80. doi: 10.1105/tpc.1.1.73. PubMed DOI PMC
Young A.P., Jackson D.J., Wyeth R.C. A technical review and guide to RNA fluorescence in situ hybridization. PeerJ. 2020;8:1–27. doi: 10.7717/peerj.8806. PubMed DOI PMC
Singer R.H., Ward D.C. Actin gene expression visualized in chicken muscle tissue culture by using in situ hybridization with a biotinated nucleotide analog. Proc. Natl. Acad. Sci. USA. 1982;79:7331–7335. doi: 10.1073/pnas.79.23.7331. PubMed DOI PMC
Kitomi Y., Hanzawa E., Kuya N., Inoue H., Hara N., Kawai S., Kanno N., Endo M., Sugimoto K., Yamazaki T., et al. Root angle modifications by the DRO1 homolog improve rice yields in saline paddy fields. Proc. Natl. Acad. Sci. USA. 2020;117:21242–21250. doi: 10.1073/pnas.2005911117. PubMed DOI PMC
Yang W., Cortijo S., Korsbo N., Roszak P., Schiessl K., Gurzadyan A., Wightman R., Jonsson H., Meyerowitz E. Molecular mechanism of cytokinin-activated cell division in Arabidopsis. Science. 2021;371:1350–1355. doi: 10.1126/science.abe2305. PubMed DOI PMC
Duncan S., Rosa S. Gaining insight into plant gene transcription using smFISH. Transcription. 2018;9:166–170. doi: 10.1080/21541264.2017.1372043. PubMed DOI PMC
Femino A.M., Fay F.S., Fogarty K., Singer R.H. Visualization of single RNA transcripts in situ. Science. 1998;280:585–590. doi: 10.1126/science.280.5363.585. PubMed DOI
Rosa S., Duncan S., Dean C. Mutually exclusive sense-antisense transcription at FLC facilitates environmentally induced gene repression. Nat. Commun. 2016;7:13031. doi: 10.1038/ncomms13031. PubMed DOI PMC
Duncan S., Olsson T.S.G., Hartley M., Dean C., Rosa S. A method for detecting single mRNA molecules in Arabidopsis thaliana. Plant Methods. 2016;12:1–10. doi: 10.1186/s13007-016-0114-x. PubMed DOI PMC
Duncan S., Olsson T.S.G., Hartley M., Dean C., Rosa S. Single molecule RNA FISH in Arabidopsis root cells. Bio Protocol. 2017;7:1–10. doi: 10.21769/BioProtoc.2240. PubMed DOI PMC
Huang K., Demirci F., Batish M., Treible W., Meyers B.C., Caplan J.L. Quantitative, super-resolution localization of small RNAs with sRNA-PAINT. Nucleic Acids Res. 2020;48:1–13. doi: 10.1093/nar/gkaa623. PubMed DOI PMC
Huang K., Batish M., Teng C., Harkess A., Meyers B.C., Caplan J.L. Quantitative fluorescence in situ hybridization detection of plant mRNAs with single-molecule resolution. In: Heinlein M., editor. RNA Tagging: Methods and Protocols. Volume 2166. Springer; New York, NY, USA: 2020. pp. 23–33. PubMed
Wang F., Flanagan J., Su N., Wang L.-C., Bui S., Nielson A., Wu X., Vo H.-T., Ma X.-J., Luo Y. RNAscope: A novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 2012;14:22–29. doi: 10.1016/j.jmoldx.2011.08.002. PubMed DOI PMC
Bowling A.J., Pence H.E., Church J.B. Application of a novel and automated branched DNA in situ hybridization method for the rapid and sensitive localization of mRNA molecules in plant tissues. Appl. Plant Sci. 2014;2:1–5. doi: 10.3732/apps.1400011. PubMed DOI PMC
Bergua M., Phelan D.M., Bak A., Bloom D.C., Folimonova S.Y. Simultaneous visualization of two Citrus tristeza virus genotypes provides new insights into the structure of multi-component virus populations in a host. Virology. 2016;491:10–19. doi: 10.1016/j.virol.2016.01.017. PubMed DOI
Munganyinka E., Margaria P., Sheat S., Ateka E.M., Tairo F., Ndunguru J., Winter S. Localization of cassava brown streak virus in Nicotiana rustica and cassava Manihot esculenta (Crantz) using RNAscope® in situ hybridization. Virol. J. 2018;15:1–11. doi: 10.1186/s12985-018-1038-z. PubMed DOI PMC
Sheat S., Winter S., Margaria P. Duplex in situ hybridization of virus nucleic acids in plant tissues using RNAscope®. In: Nielsen B.S., Jones J., editors. In Situ Hybridization Protocols. 5th ed. Volume 2148. Springer; New York, NY, USA: 2020. pp. 203–215. Methods in Molecular Biology. PubMed
Solanki S., Ameen G., Zhao J., Flaten J., Borowicz P., Brueggeman R.S. Visualization of spatial gene expression in plants by modified RNAscope fluorescent in situ hybridization. Plant Methods. 2020;16:1–9. doi: 10.1186/s13007-020-00614-4. PubMed DOI PMC
plaBiPD. [(accessed on 1 April 2021)]; Available online: https://www.plabipd.de/index.ep.
Wang K.N., editor. Agrobacterium Protocols. 3rd ed. Volume 1–2. Springer; New York, NY, USA: 2015. p. 365.
Kumar S., Barone P., Smith M., editors. Transgenic Plants: Methods and Protocols. Volume 1864. Springer; New York, NY, USA: 2020. p. 438.
Valla S., Lale R., editors. DNA Cloning and Assembly Methods. Volume 1116. Springer; New York, NY, USA: 2014. p. 308.
Ståhl P.L., Salmén F., Vickovic S., Lundmark A., Navarro J.F., Magnusson J., Giacomello S., Asp M., Westholm J.O., Huss M., et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78–82. doi: 10.1126/science.aaf2403. PubMed DOI
Marx V. Method of the year: Spatially resolved transcriptomics. Nat. Methods. 2021;18:9–14. doi: 10.1038/s41592-020-01033-y. PubMed DOI
Giacomello S., Salmén F., Terebieniec B.K., Vickovic S., Navarro J.F., Alexeyenko A., Reimegård J., McKee L.S., Mannapperuma C., Bulone V., et al. Spatially resolved transcriptome profiling in model plant species. Nat. Plants. 2017;3:1–11. doi: 10.1038/nplants.2017.61. PubMed DOI
Giacomello S., Lundeberg J. Preparation of plant tissue to enable spatial transcriptomics profiling using barcoded microarrays. Nat. Protoc. 2018;13:2425–2446. doi: 10.1038/s41596-018-0046-1. PubMed DOI
Birnbaum K., Shasha D.E., Wang J.Y., Jung J.W., Lambert G.M., Galbraith D.W., Benfey P.N. A gene expression map of the Arabidopsis root. Science. 2003;302:1956–1960. doi: 10.1126/science.1090022. PubMed DOI
Birnbaum K., Jung J.W., Wang J.Y., Lambert G.M., Hirst J.A., Galbraith D.W., Benfey P.N. Cell type–specific expression profiling in plants via cell sorting of protoplasts from fluorescent reporter lines. Nat. Methods. 2005;2:615–619. doi: 10.1038/nmeth0805-615. PubMed DOI
Brady S.M., Orlando D.A., Lee J.-Y., Wang J.Y., Koch J., Dinneny J.R., Mace D., Ohler U., Benfey P.N. A high-resolution root spatiotemporal map reveals dominant expression patterns. Science. 2007;318:801–806. doi: 10.1126/science.1146265. PubMed DOI
Zanetti M.E., Chang I.-F., Gong F., Galbraith D.W., Bailey-Serres J. Immunopurification of polyribosomal complexes of arabidopsis for global analysis of gene expression. Plant Physiol. 2005;138:624–635. doi: 10.1104/pp.105.059477. PubMed DOI PMC
Thellmann M., Andersen T.G., Vermeer J.E.M. Translating Ribosome Affinity Purification (TRAP) to investigate Arabidopsis thaliana root development at a cell type-specific scale. J. Vis. Exp. 2020;159:1–15. doi: 10.3791/60919. PubMed DOI
Zhang C., Barthelson R.A., Lambert G.M., Galbraith D.W. Global characterization of cell-specific gene expression through fluorescence-activated sorting of nuclei. Plant Physiol. 2008;147:30–40. doi: 10.1104/pp.107.115246. PubMed DOI PMC
Deal R.B., Henikoff S. A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell. 2010;18:1030–1040. doi: 10.1016/j.devcel.2010.05.013. PubMed DOI PMC
Deal R.B., Henikoff S. The INTACT method for cell type–specific gene expression and chromatin profiling in Arabidopsis thaliana. Nat. Protoc. 2011;6:56–68. doi: 10.1038/nprot.2010.175. PubMed DOI PMC
Palovaara J., Saiga S., Wendrich J.R., van‘t Wout Hofland N., van Schayck J.P., Hater F., Mutte S., Sjollema J., Boekschoten M., Hooiveld G.J., et al. Transcriptome dynamics revealed by a gene expression atlas of the early Arabidopsis embryo. Nat. Plants. 2017;3:894–904. doi: 10.1038/s41477-017-0035-3. PubMed DOI PMC
Bobrovskikh A., Doroshkov A., Mazzoleni S., Carteni F., Giannino F., Zubairova U. A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches: Benefits and Challenges for Data Analysis. Front Genet. 2021;12:652974. doi: 10.3389/fgene.2021.652974. PubMed DOI PMC
Thibivilliers S., Libault M. Plant single-cell multiomics: Cracking the molecular profiles of plant cells. Trends Plant Sci. 2021;26:662–663. doi: 10.1016/j.tplants.2021.03.001. PubMed DOI
Seyfferth C., Renema J., Wendrich J.R., Eekhout T., Seurinck R., Vandamme N., Blob B., Saeys Y., Helariutta Y., Birnbaum K.D., et al. Advances and opportunities of single-cell transcriptomics for plant research. Annu. Rev. Plant Biol. 2021;72:1–20. doi: 10.1146/annurev-arplant-081720-010120. PubMed DOI PMC
Brennecke P., Anders S., Kim J.K., Kołodziejczyk A.A., Zhang X., Proserpio V., Baying B., Benes V., Teichmann S.A., Marioni J.C., et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods. 2013;10:1093–1095. doi: 10.1038/nmeth.2645. PubMed DOI
Efroni I., Ip P.-L., Nawy T., Mello A., Birnbaum K.D. Quantification of cell identity from single-cell gene expression profiles. Genome Biol. 2015;16:1–12. doi: 10.1186/s13059-015-0580-x. PubMed DOI PMC
Efroni I., Mello A., Nawy T., Ip P.-L., Rahni R., DelRose N., Powers A., Satija R., Birnbaum K.D. Root regeneration triggers an embryo-like sequence guided by hormonal interactions. Cell. 2016;165:1721–1733. doi: 10.1016/j.cell.2016.04.046. PubMed DOI PMC
Macosko E.Z., Basu A., Satija R., Nemesh J., Shekhar K., Goldman M., Tirosh I., Bialas A.R., Kamitaki N., Martersteck E.M., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–1214. doi: 10.1016/j.cell.2015.05.002. PubMed DOI PMC
Wendrich J.R., Yang B., Vandamme N., Verstaen K., Smet W., Van de Velde C., Minne M., Wybouw B., Mor E., Arents H.E., et al. Vascular transcription factors guide plant epidermal responses to limiting phosphate conditions. Science. 2020;370:1–13. doi: 10.1126/science.aay4970. PubMed DOI PMC
Farmer A., Thibivilliers S., Ryu K.H., Schiefelbein J., Libault M. Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant. 2021;14:372–383. doi: 10.1016/j.molp.2021.01.001. PubMed DOI
Long Y., Liu Z., Jia J., Mo W., Fang L., Lu D., Liu B., Zhang H., Chen W., Zhai J. FlsnRNA-seq: Protoplasting-free full-length single-nucleus RNA profiling in plants. Genome Biol. 2021;22:1–14. doi: 10.1186/s13059-021-02288-0. PubMed DOI PMC
Dorrity M.W., Alexandre C.M., Hamm M., Vigil A.-L., Fields S., Queitsch C., Cuperus J. The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. bioRxiv. 2021 doi: 10.1101/2020.07.17.204792. PubMed DOI PMC
Rich-Griffin C., Stechemesser A., Finch J., Lucas E., Ott S., Schäfer P. Single-cell transcriptomics: A high-resolution avenue for plant functional genomics. Trends Plant Sci. 2020;25:186–197. doi: 10.1016/j.tplants.2019.10.008. PubMed DOI
Valihrach L., Androvic P., Kubista M. Platforms for single-cell collection and analysis. Int. J. Mol. Sci. 2018;19:807. doi: 10.3390/ijms19030807. PubMed DOI PMC
McGinnis C.S., Murrow L.M., Gartner Z.J. DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8:329–337. doi: 10.1016/j.cels.2019.03.003. PubMed DOI PMC
Wolock S.L., Lopez R., Klein A.M. Scrublet: Computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019;8:281–291. doi: 10.1016/j.cels.2018.11.005. PubMed DOI PMC
DePasquale E.A.K., Schnell D.J., Van Camp P.-J., Valiente-Alandí Í., Blaxall B.C., Grimes H.L., Singh H., Salomonis N. DoubletDecon: Deconvoluting doublets from single-cell RNA-sequencing data. Cell Rep. 2019;29:1718–1727. doi: 10.1016/j.celrep.2019.09.082. PubMed DOI PMC
DePasquale E.A.K., Schnell D., Chetal K., Salomonis N. Protocol for identification and removal of doublets with DoubletDecon. STAR Protoc. 2020;1:1–19. doi: 10.1016/j.xpro.2020.100085. PubMed DOI PMC
Zappia L., Phipson B., Oshlack A. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput. Biol. 2018;14:e1006245. doi: 10.1371/journal.pcbi.1006245. PubMed DOI PMC
Ma X., Denyer T., Timmermans M.C.P. PscB: A browser to explore plant single cell RNA-sequencing data sets. Plant Physiol. 2020;183:464–467. doi: 10.1104/pp.20.00250. PubMed DOI PMC
Baldrian P. The known and the unknown in soil microbial ecology. FEMS Microbiol. Ecol. 2019;95 doi: 10.1093/femsec/fiz005. PubMed DOI
Zifcakova L., Vetrovsky T., Lombard V., Henrissat B., Howe A., Baldrian P. Feed in summer, rest in winter: Microbial carbon utilization in forest topsoil. Microbiome. 2017;5:122. doi: 10.1186/s40168-017-0340-0. PubMed DOI PMC
Damon C., Lehembre F., Oger-Desfeux C., Luis P., Ranger J., Fraissinet-Tachet L., Marmeisse R. Metatranscriptomics reveals the diversity of genes expressed by eukaryotes in forest soils. PLoS ONE. 2012;7:e28967. doi: 10.1371/journal.pone.0028967. PubMed DOI PMC
Geisen S., Tveit A.T., Clark I.M., Richter A., Svenning M.M., Bonkowski M., Urich T. Metatranscriptomic census of active protists in soils. ISME J. 2015;9:2178–2190. doi: 10.1038/ismej.2015.30. PubMed DOI PMC
White R.A., 3rd, Bottos E.M., Chowdhury T.R., Zucker J.D., Brislawn C.J., Nicora C.D., Fansler S.J., Glaesemann K.R., Glass K., Jansson J.K. Moleculo Long-Read Sequencing Facilitates Assembly and Genomic Binning from Complex Soil Metagenomes. mSystems. 2016;1 doi: 10.1128/mSystems.00045-16. PubMed DOI PMC
Hayden H.L., Savin K.W., Wadeson J., Gupta V., Mele P.M. Comparative Metatranscriptomics of Wheat Rhizosphere Microbiomes in Disease Suppressive and Non-suppressive Soils for Rhizoctonia solani AG8. Front. Microbiol. 2018;9:859. doi: 10.3389/fmicb.2018.00859. PubMed DOI PMC
Marti J.M., Arias-Giraldo L.F., Diaz-Villanueva W., Arnau V., Rodriguez-Franco A., Garay C.P. Metatranscriptomic dynamics after Verticillium dahliae infection and root damage in Olea europaea. BMC Plant Biol. 2020;20:79. doi: 10.1186/s12870-019-2185-0. PubMed DOI PMC
Jo Y., Back C.G., Choi H., Cho W.K. Comparative Microbiome Study of Mummified Peach Fruits by Metagenomics and Metatranscriptomics. Plants. 2020;9:1052. doi: 10.3390/plants9081052. PubMed DOI PMC
Westermann A.J., Gorski S.A., Vogel J. Dual RNA-seq of pathogen and host. Nat. Rev. Microbiol. 2012;10:618–630. doi: 10.1038/nrmicro2852. PubMed DOI
Ettwiller L., Buswell J., Yigit E., Schildkraut I. A novel enrichment strategy reveals unprecedented number of novel transcription start sites at single base resolution in a model prokaryote and the gut microbiome. BMC Genom. 2016;17:199. doi: 10.1186/s12864-016-2539-z. PubMed DOI PMC
Sharma C.M., Hoffmann S., Darfeuille F., Reignier J., Findeiss S., Sittka A., Chabas S., Reiche K., Hackermuller J., Reinhardt R., et al. The primary transcriptome of the major human pathogen Helicobacter pylori. Nature. 2010;464:250–255. doi: 10.1038/nature08756. PubMed DOI
Gorshkov V., Gubaev R., Petrova O., Daminova A., Gogoleva N., Ageeva M., Parfirova O., Prokchorchik M., Nikolaichik Y., Gogolev Y. Transcriptome profiling helps to identify potential and true molecular switches of stealth to brute force behavior in Pectobacterium atrosepticum during systemic colonization of tobacco plants. Eur. J. Plant. Pathol. 2018;152:957–976. doi: 10.1007/s10658-018-1496-6. DOI
Crump B.C., Wojahn J.M., Tomas F., Mueller R.S. Metatranscriptomics and Amplicon Sequencing Reveal Mutualisms in Seagrass Microbiomes. Front. Microbiol. 2018;9:388. doi: 10.3389/fmicb.2018.00388. PubMed DOI PMC
Saminathan T., Garcia M., Ghimire B., Lopez C., Bodunrin A., Nimmakayala P., Abburi V.L., Levi A., Balagurusamy N., Reddy U.K. Metagenomic and Metatranscriptomic Analyses of Diverse Watermelon Cultivars Reveal the Role of Fruit Associated Microbiome in Carbohydrate Metabolism and Ripening of Mature Fruits. Front. Plant Sci. 2018;9:4. doi: 10.3389/fpls.2018.00004. PubMed DOI PMC
Gomez-Cabrero D., Abugessaisa I., Maier D., Teschendorff A., Merkenschlager M., Gisel A., Ballestar E., Bongcam-Rudloff E., Conesa A., Tegner J. Data integration in the era of omics: Current and future challenges. BMC Syst. Biol. 2014;8:I1. doi: 10.1186/1752-0509-8-S2-I1. PubMed DOI PMC
Subramanian I., Verma S., Kumar S., Jere A., Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform. Biol. Insights. 2020;14 doi: 10.1177/1177932219899051. PubMed DOI PMC
Kim T.Y., Kim H.U., Lee S.Y. Data integration and analysis of biological networks. Curr. Opin. Biotechnol. 2010;21:78–84. doi: 10.1016/j.copbio.2010.01.003. PubMed DOI
Fukushima A., Kanaya S., Nishida K. Integrated network analysis and effective tools in plant systems biology. Front. Plant Sci. 2014;5:598. doi: 10.3389/fpls.2014.00598. PubMed DOI PMC
Moreno-Risueno M.A., Busch W., Benfey P.N. Omics meet networks—Using systems approaches to infer regulatory networks in plants. Curr. Opin. Plant Biol. 2010;13:126–131. doi: 10.1016/j.pbi.2009.11.005. PubMed DOI PMC
Yu T., Bai Y. Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks. Curr. Metab. 2013;1:83–91. doi: 10.2174/2213235X11301010084. PubMed DOI PMC
Kim M., Tagkopoulos I. Data integration and predictive modeling methods for multi-omics datasets. Mol. Omics. 2018;14:8–25. doi: 10.1039/C7MO00051K. PubMed DOI
Plomion C., Bastien C., Bogeat-Triboulot M.-B., Bouffier L., Déjardin A., Duplessis S., Fady B., Heuertz M., Le Gac A.-L., Provost S., et al. Forest tree genomics: 10 achievements from the past 10 years and future prospects. Ann. For. Sci. 2016;73:77–103. doi: 10.1007/s13595-015-0488-3. DOI
Ballesta P., Maldonado C., Perez-Rodriguez P., Mora F. SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus. Plants. 2019;8:331. doi: 10.3390/plants8090331. PubMed DOI PMC
Tsai H.Y., Cericola F., Edriss V., Andersen J.R., Orabi J., Jensen J.D., Jahoor A., Janss L., Jensen J. Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data. PLoS ONE. 2020;15:e0232665. doi: 10.1371/journal.pone.0232665. PubMed DOI PMC
Fikere M., Barbulescu D.M., Malmberg M.M., Maharjan P., Salisbury P.A., Kant S., Panozzo J., Norton S., Spangenberg G.C., Cogan N.O.I., et al. Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.) Plants. 2020;9:719. doi: 10.3390/plants9060719. PubMed DOI PMC
Maldonado C., Mora-Poblete F., Contreras-Soto R.I., Ahmar S., Chen J.T., Junior A.T.D.T., Scapim C.A. Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network. Front. Plant Sci. 2020;11:593897. doi: 10.3389/fpls.2020.593897. PubMed DOI PMC
Haberer G., Kamal N., Bauer E., Gundlach H., Fischer I., Seidel M.A., Spannagl M., Marcon C., Ruban A., Urbany C., et al. European maize genomes highlight intraspecies variation in repeat and gene content. Nat. Genet. 2020;52:950–957. doi: 10.1038/s41588-020-0671-9. PubMed DOI PMC
Lopez-Cortes X.A., Matamala F., Maldonado C., Mora-Poblete F., Scapim C.A. A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize. Front. Genet. 2020;11:543459. doi: 10.3389/fgene.2020.543459. PubMed DOI PMC
Cappetta E., Andolfo G., Di Matteo A., Barone A., Frusciante L., Ercolano M.R. Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches. Plants. 2020;9:1236. doi: 10.3390/plants9091236. PubMed DOI PMC
Wang X., Gao L., Jiao C., Stravoravdis S., Hosmani P.S., Saha S., Zhang J., Mainiero S., Strickler S.R., Catala C., et al. Genome of Solanum pimpinellifolium provides insights into structural variants during tomato breeding. Nat. Commun. 2020;11:5817. doi: 10.1038/s41467-020-19682-0. PubMed DOI PMC
Sun C., Dong Z., Zhao L., Ren Y., Zhang N., Chen F. The Wheat 660K SNP array demonstrates great potential for marker-assisted selection in polyploid wheat. Plant Biotechnol. J. 2020;18:1354–1360. doi: 10.1111/pbi.13361. PubMed DOI PMC
Babu P., Baranwal D.K., Harikrishna, Pal D., Bharti H., Joshi P., Thiyagarajan B., Gaikwad K.B., Bhardwaj S.C., Singh G.P., et al. Application of Genomics Tools in Wheat Breeding to Attain Durable Rust Resistance. Front. Plant Sci. 2020;11:567147. doi: 10.3389/fpls.2020.567147. PubMed DOI PMC
Liu C., Song J., Wang Y., Huang X., Zhang F., Wang W., Xu J., Zhang Y., Yu H., Pang Y., et al. Rapid prediction of head rice yield and grain shape for genome-wide association study in indica rice. J. Cereal Sci. 2020;96 doi: 10.1016/j.jcs.2020.103091. DOI
Morales K.Y., Singh N., Perez F.A., Ignacio J.C., Thapa R., Arbelaez J.D., Tabien R.E., Famoso A., Wang D.R., Septiningsih E.M., et al. An improved 7K SNP array, the C7AIR, provides a wealth of validated SNP markers for rice breeding and genetics studies. PLoS ONE. 2020;15:e0232479. doi: 10.1371/journal.pone.0232479. PubMed DOI PMC
Maldonado C., Mora F., Scapim C.A., Coan M. Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: hapLA4. PLoS ONE. 2019;14:e0212925. doi: 10.1371/journal.pone.0212925. PubMed DOI PMC
Mora-Poblete F., Ballesta P., Lobos G.A., Molina-Montenegro M., Gleadow R., Ahmar S., Jimenez-Aspee F. Genome-wide association study of cyanogenic glycosides, proline, sugars, and pigments in Eucalyptus cladocalyx after 18 consecutive dry summers. Physiol. Plant. 2021 doi: 10.1111/ppl.13349. PubMed DOI
Allier A., Teyssedre S., Lehermeier C., Moreau L., Charcosset A. Optimized breeding strategies to harness genetic resources with different performance levels. BMC Genom. 2020;21:349. doi: 10.1186/s12864-020-6756-0. PubMed DOI PMC
Meuwissen T.H., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–1829. doi: 10.1093/genetics/157.4.1819. PubMed DOI PMC
Daetwyler H.D., Calus M.P., Pong-Wong R., de Los Campos G., Hickey J.M. Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics. 2013;193:347–365. doi: 10.1534/genetics.112.147983. PubMed DOI PMC
Supple M.A., Bragg J.G., Broadhurst L.M., Nicotra A.B., Byrne M., Andrew R.L., Widdup A., Aitken N.C., Borevitz J.O. Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change. eLife. 2018;7 doi: 10.7554/eLife.31835. PubMed DOI PMC
Tibshirani R. Regression Shrinkage and Selection via the Lasso. J. R. Stat. Soc. Ser. B. 1996;58:267–288. doi: 10.1111/j.2517-6161.1996.tb02080.x. DOI
Habier D., Fernando R.L., Kizilkaya K., Garrick D.J. Extension of the bayesian alphabet for genomic selection. BMC Bioinform. 2011;12:186. doi: 10.1186/1471-2105-12-186. PubMed DOI PMC
VanRaden P.M. Efficient methods to compute genomic predictions. J. Dairy Sci. 2008;91:4414–4423. doi: 10.3168/jds.2007-0980. PubMed DOI
Heslot N., Yang H.-P., Sorrells M.E., Jannik J.-L. Genomic Selection in Plant Breeding: A Comparison of Models. Crop Breed. Genet. 2012;52:146–160. doi: 10.2135/cropsci2011.06.0297. DOI
Du Q., Lu W., Quan M., Xiao L., Song F., Li P., Zhou D., Xie J., Wang L., Zhang D. Genome-Wide Association Studies to Improve Wood Properties: Challenges and Prospects. Front. Plant Sci. 2018;9:1912. doi: 10.3389/fpls.2018.01912. PubMed DOI PMC
Gao H., Su G., Janss L., Zhang Y., Lund M.S. Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population. J. Dairy Sci. 2013;96:4678–4687. doi: 10.3168/jds.2012-6406. PubMed DOI
Wu X., Lund M.S., Sun D., Zhang Q., Su G. Impact of relationships between test and training animals and among training animals on reliability of genomic prediction. J. Anim. Breed. Genet. 2015;132:366–375. doi: 10.1111/jbg.12165. PubMed DOI
Rutkoski J., Poland J., Mondal S., Autrique E., Perez L.G., Crossa J., Reynolds M., Singh R. Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. G3 Genes Genomes Genet. 2016;6:2799–2808. doi: 10.1534/g3.116.032888. PubMed DOI PMC
Crain J., Mondal S., Rutkoski J., Singh R.P., Poland J. Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding. Plant Genome. 2018;11 doi: 10.3835/plantgenome2017.05.0043. PubMed DOI
Cabrera-Bosquet L., Crossa J., von Zitzewitz J., Serret M.D., Araus J.L. High-throughput phenotyping and genomic selection: The frontiers of crop breeding converge. J. Integr. Plant Biol. 2012;54:312–320. doi: 10.1111/j.1744-7909.2012.01116.x. PubMed DOI
Singh D., Wang X., Kumar U., Gao L., Noor M., Imtiaz M., Singh R.P., Poland J. High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat. Front. Plant Sci. 2019;10:394. doi: 10.3389/fpls.2019.00394. PubMed DOI PMC
Mackay I., Ober E., Hickey J. GplusE: Beyond genomic selection. Food Energy Secur. 2015;4:25–35. doi: 10.1002/fes3.52. PubMed DOI PMC
Sun J., Rutkoski J.E., Poland J.A., Crossa J., Jannink J.L., Sorrells M.E. Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield. Plant Genome. 2017;10 doi: 10.3835/plantgenome2016.11.0111. PubMed DOI
Leng P., Lubberstedt T., Xu M.L. Genomics-assisted breeding—A revolutionary strategy for crop improvement. J. Integr. Agric. 2017;16:2674–2685. doi: 10.1016/S2095-3119(17)61813-6. DOI
Feng H., Guo Z., Yang W., Huang C., Chen G., Fang W., Xiong X., Zhang H., Wang G., Xiong L., et al. An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice. Sci. Rep. 2017;7:4401. doi: 10.1038/s41598-017-04668-8. PubMed DOI PMC
Rincent R., Charpentier J.P., Faivre-Rampant P., Paux E., Le Gouis J., Bastien C., Segura V. Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar. G3 Genes Genomes Genet. 2018;8:3961–3972. doi: 10.1534/g3.118.200760. PubMed DOI PMC
Krause M.R., Gonzalez-Perez L., Crossa J., Perez-Rodriguez P., Montesinos-Lopez O., Singh R.P., Dreisigacker S., Poland J., Rutkoski J., Sorrells M., et al. Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat. G3 Genes Genomes Genet. 2019;9:1231–1247. doi: 10.1534/g3.118.200856. PubMed DOI PMC
Iwasaki M., Paszkowski J. Epigenetic memory in plants. EMBO J. 2014;33:1987–1998. doi: 10.15252/embj.201488883. PubMed DOI PMC
Roudier F., Ahmed I., Berard C., Sarazin A., Mary-Huard T., Cortijo S., Bouyer D., Caillieux E., Duvernois-Berthet E., Al-Shikhley L., et al. Integrative epigenomic mapping defines four main chromatin states in Arabidopsis. EMBO J. 2011;30:1928–1938. doi: 10.1038/emboj.2011.103. PubMed DOI PMC
Rigal M., Mathieu O. A “mille-feuille” of silencing: Epigenetic control of transposable elements. Biochim. Biophys. Acta. 2011;1809:452–458. doi: 10.1016/j.bbagrm.2011.04.001. PubMed DOI
Haag J.R., Pikaard C.S. Multisubunit RNA polymerases IV and V: Purveyors of non-coding RNA for plant gene silencing. Nat. Rev. Mol. Cell Biol. 2011;12:483–492. doi: 10.1038/nrm3152. PubMed DOI
Law J.A., Jacobsen S.E. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat. Rev. Genet. 2010;11:204–220. doi: 10.1038/nrg2719. PubMed DOI PMC
Stroud H., Greenberg M.V., Feng S., Bernatavichute Y.V., Jacobsen S.E. Comprehensive analysis of silencing mutants reveals complex regulation of the Arabidopsis methylome. Cell. 2013;152:352–364. doi: 10.1016/j.cell.2012.10.054. PubMed DOI PMC
Stroud H., Do T., Du J., Zhong X., Feng S., Johnson L., Patel D.J., Jacobsen S.E. Non-CG methylation patterns shape the epigenetic landscape in Arabidopsis. Nat. Struct. Mol. Biol. 2014;21:64–72. doi: 10.1038/nsmb.2735. PubMed DOI PMC
Ebbs M.L., Bender J. Locus-specific control of DNA methylation by the Arabidopsis SUVH5 histone methyltransferase. Plant Cell. 2006;18:1166–1176. doi: 10.1105/tpc.106.041400. PubMed DOI PMC
Kakutani T., Jeddeloh J.A., Richards E.J. Characterization of an Arabidopsis thaliana DNA hypomethylation mutant. Nucleic Acids Res. 1995;23:130–137. doi: 10.1093/nar/23.1.130. PubMed DOI PMC
Zemach A., Kim M.Y., Hsieh P.H., Coleman-Derr D., Eshed-Williams L., Thao K., Harmer S.L., Zilberman D. The Arabidopsis nucleosome remodeler DDM1 allows DNA methyltransferases to access H1-containing heterochromatin. Cell. 2013;153:193–205. doi: 10.1016/j.cell.2013.02.033. PubMed DOI PMC
Khan A., Yadav N.S., Morgenstern Y., Zemach A., Grafi G. Activation of Tag1 transposable elements in Arabidopsis dedifferentiating cells and their regulation by CHROMOMETHYLASE 3-mediated CHG methylation. Biochim. Biophys. Acta. 2016;1859:1289–1298. doi: 10.1016/j.bbagrm.2016.07.012. PubMed DOI
Yadav N.S., Khadka J., Domb K., Zemach A., Grafi G. CMT3 and SUVH4/KYP silence the exonic Evelknievel retroelement to allow for reconstitution of CMT1 mRNA. Epigenetics Chromatin. 2018;11:69. doi: 10.1186/s13072-018-0240-y. PubMed DOI PMC
Gehring M., Henikoff S. DNA methylation and demethylation in Arabidopsis. Arab. Book. 2008;6:e0102. doi: 10.1199/tab.0102. PubMed DOI PMC
Li B., Carey M., Workman J.L. The role of chromatin during transcription. Cell. 2007;128:707–719. doi: 10.1016/j.cell.2007.01.015. PubMed DOI
Xiao J., Lee U.S., Wagner D. Tug of war: Adding and removing histone lysine methylation in Arabidopsis. Curr. Opin. Plant Biol. 2016;34:41–53. doi: 10.1016/j.pbi.2016.08.002. PubMed DOI
Liu C., Lu F., Cui X., Cao X. Histone methylation in higher plants. Annu. Rev. Plant Biol. 2010;61:395–420. doi: 10.1146/annurev.arplant.043008.091939. PubMed DOI
Cao R., Wang L., Wang H., Xia L., Erdjument-Bromage H., Tempst P., Jones R.S., Zhang Y. Role of histone H3 lysine 27 methylation in Polycomb-group silencing. Science. 2002;298:1039–1043. doi: 10.1126/science.1076997. PubMed DOI
Jackson J.P., Johnson L., Jasencakova Z., Zhang X., PerezBurgos L., Singh P.B., Cheng X., Schubert I., Jenuwein T., Jacobsen S.E. Dimethylation of histone H3 lysine 9 is a critical mark for DNA methylation and gene silencing in Arabidopsis thaliana. Chromosoma. 2004;112:308–315. doi: 10.1007/s00412-004-0275-7. PubMed DOI
Zhang C., Du X., Tang K., Yang Z., Pan L., Zhu P., Luo J., Jiang Y., Zhang H., Wan H., et al. Arabidopsis AGDP1 links H3K9me2 to DNA methylation in heterochromatin. Nat. Commun. 2018;9:4547. doi: 10.1038/s41467-018-06965-w. PubMed DOI PMC
Kim D.H., Sung S. Polycomb-mediated gene silencing in Arabidopsis thaliana. Mol. Cells. 2014;37:841–850. doi: 10.14348/molcells.2014.0249. PubMed DOI PMC
Chen X., Hu Y., Zhou D.X. Epigenetic gene regulation by plant Jumonji group of histone demethylase. Biochim. Biophys. Acta. 2011;1809:421–426. doi: 10.1016/j.bbagrm.2011.03.004. PubMed DOI
Yadav N.S., Titov V., Ayemere I., Byeon B., Ilnytskyy Y., Kovalchuk I. Multigenerational exposure to heat stress induces phenotypic resilience, and genetic and epigenetic variations in Arabidopsis thaliana offspring. bioRxiv. 2020 doi: 10.1101/2020.11.30.405365. PubMed DOI PMC
Wong M.M., Chong G.L., Verslues P.E. Epigenetics and RNA Processing: Connections to Drought, Salt, and ABA? Methods Mol. Biol. 2017;1631:3–21. doi: 10.1007/978-1-4939-7136-7_1. PubMed DOI
Wang G., Li H., Meng S., Yang J., Ye N., Zhang J. Analysis of Global Methylome and Gene Expression during Carbon Reserve Mobilization in Stems under Soil Drying. Plant Physiol. 2020;183:1809–1824. doi: 10.1104/pp.20.00141. PubMed DOI PMC
Al-Harrasi I., Al-Yahyai R., Yaish M.W. Differential DNA methylation and transcription profiles in date palm roots exposed to salinity. PLoS ONE. 2018;13:e0191492. doi: 10.1371/journal.pone.0191492. PubMed DOI PMC
Yaish M.W., Al-Lawati A., Al-Harrasi I., Patankar H.V. Genome-wide DNA Methylation analysis in response to salinity in the model plant caliph medic (Medicago truncatula) BMC Genom. 2018;19:78. doi: 10.1186/s12864-018-4484-5. PubMed DOI PMC
Ma Y., Min L., Wang M., Wang C., Zhao Y., Li Y., Fang Q., Wu Y., Xie S., Ding Y., et al. Disrupted Genome Methylation in Response to High Temperature Has Distinct Affects on Microspore Abortion and Anther Indehiscence. Plant Cell. 2018;30:1387–1403. doi: 10.1105/tpc.18.00074. PubMed DOI PMC
Hossain M.S., Kawakatsu T., Kim K.D., Zhang N., Nguyen C.T., Khan S.M., Batek J.M., Joshi T., Schmutz J., Grimwood J., et al. Divergent cytosine DNA methylation patterns in single-cell, soybean root hairs. New Phytol. 2017;214:808–819. doi: 10.1111/nph.14421. PubMed DOI
Li J., Huang Q., Sun M., Zhang T., Li H., Chen B., Xu K., Gao G., Li F., Yan G., et al. Global DNA methylation variations after short-term heat shock treatment in cultured microspores of Brassica napus cv. Topas. Sci. Rep. 2016;6:38401. doi: 10.1038/srep38401. PubMed DOI PMC
Gao G., Li J., Li H., Li F., Xu K., Yan G., Chen B., Qiao J., Wu X. Comparison of the heat stress induced variations in DNA methylation between heat-tolerant and heat-sensitive rapeseed seedlings. Breed. Sci. 2014;64:125–133. doi: 10.1270/jsbbs.64.125. PubMed DOI PMC
Villagomez-Aranda A.L., Garcia-Ortega L.F., Torres-Pacheco I., Guevara-Gonzalez R.G. Whole-Genome DNA Methylation Analysis in Hydrogen Peroxide Overproducing Transgenic Tobacco Resistant to Biotic and Abiotic Stresses. Plants. 2021;10:178. doi: 10.3390/plants10010178. PubMed DOI PMC
Xu J., Zhou S., Gong X., Song Y., van Nocker S., Ma F., Guan Q. Single-base methylome analysis reveals dynamic epigenomic differences associated with water deficit in apple. Plant. Biotechnol. J. 2018;16:672–687. doi: 10.1111/pbi.12820. PubMed DOI PMC
Rajkumar M.S., Shankar R., Garg R., Jain M. Bisulphite sequencing reveals dynamic DNA methylation under desiccation and salinity stresses in rice cultivars. Genomics. 2020;112:3537–3548. doi: 10.1016/j.ygeno.2020.04.005. PubMed DOI
Li R., Hu F., Li B., Zhang Y., Chen M., Fan T., Wang T. Whole genome bisulfite sequencing methylome analysis of mulberry (Morus alba) reveals epigenome modifications in response to drought stress. Sci. Rep. 2020;10:8013. doi: 10.1038/s41598-020-64975-5. PubMed DOI PMC
Qian Y., Hu W., Liao J., Zhang J., Ren Q. The Dynamics of DNA methylation in the maize (Zea mays L.) inbred line B73 response to heat stress at the seedling stage. Biochem. Biophys. Res. Commun. 2019;512:742–749. doi: 10.1016/j.bbrc.2019.03.150. PubMed DOI
Sun L., Miao X., Cui J., Deng J., Wang X., Wang Y., Zhang Y., Gao S., Yang K. Genome-wide high-resolution mapping of DNA methylation identifies epigenetic variation across different salt stress in Maize (Zea mays L.) Euphytica. 2018;214 doi: 10.1007/s10681-017-2076-0. DOI
An Y.C., Goettel W., Han Q., Bartels A., Liu Z., Xiao W. Dynamic Changes of Genome-Wide DNA Methylation during Soybean Seed Development. Sci. Rep. 2017;7:12263. doi: 10.1038/s41598-017-12510-4. PubMed DOI PMC
Atighi M.R., Verstraeten B., De Meyer T., Kyndt T. Genome-wide DNA hypomethylation shapes nematode pattern-triggered immunity in plants. New Phytol. 2020;227:545–558. doi: 10.1111/nph.16532. PubMed DOI PMC
Lamke J., Baurle I. Epigenetic and chromatin-based mechanisms in environmental stress adaptation and stress memory in plants. Genome Biol. 2017;18:124. doi: 10.1186/s13059-017-1263-6. PubMed DOI PMC
Boyko A., Kovalchuk I. Transgenerational response to stress in Arabidopsis thaliana. Plant Signal. Behav. 2010;5:995–998. doi: 10.4161/psb.5.8.12227. PubMed DOI PMC
Suter L., Widmer A. Phenotypic effects of salt and heat stress over three generations in Arabidopsis thaliana. PLoS ONE. 2013;8:e80819. doi: 10.1371/journal.pone.0080819. PubMed DOI PMC
Ramirez-Carrasco G., Martinez-Aguilar K., Alvarez-Venegas R. Transgenerational Defense Priming for Crop Protection against Plant Pathogens: A Hypothesis. Front. Plant Sci. 2017;8:696. doi: 10.3389/fpls.2017.00696. PubMed DOI PMC
Wibowo A., Becker C., Marconi G., Durr J., Price J., Hagmann J., Papareddy R., Putra H., Kageyama J., Becker J., et al. Hyperosmotic stress memory in Arabidopsis is mediated by distinct epigenetically labile sites in the genome and is restricted in the male germline by DNA glycosylase activity. eLife. 2016;5 doi: 10.7554/eLife.13546. PubMed DOI PMC
Zheng X., Chen L., Xia H., Wei H., Lou Q., Li M., Li T., Luo L. Transgenerational epimutations induced by multi-generation drought imposition mediate rice plant’s adaptation to drought condition. Sci. Rep. 2017;7:39843. doi: 10.1038/srep39843. PubMed DOI PMC
Ou X., Zhang Y., Xu C., Lin X., Zang Q., Zhuang T., Jiang L., von Wettstein D., Liu B. Transgenerational inheritance of modified DNA methylation patterns and enhanced tolerance induced by heavy metal stress in rice (Oryza sativa L.) PLoS ONE. 2012;7:e41143. doi: 10.1371/journal.pone.0041143. PubMed DOI PMC
Kim J.M., Sasaki T., Ueda M., Sako K., Seki M. Chromatin changes in response to drought, salinity, heat, and cold stresses in plants. Front. Plant Sci. 2015;6:114. doi: 10.3389/fpls.2015.00114. PubMed DOI PMC
van Dijk K., Ding Y., Malkaram S., Riethoven J.J., Liu R., Yang J., Laczko P., Chen H., Xia Y., Ladunga I., et al. Dynamic changes in genome-wide histone H3 lysine 4 methylation patterns in response to dehydration stress in Arabidopsis thaliana. BMC Plant Biol. 2010;10:238. doi: 10.1186/1471-2229-10-238. PubMed DOI PMC
Yan L., Fan G., Li X. Genome-wide analysis of three histone marks and gene expression in Paulownia fortunei with phytoplasma infection. BMC Genom. 2019;20 doi: 10.1186/s12864-019-5609-1. PubMed DOI PMC
Yan L., Zhai X., Zhao Z., Fan G. Whole-genome landscape of H3K4me3, H3K36me3 and H3K9ac and their association with gene expression during Paulownia witches’ broom disease infection and recovery processes. 3 Biotech. 2020;10:336. doi: 10.1007/s13205-020-02331-0. PubMed DOI PMC
Hussey S.G., Loots M.T., van der Merwe K., Mizrachi E., Myburg A.A. Integrated analysis and transcript abundance modelling of H3K4me3 and H3K27me3 in developing secondary xylem. Sci. Rep. 2017;7:3370. doi: 10.1038/s41598-017-03665-1. PubMed DOI PMC
Zeng Z., Zhang W., Marand A.P., Zhu B., Buell C.R., Jiang J. Cold stress induces enhanced chromatin accessibility and bivalent histone modifications H3K4me3 and H3K27me3 of active genes in potato. Genome Biol. 2019;20:123. doi: 10.1186/s13059-019-1731-2. PubMed DOI PMC
Zhang Y., Liang Y., Dong Y., Gao Y., Yang X., Yuan J., Qiu D. The Magnaporthe oryzae Alt A 1-like protein MoHrip1 binds to the plant plasma membrane. Biochem. Biophys. Res. Commun. 2017;492:55–60. doi: 10.1016/j.bbrc.2017.08.039. PubMed DOI
Li Z., Jiang G., Liu X., Ding X., Zhang D., Wang X., Zhou Y., Yan H., Li T., Wu K., et al. Histone demethylase SlJMJ6 promotes fruit ripening by removing H3K27 methylation of ripening-related genes in tomato. New Phytol. 2020;227:1138–1156. doi: 10.1111/nph.16590. PubMed DOI
Liu B., Wendel J.F. Epigenetic phenomena and the evolution of plant allopolyploids. Mol. Phylogenet Evol. 2003;29:365–379. doi: 10.1016/S1055-7903(03)00213-6. PubMed DOI
Zhang Y.Y., Fischer M., Colot V., Bossdorf O. Epigenetic variation creates potential for evolution of plant phenotypic plasticity. New Phytol. 2013;197:314–322. doi: 10.1111/nph.12010. PubMed DOI
Varotto S., Tani E., Abraham E., Krugman T., Kapazoglou A., Melzer R., Radanovic A., Miladinovic D. Epigenetics: Possible applications in climate-smart crop breeding. J. Exp. Bot. 2020;71:5223–5236. doi: 10.1093/jxb/eraa188. PubMed DOI PMC
Yang X., Kundariya H., Xu Y.Z., Sandhu A., Yu J., Hutton S.F., Zhang M., Mackenzie S.A. MutS HOMOLOG1-derived epigenetic breeding potential in tomato. Plant Physiol. 2015;168:222–232. doi: 10.1104/pp.15.00075. PubMed DOI PMC
Raju S.K.K., Shao M.R., Sanchez R., Xu Y.Z., Sandhu A., Graef G., Mackenzie S. An epigenetic breeding system in soybean for increased yield and stability. Plant Biotechnol. J. 2018;16:1836–1847. doi: 10.1111/pbi.12919. PubMed DOI PMC
Hauben M., Haesendonckx B., Standaert E., Van Der Kelen K., Azmi A., Akpo H., Van Breusegem F., Guisez Y., Bots M., Lambert B., et al. Energy use efficiency is characterized by an epigenetic component that can be directed through artificial selection to increase yield. Proc. Natl. Acad. Sci. USA. 2009;106:20109–20114. doi: 10.1073/pnas.0908755106. PubMed DOI PMC
Greaves I.K., Groszmann M., Wang A., Peacock W.J., Dennis E.S. Inheritance of Trans Chromosomal Methylation patterns from Arabidopsis F1 hybrids. Proc. Natl. Acad. Sci. USA. 2014;111:2017–2022. doi: 10.1073/pnas.1323656111. PubMed DOI PMC
Wang L., Greaves I.K., Groszmann M., Wu L.M., Dennis E.S., Peacock W.J. Hybrid mimics and hybrid vigor in Arabidopsis. Proc. Natl. Acad. Sci. USA. 2015;112:E4959–E4967. doi: 10.1073/pnas.1514190112. PubMed DOI PMC
Jonas E., de Koning D.J. Does genomic selection have a future in plant breeding? Trends Biotechnol. 2013;31:497–504. doi: 10.1016/j.tibtech.2013.06.003. PubMed DOI
Oakey H., Cullis B., Thompson R., Comadran J., Halpin C., Waugh R. Genomic Selection in Multi-environment Crop Trials. G3 Genes Genomes Genet. 2016;6:1313–1326. doi: 10.1534/g3.116.027524. PubMed DOI PMC
Johannes F., Porcher E., Teixeira F.K., Saliba-Colombani V., Simon M., Agier N., Bulski A., Albuisson J., Heredia F., Audigier P., et al. Assessing the impact of transgenerational epigenetic variation on complex traits. PLoS Genet. 2009;5:e1000530. doi: 10.1371/journal.pgen.1000530. PubMed DOI PMC
Reinders J., Wulff B.B., Mirouze M., Mari-Ordonez A., Dapp M., Rozhon W., Bucher E., Theiler G., Paszkowski J. Compromised stability of DNA methylation and transposon immobilization in mosaic Arabidopsis epigenomes. Genes Dev. 2009;23:939–950. doi: 10.1101/gad.524609. PubMed DOI PMC
Roux F., Colome-Tatche M., Edelist C., Wardenaar R., Guerche P., Hospital F., Colot V., Jansen R.C., Johannes F. Genome-wide epigenetic perturbation jump-starts patterns of heritable variation found in nature. Genetics. 2011;188:1015–1017. doi: 10.1534/genetics.111.128744. PubMed DOI PMC
Cortijo S., Wardenaar R., Colome-Tatche M., Gilly A., Etcheverry M., Labadie K., Caillieux E., Hospital F., Aury J.M., Wincker P., et al. Mapping the epigenetic basis of complex traits. Science. 2014;343:1145–1148. doi: 10.1126/science.1248127. PubMed DOI
Bond D.M., Baulcombe D.C. Small RNAs and heritable epigenetic variation in plants. Trends Cell Biol. 2014;24:100–107. doi: 10.1016/j.tcb.2013.08.001. PubMed DOI
Latzel V., Allan E., Bortolini Silveira A., Colot V., Fischer M., Bossdorf O. Epigenetic diversity increases the productivity and stability of plant populations. Nat. Commun. 2013;4:2875. doi: 10.1038/ncomms3875. PubMed DOI
Abdelnoor R.V., Yule R., Elo A., Christensen A.C., Meyer-Gauen G., Mackenzie S.A. Substoichiometric shifting in the plant mitochondrial genome is influenced by a gene homologous to MutS. Proc. Natl. Acad. Sci. USA. 2003;100:5968–5973. doi: 10.1073/pnas.1037651100. PubMed DOI PMC
Xu Y.Z., Arrieta-Montiel M.P., Virdi K.S., de Paula W.B., Widhalm J.R., Basset G.J., Davila J.I., Elthon T.E., Elowsky C.G., Sato S.J., et al. MutS HOMOLOG1 is a nucleoid protein that alters mitochondrial and plastid properties and plant response to high light. Plant Cell. 2011;23:3428–3441. doi: 10.1105/tpc.111.089136. PubMed DOI PMC
Shedge V., Davila J., Arrieta-Montiel M.P., Mohammed S., Mackenzie S.A. Extensive rearrangement of the Arabidopsis mitochondrial genome elicits cellular conditions for thermotolerance. Plant Physiol. 2010;152:1960–1970. doi: 10.1104/pp.109.152827. PubMed DOI PMC
Xu Y.Z., Rde L.S., Virdi K.S., Arrieta-Montiel M.P., Razvi F., Li S., Ren G., Yu B., Alexander D., Guo L., et al. The chloroplast triggers developmental reprogramming when mutS HOMOLOG1 is suppressed in plants. Plant Physiol. 2012;159:710–720. doi: 10.1104/pp.112.196055. PubMed DOI PMC
Virdi K.S., Wamboldt Y., Kundariya H., Laurie J.D., Keren I., Kumar K.R.S., Block A., Basset G., Luebker S., Elowsky C., et al. MSH1 Is a Plant Organellar DNA Binding and Thylakoid Protein under Precise Spatial Regulation to Alter Development. Mol. Plant. 2016;9:245–260. doi: 10.1016/j.molp.2015.10.011. PubMed DOI
Kalisz S., Kramer E.M. Variation and constraint in plant evolution and development. Heredity. 2008;100:171–177. doi: 10.1038/sj.hdy.6800939. PubMed DOI
Virdi K.S., Laurie J.D., Xu Y.Z., Yu J., Shao M.R., Sanchez R., Kundariya H., Wang D., Riethoven J.J., Wamboldt Y., et al. Arabidopsis MSH1 mutation alters the epigenome and produces heritable changes in plant growth. Nat. Commun. 2015;6:6386. doi: 10.1038/ncomms7386. PubMed DOI PMC
Shao M.R., Raju S.K.K., Laurie J.D., Sanchez R., Mackenzie S.A. Stress-responsive pathways and small RNA changes distinguish variable developmental phenotypes caused by MSH1 loss. BMC Plant Biol. 2017;17:47. doi: 10.1186/s12870-017-0996-4. PubMed DOI PMC
de la Rosa Santamaria R., Shao M.R., Wang G., Nino-Liu D.O., Kundariya H., Wamboldt Y., Dweikat I., Mackenzie S.A. MSH1-induced non-genetic variation provides a source of phenotypic diversity in Sorghum bicolor. PLoS ONE. 2014;9:e108407. doi: 10.1371/journal.pone.0108407. PubMed DOI PMC
Manning K., Tor M., Poole M., Hong Y., Thompson A.J., King G.J., Giovannoni J.J., Seymour G.B. A naturally occurring epigenetic mutation in a gene encoding an SBP-box transcription factor inhibits tomato fruit ripening. Nat. Genet. 2006;38:948–952. doi: 10.1038/ng1841. PubMed DOI
Bilichak A., Kovalchuk I. Transgenerational response to stress in plants and its application for breeding. J. Exp. Bot. 2016;67:2081–2092. doi: 10.1093/jxb/erw066. PubMed DOI
Johnson L.M., Du J., Hale C.J., Bischof S., Feng S., Chodavarapu R.K., Zhong X., Marson G., Pellegrini M., Segal D.J., et al. SRA- and SET-domain-containing proteins link RNA polymerase V occupancy to DNA methylation. Nature. 2014;507:124–128. doi: 10.1038/nature12931. PubMed DOI PMC
Gallego-Bartolome J., Gardiner J., Liu W., Papikian A., Ghoshal B., Kuo H.Y., Zhao J.M., Segal D.J., Jacobsen S.E. Targeted DNA demethylation of the Arabidopsis genome using the human TET1 catalytic domain. Proc. Natl. Acad. Sci. USA. 2018;115:E2125–E2134. doi: 10.1073/pnas.1716945115. PubMed DOI PMC
Vojta A., Dobrinic P., Tadic V., Bockor L., Korac P., Julg B., Klasic M., Zoldos V. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 2016;44:5615–5628. doi: 10.1093/nar/gkw159. PubMed DOI PMC
Xiong T., Meister G.E., Workman R.E., Kato N.C., Spellberg M.J., Turker F., Timp W., Ostermeier M., Novina C.D. Targeted DNA methylation in human cells using engineered dCas9-methyltransferases. Sci. Rep. 2017;7:6732. doi: 10.1038/s41598-017-06757-0. PubMed DOI PMC
McDonald J.I., Celik H., Rois L.E., Fishberger G., Fowler T., Rees R., Kramer A., Martens A., Edwards J.R., Challen G.A. Reprogrammable CRISPR/Cas9-based system for inducing site-specific DNA methylation. Biol. Open. 2016;5:866–874. doi: 10.1242/bio.019067. PubMed DOI PMC
Xu X., Tao Y., Gao X., Zhang L., Li X., Zou W., Ruan K., Wang F., Xu G.L., Hu R. A CRISPR-based approach for targeted DNA demethylation. Cell Discov. 2016;2:16009. doi: 10.1038/celldisc.2016.9. PubMed DOI PMC
Choudhury S.R., Cui Y., Lubecka K., Stefanska B., Irudayaraj J. CRISPR-dCas9 mediated TET1 targeting for selective DNA demethylation at BRCA1 promoter. Oncotarget. 2016;7:46545–46556. doi: 10.18632/oncotarget.10234. PubMed DOI PMC
Papikian A., Liu W., Gallego-Bartolome J., Jacobsen S.E. Site-specific manipulation of Arabidopsis loci using CRISPR-Cas9 SunTag systems. Nat. Commun. 2019;10:729. doi: 10.1038/s41467-019-08736-7. PubMed DOI PMC
Nunez J.K., Chen J., Pommier G.C., Cogan J.Z., Replogle J.M., Adriaens C., Ramadoss G.N., Shi Q., Hung K.L., Samelson A.J., et al. Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing. Cell. 2021;184:2503–2519. e2517. doi: 10.1016/j.cell.2021.03.025. PubMed DOI PMC
Hu J.H., Miller S.M., Geurts M.H., Tang W., Chen L., Sun N., Zeina C.M., Gao X., Rees H.A., Lin Z., et al. Evolved Cas9 variants with broad PAM compatibility and high DNA specificity. Nature. 2018;556:57–63. doi: 10.1038/nature26155. PubMed DOI PMC
Xu X., Qi L.S. A CRISPR-dCas Toolbox for Genetic Engineering and Synthetic Biology. J. Mol. Biol. 2019;431:34–47. doi: 10.1016/j.jmb.2018.06.037. PubMed DOI
Quenneville S., Turelli P., Bojkowska K., Raclot C., Offner S., Kapopoulou A., Trono D. The KRAB-ZFP/KAP1 system contributes to the early embryonic establishment of site-specific DNA methylation patterns maintained during development. Cell Rep. 2012;2:766–773. doi: 10.1016/j.celrep.2012.08.043. PubMed DOI PMC
Fernie A.R., Yan J. De Novo Domestication: An Alternative Route toward New Crops for the Future. Mol. Plant. 2019;12:615–631. doi: 10.1016/j.molp.2019.03.016. PubMed DOI
Zsogon A., Cermak T., Naves E.R., Notini M.M., Edel K.H., Weinl S., Freschi L., Voytas D.F., Kudla J., Peres L.E.P. De novo domestication of wild tomato using genome editing. Nat. Biotechnol. 2018 doi: 10.1038/nbt.4272. PubMed DOI
Hu X., Cui Y., Dong G., Feng A., Wang D., Zhao C., Zhang Y., Hu J., Zeng D., Guo L., et al. Using CRISPR-Cas9 to generate semi-dwarf rice lines in elite landraces. Sci. Rep. 2019;9:19096. doi: 10.1038/s41598-019-55757-9. PubMed DOI PMC
Lacchini E., Kiegle E., Castellani M., Adam H., Jouannic S., Gregis V., Kater M.M. CRISPR-mediated accelerated domestication of African rice landraces. PLoS ONE. 2020;15:e0229782. doi: 10.1371/journal.pone.0229782. PubMed DOI PMC
Okuzaki A., Ogawa T., Koizuka C., Kaneko K., Inaba M., Imamura J., Koizuka N. CRISPR/Cas9-mediated genome editing of the fatty acid desaturase 2 gene in Brassica napus. Plant Physiol. Biochem. 2018;131:63–69. doi: 10.1016/j.plaphy.2018.04.025. PubMed DOI
Cermak T., Baltes N.J., Cegan R., Zhang Y., Voytas D.F. High-frequency, precise modification of the tomato genome. Genome Biol. 2015;16:232. doi: 10.1186/s13059-015-0796-9. PubMed DOI PMC
Kim Y.G., Cha J., Chandrasegaran S. Hybrid restriction enzymes: Zinc finger fusions to Fok I cleavage domain. Proc. Natl. Acad. Sci. USA. 1996;93:1156–1160. doi: 10.1073/pnas.93.3.1156. PubMed DOI PMC
Boch J., Scholze H., Schornack S., Landgraf A., Hahn S., Kay S., Lahaye T., Nickstadt A., Bonas U. Breaking the code of DNA binding specificity of TAL-type III effectors. Science. 2009;326:1509–1512. doi: 10.1126/science.1178811. PubMed DOI
Jinek M., Chylinski K., Fonfara I., Hauer M., Doudna J.A., Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337:816–821. doi: 10.1126/science.1225829. PubMed DOI PMC
Sanchez-Leon S., Gil-Humanes J., Ozuna C.V., Gimenez M.J., Sousa C., Voytas D.F., Barro F. Low-gluten, nontransgenic wheat engineered with CRISPR/Cas9. Plant Biotechnol. J. 2018;16:902–910. doi: 10.1111/pbi.12837. PubMed DOI PMC
Cong L., Ran F.A., Cox D., Lin S., Barretto R., Habib N., Hsu P.D., Wu X., Jiang W., Marraffini L.A., et al. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339:819–823. doi: 10.1126/science.1231143. PubMed DOI PMC
Zetsche B., Gootenberg J.S., Abudayyeh O.O., Slaymaker I.M., Makarova K.S., Essletzbichler P., Volz S.E., Joung J., van der Oost J., Regev A., et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell. 2015;163:759–771. doi: 10.1016/j.cell.2015.09.038. PubMed DOI PMC
Ran F.A., Cong L., Yan W.X., Scott D.A., Gootenberg J.S., Kriz A.J., Zetsche B., Shalem O., Wu X., Makarova K.S., et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature. 2015;520:186–191. doi: 10.1038/nature14299. PubMed DOI PMC
Nishimasu H., Shi X., Ishiguro S., Gao L., Hirano S., Okazaki S., Noda T., Abudayyeh O.O., Gootenberg J.S., Mori H., et al. Engineered CRISPR-Cas9 nuclease with expanded targeting space. Science. 2018;361:1259–1262. doi: 10.1126/science.aas9129. PubMed DOI PMC
Gorbunova V., Levy A.A. Non-homologous DNA end joining in plant cells is associated with deletions and filler DNA insertions. Nucleic Acids Res. 1997;25:4650–4657. doi: 10.1093/nar/25.22.4650. PubMed DOI PMC
Lindbo J.A. A historical overview of RNAi in plants. Methods Mol. Biol. 2012;894:1–16. doi: 10.1007/978-1-61779-882-5_1. PubMed DOI
Budhagatapalli N., Rutten T., Gurushidze M., Kumlehn J., Hensel G. Targeted Modification of Gene Function Exploiting Homology-Directed Repair of TALEN-Mediated Double-Strand Breaks in Barley. G3 Genes Genomes Genet. 2015;5:1857–1863. doi: 10.1534/g3.115.018762. PubMed DOI PMC
Svitashev S., Young J.K., Schwartz C., Gao H., Falco S.C., Cigan A.M. Targeted Mutagenesis, Precise Gene Editing, and Site-Specific Gene Insertion in Maize Using Cas9 and Guide RNA. Plant Physiol. 2015;169:931–945. doi: 10.1104/pp.15.00793. PubMed DOI PMC
Najera V.A., Twyman R.M., Christou P., Zhu C. Applications of multiplex genome editing in higher plants. Curr. Opin. Biotechnol. 2019;59:93–102. doi: 10.1016/j.copbio.2019.02.015. PubMed DOI
Kannan B., Jung J.H., Moxley G.W., Lee S.M., Altpeter F. TALEN-mediated targeted mutagenesis of more than 100 COMT copies/alleles in highly polyploid sugarcane improves saccharification efficiency without compromising biomass yield. Plant. Biotechnol. J. 2018;16:856–866. doi: 10.1111/pbi.12833. PubMed DOI PMC
Stuttmann J., Barthel K., Martin P., Ordon J., Erickson J.L., Herr R., Ferik F., Kretschmer C., Berner T., Keilwagen J., et al. Highly efficient multiplex editing: One-shot generation of 8x Nicotiana benthamiana and 12x Arabidopsis mutants. Plant J. 2021;106:8–22. doi: 10.1111/tpj.15197. PubMed DOI
Wang Y., Cheng X., Shan Q., Zhang Y., Liu J., Gao C., Qiu J.L. Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat. Biotechnol. 2014;32:947–951. doi: 10.1038/nbt.2969. PubMed DOI
Liang Z., Zhang K., Chen K., Gao C. Targeted mutagenesis in Zea mays using TALENs and the CRISPR/Cas system. J. Genet. Genom. 2014;41:63–68. doi: 10.1016/j.jgg.2013.12.001. PubMed DOI
Ku H.K., Ha S.H. Improving Nutritional and Functional Quality by Genome Editing of Crops: Status and Perspectives. Front. Plant Sci. 2020;11:577313. doi: 10.3389/fpls.2020.577313. PubMed DOI PMC
Komor A.C., Kim Y.B., Packer M.S., Zuris J.A., Liu D.R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature. 2016;533:420–424. doi: 10.1038/nature17946. PubMed DOI PMC
Anzalone A.V., Randolph P.B., Davis J.R., Sousa A.A., Koblan L.W., Levy J.M., Chen P.J., Wilson C., Newby G.A., Raguram A., et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature. 2019;576:149–157. doi: 10.1038/s41586-019-1711-4. PubMed DOI PMC
Schmidt C., Fransz P., Ronspies M., Dreissig S., Fuchs J., Heckmann S., Houben A., Puchta H. Changing local recombination patterns in Arabidopsis by CRISPR/Cas mediated chromosome engineering. Nat. Commun. 2020;11:4418. doi: 10.1038/s41467-020-18277-z. PubMed DOI PMC
Kumlehn J., Pietralla J., Hensel G., Pacher M., Puchta H. The CRISPR/Cas revolution continues: From efficient gene editing for crop breeding to plant synthetic biology. J. Integr. Plant Biol. 2018;60:1127–1153. doi: 10.1111/jipb.12734. PubMed DOI
Schindele A., Dorn A., Puchta H. CRISPR/Cas brings plant biology and breeding into the fast lane. Curr. Opin. Biotechnol. 2020;61:7–14. doi: 10.1016/j.copbio.2019.08.006. PubMed DOI
Ganie S.A., Wani S.H., Henry R., Hensel G. Improving rice salt tolerance by precision breeding in a new era. Curr. Opin. Plant Biol. 2021;60:101996. doi: 10.1016/j.pbi.2020.101996. PubMed DOI
Chandrasekaran J., Brumin M., Wolf D., Leibman D., Klap C., Pearlsman M., Sherman A., Arazi T., Gal-On A. Development of broad virus resistance in non-transgenic cucumber using CRISPR/Cas9 technology. Mol. Plant Pathol. 2016;17:1140–1153. doi: 10.1111/mpp.12375. PubMed DOI PMC
Waltz E. Gene-edited CRISPR mushroom escapes US regulation. Nature. 2016;532:293. doi: 10.1038/nature.2016.19754. PubMed DOI
Osakabe Y., Liang Z., Ren C., Nishitani C., Osakabe K., Wada M., Komori S., Malnoy M., Velasco R., Poli M., et al. CRISPR-Cas9-mediated genome editing in apple and grapevine. Nat. Protoc. 2018;13:2844–2863. doi: 10.1038/s41596-018-0067-9. PubMed DOI
Schmidt S.M., Belisle M., Frommer W.B. The evolving landscape around genome editing in agriculture: Many countries have exempted or move to exempt forms of genome editing from GMO regulation of crop plants. EMBO Rep. 2020;21:e50680. doi: 10.15252/embr.202050680. PubMed DOI PMC
Evangelatos N., Upadya S.P., Venne J., Satyamoorthy K., Brand H., Ramashesha C.S., Brand A. Digital Transformation and Governance Innovation for Public Biobanks and Free/Libre Open Source Software Using a Blockchain Technology. OMICS. 2020;24:278–285. doi: 10.1089/omi.2019.0178. PubMed DOI
Liu J., Huang L., Wang C., Liu Y., Yan Z., Wang Z., Xiang L., Zhong X., Gong F., Zheng Y., et al. Genome-Wide Association Study Reveals Novel Genomic Regions Associated With High Grain Protein Content in Wheat Lines Derived From Wild Emmer Wheat. Front. Plant Sci. 2019;10:464. doi: 10.3389/fpls.2019.00464. PubMed DOI PMC
Galan R.J., Bernal-Vasquez A.M., Jebsen C., Piepho H.P., Thorwarth P., Steffan P., Gordillo A., Miedaner T. Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye. Theor. Appl. Genet. 2020;133:3001–3015. doi: 10.1007/s00122-020-03651-8. PubMed DOI PMC
Marzec M., Braszewska-Zalewska A., Hensel G. Prime Editing: A New Way for Genome Editing. Trends Cell Biol. 2020;30:257–259. doi: 10.1016/j.tcb.2020.01.004. PubMed DOI
Khatri S., Sharma S. How does organic farming shape the soil- and plant-associated microbiota? Symbiosis. 2021:1–8. doi: 10.1007/s13199-021-00767-3. PubMed DOI
Belimov A.A., Shaposhnikov A.I., Azarova T.S., Makarova N.M., Safronova V.I., Litvinskiy V.A., Nosikov V.V., Zavalin A.A., Tikhonovich I.A. Microbial Consortium of PGPR, Rhizobia and Arbuscular Mycorrhizal Fungus Makes Pea Mutant SGECd(t) Comparable with Indian Mustard in Cadmium Tolesrance and Accumulation. Plants. 2020;9:975. doi: 10.3390/plants9080975. PubMed DOI PMC
Reynolds M., Atkin O.K., Bennett M., Cooper M., Dodd I.C., Foulkes M.J., Frohberg C., Hammer G., Henderson I.R., Huang B., et al. Addressing Research Bottlenecks to Crop Productivity. Trends Plant Sci. 2021;26:607–630. doi: 10.1016/j.tplants.2021.03.011. PubMed DOI