1932

Abstract

The foliar microbiome can extend the host plant phenotype by expanding its genomic and metabolic capabilities. Despite increasing recognition of the importance of the foliar microbiome for plant fitness, stress physiology, and yield, the diversity, function, and contribution of foliar microbiomes to plant phenotypic traits remain largely elusive. The recent adoption of high-throughput technologies is helping to unravel the diversityand spatiotemporal dynamics of foliar microbiomes, but we have yet to resolve their functional importance for plant growth, development, and ecology. Here, we focus on the processes that govern the assembly of the foliar microbiome and the potential mechanisms involved in extended plant phenotypes. We highlight knowledge gaps and provide suggestions for new research directions that can propel the field forward. These efforts will be instrumental in maximizing the functional potential of the foliar microbiome for sustainable crop production.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-arplant-080620-114342
2021-06-17
2024-04-18
Loading full text...

Full text loading...

/deliver/fulltext/arplant/72/1/annurev-arplant-080620-114342.html?itemId=/content/journals/10.1146/annurev-arplant-080620-114342&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Abadi VAJM, Sepehri M, Rahmani HA, Zarei M, Ronaghi A et al. 2020. Role of dominant phyllosphere bacteria with plant growth–promoting characteristics on growth and nutrition of maize (Zea mays L.). J. Soil Sci. Plant Nutr. 20:2348–63
    [Google Scholar]
  2. 2. 
    Agtuca BJ, Stopka SA, Evans S, Samarah L, Liu Y et al. 2020. Metabolomic profiling of wild-type and mutant soybean root nodules using laser-ablation electrospray ionization mass spectrometry reveals altered metabolism. Plant J 103:1937–58
    [Google Scholar]
  3. 3. 
    Amante E, Salomone A, Alladio E, Vincenti M, Porpiglia F, Bro R. 2019. Untargeted metabolomic profile for the detection of prostate carcinoma—preliminary results from PARAFAC2 and PLS-DA models. Molecules 24:3063
    [Google Scholar]
  4. 4. 
    Ambrose KV, Belanger FC. 2012. SOLiD-SAGE of endophyte-infected red fescue reveals numerous effects on host transcriptome and an abundance of highly expressed fungal secreted proteins. PLOS ONE 7:e53214
    [Google Scholar]
  5. 5. 
    Azarbad H, Tremblay J, Giard-Laliberté C, Bainard LD, Yergeau E. 2020. Four decades of soil water stress history together with host genotype constrain the response of the wheat microbiome to soil moisture. FEMS Microbiol. Ecol. 96:fiaa098
    [Google Scholar]
  6. 6. 
    Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E et al. 2015. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528:364–69
    [Google Scholar]
  7. 7. 
    Beilsmith K, Thoen MPM, Brachi B, Gloss AD, Khan MH, Bergelson J. 2019. Genome-wide association studies on the phyllosphere microbiome: embracing complexity in host–microbe interactions. Plant J 97:164–81
    [Google Scholar]
  8. 8. 
    Bohnenkamp D, Kuska MT, Mahlein A-K, Behmann J. 2019. Hyperspectral signal decomposition and symptom detection of wheat rust disease at the leaf scale using pure fungal spore spectra as reference. Plant Pathol 68:1188–95
    [Google Scholar]
  9. 9. 
    Brachi B, Filiault D, Darme P, Mentec ML, Kerdaffrec E et al. 2017. Plant genes influence microbial hubs that shape beneficial leaf communities. bioRxiv 181198. https://doi.org/10.1101/181198
    [Crossref]
  10. 10. 
    Busby PE, Peay KG, Newcombe G. 2016. Common foliar fungi of Populus trichocarpa modify Melampsora rust disease severity. New Phytol 209:1681–92
    [Google Scholar]
  11. 11. 
    Busby PE, Ridout M, Newcombe G. 2016. Fungal endophytes: modifiers of plant disease. Plant Mol. Biol. 90:645–55
    [Google Scholar]
  12. 12. 
    Calderón R, Navas-Cortés JA, Lucena C, Zarco-Tejada PJ. 2013. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 139:231–45
    [Google Scholar]
  13. 13. 
    Caldwell D, Iyer-Pascuzzi AS. 2019. A scanning electron microscopy technique for viewing plant−microbe interactions at tissue and cell-type resolution. Phytopathology 109:1302–11
    [Google Scholar]
  14. 14. 
    Cardinale M. 2014. Scanning a microhabitat: plant-microbe interactions revealed by confocal laser microscopy. Front. Microbiol. 5:94
    [Google Scholar]
  15. 15. 
    Carrión O, Gibson L, Elias DMO, McNamara NP, van Alen TA et al. 2020. Diversity of isoprene-degrading bacteria in phyllosphere and soil communities from a high isoprene-emitting environment: a Malaysian oil palm plantation. Microbiome 8:81
    [Google Scholar]
  16. 16. 
    Chapelle E, Alunni B, Malfatti P, Solier L, Pédron J et al. 2015. A straightforward and reliable method for bacterial in planta transcriptomics: application to the Dickeya dadantii/Arabidopsis thaliana pathosystem. Plant J 82:352–62
    [Google Scholar]
  17. 17. 
    Chen T, Nomura K, Wang X, Sohrabi R, Xu J et al. 2020. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580:653–57
    [Google Scholar]
  18. 18. 
    Chen Y, Wang J, Yang N, Wen Z, Sun X et al. 2018. Wheat microbiome bacteria can reduce virulence of a plant pathogenic fungus by altering histone acetylation. Nat. Commun. 9:3429
    [Google Scholar]
  19. 19. 
    Coleman-Derr D, Desgarennes D, Fonseca-Garcia C, Gross S, Clingenpeel S et al. 2016. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol 209:798–811
    [Google Scholar]
  20. 20. 
    Colgan AM, Cameron ADS, Kröger C. 2017. If it transcribes, we can sequence it: mining the complexities of host–pathogen–environment interactions using RNA-seq. Curr. Opin. Microbiol. 36:37–46
    [Google Scholar]
  21. 21. 
    Connor EW, Sandy M, Hawkes CV. 2017. Microbial tools in agriculture require an ecological context: stress-dependent non-additive symbiont interactions. Agronomy J 109:917–26
    [Google Scholar]
  22. 22. 
    Cordovez V, Dini-Andreote F, Carrión VJ, Raaijmakers JM. 2019. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73:69–88
    [Google Scholar]
  23. 23. 
    Das Choudhury S, Samal A, Awada T 2019. Leveraging image analysis for high-throughput plant phenotyping. Front. Plant Sci. 10:508
    [Google Scholar]
  24. 24. 
    Dastogeer KMG, Li H, Sivasithamparam K, Jones MGK, Wylie SJ. 2018. Fungal endophytes and a virus confer drought tolerance to Nicotiana benthamiana plants through modulating osmolytes, antioxidant enzymes and expression of host drought responsive genes. Environ. Exp. Bot. 149:95–108
    [Google Scholar]
  25. 25. 
    Dawkins R. 1982. The Extended Phenotype: The Long Reach of the Gene Oxford, UK: Oxford Univ. Press
  26. 26. 
    De Vrieze M, Germanier F, Vuille N, Weisskopf L. 2018. Combining different potato-associated Pseudomonas strains for improved biocontrol of Phytophthora infestans. Front. Microbiol 9:2573
    [Google Scholar]
  27. 27. 
    Delmotte N, Knief C, Chaffron S, Innerebner G, Roschitzki B et al. 2009. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. PNAS 106:16428–33
    [Google Scholar]
  28. 28. 
    Ding T, Su B, Chen X, Xie S, Gu S et al. 2017. An endophytic bacterial strain isolated from Eucommia ulmoides inhibits southern corn leaf blight. Front. Microbiol. 8:903
    [Google Scholar]
  29. 29. 
    D'Jonsiles MF, Galizzi GE, Dolinko AE, Novas MV, Ceriani Nakamurakare E, Carmarán CC 2020. Optical study of laser biospeckle activity in leaves of Jatropha curcas L.: a non-invasive and indirect assessment of foliar endophyte colonization. Mycological Progress 19:339–49
    [Google Scholar]
  30. 30. 
    Erbilgin O, Rübel O, Louie KB, Trinh M, de Raad M et al. 2019. MAGI: a method for metabolite annotation and gene integration. ACS Chem. Biol. 14:704–14
    [Google Scholar]
  31. 31. 
    Farber C, Kurouski D. 2018. Detection and identification of plant pathogens on maize kernels with a hand-held Raman spectrometer. Anal. Chem. 90:3009–12
    [Google Scholar]
  32. 32. 
    Farber C, Mahnke M, Sanchez L, Kurouski D. 2019. Advanced spectroscopic techniques for plant disease diagnostics. A review. TrAC Trends Anal. Chem. 118:43–49
    [Google Scholar]
  33. 33. 
    Farré-Armengol G, Filella I, Llusia J, Peñuelas J. 2016. Bidirectional interaction between phyllospheric microbiotas and plant volatile emissions. Trends Plant Sci 21:854–60
    [Google Scholar]
  34. 34. 
    Finkel OM, Castrillo G, Herrera Paredes S, Salas González I, Dangl JL 2017. Understanding and exploiting plant beneficial microbes. Curr. Opin. Plant Biol. 38:155–63
    [Google Scholar]
  35. 35. 
    Fitzpatrick CR, Lu-Irving P, Copeland J, Guttman DS, Wang PW et al. 2018. Chloroplast sequence variation and the efficacy of peptide nucleic acids for blocking host amplification in plant microbiome studies. Microbiome 6:144
    [Google Scholar]
  36. 36. 
    Flemming H-C, Wuertz S. 2019. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17:247–60
    [Google Scholar]
  37. 37. 
    Giauque H, Connor EW, Hawkes CV. 2019. Endophyte traits relevant to stress tolerance, resource use and habitat of origin predict effects on host plants. New Phytol 221:2239–49
    [Google Scholar]
  38. 38. 
    Giauque H, Hawkes CV. 2013. Climate affects symbiotic fungal endophyte diversity and performance. Am. J. Bot. 100:1435–44
    [Google Scholar]
  39. 39. 
    Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D et al. 2014. Generative adversarial nets. NIPS 2014: Proceedings of the 17th International Conference on Neural Information Processing Systems, Vol. 2 Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger 2672–80 Cambridge, MA: MIT Press
    [Google Scholar]
  40. 40. 
    Grady KL, Sorensen JW, Stopnisek N, Guittar J, Shade A. 2019. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. Nat. Commun. 10:4135
    [Google Scholar]
  41. 41. 
    Großkinsky DK, Svensgaard J, Christensen S, Roitsch T. 2015. Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J. Exp. Bot. 66:5429–40
    [Google Scholar]
  42. 42. 
    Hassani MA, Durán P, Hacquard S. 2018. Microbial interactions within the plant holobiont. Microbiome 6:58
    [Google Scholar]
  43. 43. 
    Hassani MA, Özkurt E, Franzenburg S, Stukenbrock EH. 2020. Ecological assembly processes of the bacterial and fungal microbiota of wild and domesticated wheat species. Phytobiomes J 4:217–24Addressed the role of selection, drift, and dispersal in microbiome assembly of wild and domesticated wheat.
    [Google Scholar]
  44. 44. 
    Hawkes CV, Bull JJ, Lau JA. 2020. Symbiosis and stress: how plant microbiomes affect host evolution. Philos. Trans. R. Soc. B 375:20190590
    [Google Scholar]
  45. 45. 
    Hawkes CV, Connor EW. 2017. Translating phytobiomes from theory to practice: ecological and evolutionary considerations. Phytobiomes 1:57–69
    [Google Scholar]
  46. 46. 
    Heard E, Martienssen RA. 2014. Transgenerational epigenetic inheritance: myths and mechanisms. Cell 157:95–109
    [Google Scholar]
  47. 47. 
    Henry LP, Bruijning M, Forsberg SKG, Ayroles JF. 2019. Can the microbiome influence host evolutionary trajectories?. bioRxiv 700237. https://doi.org/10.1101/700237 Applied a quantitative genetics framework to identify microbiome contribution to host phenotype and response to selection.
    [Crossref]
  48. 48. 
    Herpell JB, Schindler F, Bejtović M, Fragner L, Diallo B et al. 2020. The potato yam phyllosphere ectosymbiont Paraburkholderia sp. Msb3 is a potent growth promotor in tomato. Front. Microbiol. 11:581
    [Google Scholar]
  49. 49. 
    Heslot N, Akdemir D, Sorrells ME, Jannink J-L. 2014. Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor. Appl. Genet 127:463–80Modeled genotype by environment interactions in wheat to predict yield from weather parameters.
    [Google Scholar]
  50. 50. 
    Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput 9:1735–80
    [Google Scholar]
  51. 51. 
    Horton MW, Bodenhausen N, Beilsmith K, Meng D, Muegge BD et al. 2014. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5:5320
    [Google Scholar]
  52. 52. 
    Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P et al. 2015. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81:7570–81
    [Google Scholar]
  53. 53. 
    Hunter P. 2018. The revival of the extended phenotype: After more than 30 years, Dawkins' Extended Phenotype hypothesis is enriching evolutionary biology and inspiring potential applications. EMBO Rep 19:e46477
    [Google Scholar]
  54. 54. 
    Ichida H, Matsuyama T, Abe T, Koba T 2007. DNA adenine methylation changes dramatically during establishment of symbiosis. FEBS J 274:951–62
    [Google Scholar]
  55. 55. 
    Indigo Ag 2018. Indigo Cotton™ demonstrates continuous improvement with significant yield gains in second commercial season Press Release, March 13. https://www.indigoag.com/pages/news/indigo-cotton-demonstrates-continuous-improvement#_ftnref1
  56. 56. 
    Ji B, Bever JD 2016. Plant preferential allocation and fungal reward decline with soil phosphorus: implications for mycorrhizal mutualism. Ecosphere 7:e01256
    [Google Scholar]
  57. 57. 
    Jia Y, Whalen JK. 2020. A new perspective on functional redundancy and phylogenetic niche conservatism in soil microbial communities. Pedosphere 30:18–24
    [Google Scholar]
  58. 58. 
    Kaddes A, Fauconnier M-L, Sassi K, Nasraoui B, Jijakli M-H. 2019. Endophytic fungal volatile compounds as solution for sustainable agriculture. Molecules 24:1065
    [Google Scholar]
  59. 59. 
    Kanchiswamy CN, Malnoy M, Maffei ME. 2015. Chemical diversity of microbial volatiles and their potential for plant growth and productivity. Front. Plant Sci. 6:151
    [Google Scholar]
  60. 60. 
    Kang DD, Li F, Kirton E, Thomas A, Egan R et al. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359
    [Google Scholar]
  61. 61. 
    Kempa S, Hummel J, Schwemmer T, Pietzke M, Strehmel N et al. 2009. An automated GCxGC-TOF-MS protocol for batch-wise extraction and alignment of mass isotopomer matrixes from differential 13C-labelling experiments: a case study for photoautotrophic-mixotrophic grown Chlamydomonas reinhardtii cells. J. Basic Microbiol. 49:82–91
    [Google Scholar]
  62. 62. 
    Kingma DP, Welling M. 2013. Auto-encoding variational bayes. arXiv:1312.6114 [stat.ML]
  63. 63. 
    Kjøller R, Rosendahl S. 1997. The presence of the arbuscular mycorrhizal fungus Glomus intraradices influences enzymatic activities of the root pathogen Aphanomyces euteiches in pea roots. Mycorrhiza 6:487–91
    [Google Scholar]
  64. 64. 
    Kleiner M, Dong X, Hinzke T, Wippler J, Thorson E et al. 2018. Metaproteomics method to determine carbon sources and assimilation pathways of species in microbial communities. PNAS 115:E5576–84
    [Google Scholar]
  65. 65. 
    Köhl J, Kolnaar R, Ravensberg WJ. 2019. Mode of action of microbial biological control agents against plant diseases: relevance beyond efficacy. Front. Plant Sci. 10:845
    [Google Scholar]
  66. 66. 
    Kosina SM, Greiner AM, Lau RK, Jenkins S, Baran R et al. 2018. Web of microbes (WoM): a curated microbial exometabolomics database for linking chemistry and microbes. BMC Microbiol 18:115
    [Google Scholar]
  67. 67. 
    Koskella B, Taylor TB. 2018. Multifaceted impacts of bacteriophages in the plant microbiome. Annu. Rev. Phytopathol. 56:361–80
    [Google Scholar]
  68. 68. 
    Kumar AS, Sridar R, Uthandi S. 2017. Mitigation of drought in rice by a phyllosphere bacterium Bacillus altitudinis FD48. Afr. J. Microbiol. Res. 11:1614–25
    [Google Scholar]
  69. 69. 
    Kyndt T, Quispe D, Zhai H, Jarret R, Ghislain M et al. 2015. The genome of cultivated sweet potato contains Agrobacterium T-DNAs with expressed genes: an example of a naturally transgenic food crop. PNAS 112:5844–49
    [Google Scholar]
  70. 70. 
    Lajoie G, Maglione R, Kembel SW. 2019. Drivers of phyllosphere microbial functional diversity in a neotropical forest. bioRxiv 851485. https://doi.org/10.1101/851485
    [Crossref]
  71. 71. 
    Lambais MR, Barrera SE, Santos EC, Crowley DE, Jumpponen A. 2017. Phyllosphere metaproteomes of trees from the Brazilian Atlantic forest show high levels of functional redundancy. Microb. Ecol. 73:123–34
    [Google Scholar]
  72. 72. 
    Larke-Mejía NL, Crombie AT, Pratscher J, McGenity TJ, Murrell JC. 2019. Novel isoprene-degrading Proteobacteria from soil and leaves identified by cultivation and metagenomics analysis of stable isotope probing experiments. Front. Microbiol. 10:2700
    [Google Scholar]
  73. 73. 
    Larran S, Simón MR, Moreno MV, Siurana MPS, Perelló A. 2016. Endophytes from wheat as biocontrol agents against tan spot disease. Biol. Control 92:17–23
    [Google Scholar]
  74. 74. 
    Leach JE, Triplett LR, Argueso CT, Trivedi P. 2017. Communication in the phytobiome. Cell 169:587–96
    [Google Scholar]
  75. 75. 
    LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
    [Google Scholar]
  76. 76. 
    Lee MR, Hawkes CV. 2021. Plant and soil drivers of whole-plant microbiomes: variation in switchgrass fungi from coastal to mountain sites. PhytobiomesIn press. https://doi.org/10.1094/PBIOMES-07-20-0056-FI
    [Crossref] [Google Scholar]
  77. 77. 
    Lemfack MC, Gohlke BO, Toguem SMT, Preissner S, Piechulla B, Preissner R. 2018. mVOC 2.0: a database of microbial volatiles. Nucleic Acids Res 46:D1261–65Introduced an online database of ∼2000 microbial volatiles from almost 1000 taxa.
    [Google Scholar]
  78. 78. 
    Leopold DR, Busby PE. 2020. Host genotype and colonist arrival order jointly govern plant microbiome composition and function. Curr. Biol. 30:3260–66.e5
    [Google Scholar]
  79. 79. 
    Leveau JH. 2019. A brief from the leaf: latest research to inform our understanding of the phyllosphere microbiome. Curr. Opin. Microbiol. 49:41–49
    [Google Scholar]
  80. 80. 
    Levy A, Conway JM, Dangl JL, Woyke T. 2018. Elucidating bacterial gene functions in the plant microbiome. Cell Host Microbe 24:475–85
    [Google Scholar]
  81. 81. 
    Liu H, Zhu H, Li Z, Yang G 2020. Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat. Int. J. Remote Sens. 41:858–81
    [Google Scholar]
  82. 82. 
    Liu L, Song B, Zhang S, Liu X. 2017. A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents. Remote Sens 9:1113
    [Google Scholar]
  83. 83. 
    Llorens E, Sharon O, Camañes G, García-Agustín P, Sharon A 2019. Endophytes from wild cereals protect wheat plants from drought by alteration of physiological responses of the plants to water stress. Environ. Microbiol. 21:3299–312
    [Google Scholar]
  84. 84. 
    Lloyd KG, Steen AD, Ladau J, Yin J, Crosby L. 2018. Phylogenetically novel uncultured microbial cells dominate Earth microbiomes. mSystems 3:e00055–18
    [Google Scholar]
  85. 85. 
    Lugtenberg BJJ, Caradus JR, Johnson LJ. 2016. Fungal endophytes for sustainable crop production. FEMS Microbiol. Ecol. 92:fiw194
    [Google Scholar]
  86. 86. 
    Lundberg DS, Yourstone S, Mieczkowski P, Jones CD, Dangl JL. 2013. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10:999–1002
    [Google Scholar]
  87. 87. 
    Ma J, Yu MK, Fong S, Ono K, Sage E et al. 2018. Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15:290–98
    [Google Scholar]
  88. 88. 
    Mahlein A-K, Kuska MT, Behmann J, Polder G, Walter A. 2018. Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annu. Rev. Phytopathol. 56:535–58
    [Google Scholar]
  89. 89. 
    Manching H, Carlson K, Kosowsky S, Smitherman C, Stapleton A. 2018. Maize phyllosphere microbial community niche development across stages of host leaf growth. F1000Research 6:1698
    [Google Scholar]
  90. 90. 
    Maslov S, Sneppen K. 2017. Population cycles and species diversity in dynamic Kill-the-Winner model of microbial ecosystems. Sci. Rep. 7:39642
    [Google Scholar]
  91. 91. 
    McArdle AJ, Kaforou M. 2020. Sensitivity of shotgun metagenomics to host DNA: abundance estimates depend on bioinformatic tools and contamination is the main issue. Access Microbiol 2:e000104
    [Google Scholar]
  92. 92. 
    Mejía LC, Herre EA, Sparks JP, Winter K, García MN et al. 2014. Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front. Microbiol. 5:479
    [Google Scholar]
  93. 93. 
    Mhlongo MI, Piater LA, Madala NE, Labuschagne N, Dubery IA. 2018. The chemistry of plant-microbe interactions in the rhizosphere and the potential for metabolomics to reveal signaling related to defense priming and induced systemic resistance. Front. Plant Sci. 9:112
    [Google Scholar]
  94. 94. 
    Michavila G, Adler C, De Gregorio PR, Lami MJ, Caram Di Santo MC et al. 2017. Pseudomonas protegens CS1 from the lemon phyllosphere as a candidate for citrus canker biocontrol agent. Plant Biol 19:608–17
    [Google Scholar]
  95. 95. 
    Morella NM, Gomez AL, Wang G, Leung MS, Koskella B. 2018. The impact of bacteriophages on phyllosphere bacterial abundance and composition. Mol. Ecol. 27:2025–38
    [Google Scholar]
  96. 96. 
    Morella NM, Weng FC-H, Joubert PM, Metcalf CJE, Lindow S, Koskella B. 2020. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. PNAS 117:1148–59Demonstrated host and environmental selection on tomato leaf microbiome assembly and stability.
    [Google Scholar]
  97. 97. 
    Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C. 2013. Microbial syntrophy: interaction for the common good. FEMS Microbiol. Rev. 37:384–406
    [Google Scholar]
  98. 98. 
    Moss EL, Maghini DG, Bhatt AS. 2020. Complete, closed bacterial genomes from microbiomes using nanopore sequencing. Nat. Biotechnol. 38:701–7
    [Google Scholar]
  99. 99. 
    Moyes AB, Kueppers LM, Pett-Ridge J, Carper DL, Vandehey N et al. 2016. Evidence for foliar endophytic nitrogen fixation in a widely distributed subalpine conifer. New Phytol 210:657–68
    [Google Scholar]
  100. 100. 
    Muller DB, Vogel C, Bai Y, Vorholt JA. 2016. The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50:211–34
    [Google Scholar]
  101. 101. 
    Nissen JN, Johansen J, Allesøe RL, Sønderby CK, Armenteros JJA et al. 2021. Improved metagenome binning and assembly using deep variational autoencoders. Nat. Biotech https://doi.org/10.1038/s41587-020-00777-4
    [Crossref] [Google Scholar]
  102. 102. 
    Nobori T, Wang Y, Wu J, Stolze SC, Tsuda Y et al. 2020. Multidimensional gene regulatory landscape of a bacterial pathogen in plants. Nat. Plants 6:883–96
    [Google Scholar]
  103. 103. 
    Ofek-Lalzar M, Gur Y, Ben-Moshe S, Sharon O, Kosman E et al. 2016. Diversity of fungal endophytes in recent and ancient wheat ancestors Triticum dicoccoides and Aegilops sharonensis. FEMS Microbiol. Ecol 92:fiw152Found that wild relatives of wheat contain beneficial endophytes not found in modern wheat.
    [Google Scholar]
  104. 104. 
    Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ et al. 2017. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2:1533–42
    [Google Scholar]
  105. 105. 
    Parnell JJ, Berka R, Young HA, Sturino JM, Kang Y et al. 2016. From the lab to the farm: an industrial perspective of plant beneficial microorganisms. Front. Plant Sci. 7:1110
    [Google Scholar]
  106. 106. 
    Peredo EL, Simmons SL. 2018. Leaf-FISH: microscale imaging of bacterial taxa on phyllosphere. Front. Microbiol. 8:2669
    [Google Scholar]
  107. 107. 
    Pérez-Jaramillo JE, Mendes R, Raaijmakers JM. 2016. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90:635–44
    [Google Scholar]
  108. 108. 
    Pieterse CM, Zamioudis C, Berendsen RL, Weller DM, Van Wees SC, Bakker PA 2014. Induced systemic resistance by beneficial microbes. Annu. Rev. Phytopathol. 52:347–75
    [Google Scholar]
  109. 109. 
    Poplin R, Chang P-C, Alexander D, Schwartz S, Colthurst T et al. 2018. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36:983–87
    [Google Scholar]
  110. 110. 
    Pratama AA, Terpstra J, de Oliveria ALM, Salles JF. 2020. The role of rhizosphere bacteriophages in plant health. Trends Microbiol 28:709–18
    [Google Scholar]
  111. 111. 
    Preston GM. 2017. Profiling the extended phenotype of plant pathogens. Mol. Plant Pathol. 18:443–56A review of how plant pathogens extend their phenotypes via niche construction.
    [Google Scholar]
  112. 112. 
    Prusty R, Grisafi P, Fink GR 2004. The plant hormone indoleacetic acid induces invasive growth in Saccharomyces cerevisiae. PNAS 101:4153–57
    [Google Scholar]
  113. 113. 
    Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. 2017. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35:833–44
    [Google Scholar]
  114. 114. 
    Raaijmakers JM, Vlami M, de Souza JT. 2002. Antibiotic production by bacterial biocontrol agents. Antonie van Leeuwenhoek 81:537
    [Google Scholar]
  115. 115. 
    Ren J, Song K, Deng C, Ahlgren NA, Fuhrman JA et al. 2020. Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8:64–77
    [Google Scholar]
  116. 116. 
    Ritpitakphong U, Falquet L, Vimoltust A, Berger A, Metraux JP, L'Haridon F. 2016. The microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen. New Phytol 210:1033–43
    [Google Scholar]
  117. 117. 
    Rodriguez RJ, Henson J, Van Volkenburgh E, Hoy M, Wright L et al. 2008. Stress tolerance in plants via habitat-adapted symbiosis. ISME J 2:404–16
    [Google Scholar]
  118. 118. 
    Rojas EC, Jensen B, Jørgensen HJL, Latz MAC, Esteban P et al. 2020. Selection of fungal endophytes with biocontrol potential against Fusarium head blight in wheat. Biol. Control 144:104222
    [Google Scholar]
  119. 119. 
    Rojas EC, Sapkota R, Jensen B, Jørgensen HJL, Henriksson T et al. 2020. Fusarium head blight modifies fungal endophytic communities during infection of wheat spikes. Microb. Ecol. 79:397–408
    [Google Scholar]
  120. 120. 
    Rondot Y, Reineke A. 2019. Endophytic Beauveria bassiana activates expression of defence genes in grapevine and prevents infections by grapevine downy mildew Plasmopara viticola. Plant Pathol 68:1719–31
    [Google Scholar]
  121. 121. 
    Rosado BHP, Almeida LC, Alves LF, Lambais MR, Oliveira RS. 2018. The importance of phyllosphere on plant functional ecology: a phyllo trait manifesto. New Phytol 219:1145–49
    [Google Scholar]
  122. 122. 
    Russel J, Røder HL, Madsen JS, Burmølle M, Sørensen SJ 2017. Antagonism correlates with metabolic similarity in diverse bacteria. PNAS 114:10684–88
    [Google Scholar]
  123. 123. 
    Sachs JL, Mueller UG, Wilcox TP, Bull JJ. 2004. The evolution of cooperation. Q. Rev. Biol. 79:135–60
    [Google Scholar]
  124. 124. 
    Sarhan MS, Hamza MA, Youssef HH, Patz S, Becker M et al. 2019. Culturomics of the plant prokaryotic microbiome and the dawn of plant-based culture media—A review. J. Adv. Res. 19:15–27Culturomics as a prerequisite for increasing the current understanding of in vivo microbial gene functioning in planta.
    [Google Scholar]
  125. 125. 
    Sartori M, Nesci A, García J, Passone MA, Montemarani A, Etcheverry M. 2017. Efficacy of epiphytic bacteria to prevent northern leaf blight caused by Exserohilum turcicum in maize. Rev. Argent. Microbiol. 49:75–82
    [Google Scholar]
  126. 126. 
    Saxena A, Raghuwanshi R, Singh HB. 2016. Elevation of defense network in chilli against Colletotrichum capsici by phyllospheric Trichoderma strain. J. Plant Growth Regul. 35:377–89
    [Google Scholar]
  127. 127. 
    Schulz-Bohm K, Gerards S, Hundscheid M, Melenhorst J, de Boer W, Garbeva P. 2018. Calling from distance: attraction of soil bacteria by plant root volatiles. ISME J 12:1252–62
    [Google Scholar]
  128. 128. 
    Segarra G, Van der Ent S, Trillas I, Pieterse CM. 2009. MYB72, a node of convergence in induced systemic resistance triggered by a fungal and a bacterial beneficial microbe. Plant Biol 11:90–96
    [Google Scholar]
  129. 129. 
    Senthilkumar M, Krishnamoorthy R. 2017. Isolation and characterization of tomato leaf phyllosphere Methylobacterium and their effect on plant growth. Int. J. Curr. Microbiol. Appl. Sci. 6:2121–36
    [Google Scholar]
  130. 130. 
    Seybold H, Demetrowitsch TJ, Hassani MA, Szymczak S, Reim E et al. 2020. A fungal pathogen induces systemic susceptibility and systemic shifts in wheat metabolome and microbiome composition. Nat. Commun. 11:1910
    [Google Scholar]
  131. 131. 
    Singh BK. 2017. Creating new business, economic growth and regional prosperity through microbiome-based products in the agriculture industry. Microb. Biotechnol. 10:224–27
    [Google Scholar]
  132. 132. 
    Strauss SY, Irwin RE. 2004. Ecological and evolutionary consequences of multispecies plant-animal interactions. Annu. Rev. Ecol. Evol. Syst. 35:435–66
    [Google Scholar]
  133. 133. 
    Strehmel N, Kopka J, Scheel D, Böttcher C. 2013. Annotating unknown components from GC/EI-MS-based metabolite profiling experiments using GC/APCI(+)-QTOFMS. Metabolomics 10:324–36
    [Google Scholar]
  134. 134. 
    Surana NK, Kasper DL. 2017. Moving beyond microbiome-wide associations to causal microbe identification. Nature 552:244–47
    [Google Scholar]
  135. 135. 
    Thapa S, Prasanna R. 2018. Prospecting the characteristics and significance of the phyllosphere microbiome. Ann. Microbiol. 68:229–45
    [Google Scholar]
  136. 136. 
    Thorp KR, Wang G, Bronson KF, Badaruddin M, Mon J. 2017. Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield. Comput. Electron. Agric. 136:1–12
    [Google Scholar]
  137. 137. 
    Vacher C, Hampe A, Porté AJ, Sauer U, Compant S, Morris CE. 2016. The phyllosphere: microbial jungle at the plant–climate interface. Annu. Rev. Ecol. Evol. Syst 47:1–24A review of microbial community assembly in the phyllosphere.
    [Google Scholar]
  138. 138. 
    Van der Ent S, Verhagen BW, Van Doorn R, Bakker D, Verlaan MG et al. 2008. MYB72 is required in early signaling steps of rhizobacteria-induced systemic resistance in Arabidopsis. Plant Physiol 146:1293–304
    [Google Scholar]
  139. 139. 
    van Vliet S, Doebeli M 2019. The role of multilevel selection in host microbiome evolution. PNAS 116:20591–97
    [Google Scholar]
  140. 140. 
    Vannier N, Agler M, Hacquard S. 2019. Microbiota-mediated disease resistance in plants. PLOS Pathog 15:e1007740
    [Google Scholar]
  141. 141. 
    Vannier N, Mony C, Bittebière A-K, Vandenkoornhuyse P. 2015. Epigenetic mechanisms and microbiota as a toolbox for plant phenotypic adjustment to environment. Front. Plant Sci. 6:1159
    [Google Scholar]
  142. 142. 
    Vidal S, Jaber LR. 2015. Entomopathogenic fungi as endophytes: plant–endophyte–herbivore interactions and prospects for use in biological control. Curr. Sci. 109:46–54
    [Google Scholar]
  143. 143. 
    Vogel C, Bodenhausen N, Gruissem W, Vorholt JA. 2016. The Arabidopsis leaf transcriptome reveals distinct but also overlapping responses to colonization by phyllosphere commensals and pathogen infection with impact on plant health. New Phytol 212:192–207
    [Google Scholar]
  144. 144. 
    Vorholt JA. 2012. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10:828–40
    [Google Scholar]
  145. 145. 
    Vos M, Wolf AB, Jennings SJ, Kowalchuk GA. 2013. Micro-scale determinants of bacterial diversity in soil. FEMS Microbiol. Rev. 37:936–54
    [Google Scholar]
  146. 146. 
    Wallace JG, Kremling KA, Kovar LL, Buckler ES. 2018. Quantitative genetics of the maize leaf microbiome. Phytobiomes J 2:208–24
    [Google Scholar]
  147. 147. 
    Wang H, Sun S, Ge W, Zhao L, Hou B et al. 2020. Horizontal gene transfer of Fhb7 from fungus underlies Fusarium head blight resistance in wheat. Science 368:eaba5435
    [Google Scholar]
  148. 148. 
    Wang X, Zhang X, Liu L, Xiang M, Wang W et al. 2015. Genomic and transcriptomic analysis of the endophytic fungus Pestalotiopsis fici reveals its lifestyle and high potential for synthesis of natural products. BMC Genom 16:28
    [Google Scholar]
  149. 149. 
    Wei Z, Jousset A. 2017. Plant breeding goes microbial. Trends Plant Sci 22:555–58Highlights developments in recovering desired plant phenotypes through microbial transmission to the next generation.
    [Google Scholar]
  150. 150. 
    Wu Y-W, Simmons BA, Singer SW. 2015. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605–7
    [Google Scholar]
  151. 151. 
    Zhang H, Zhu L, Luo L, Wang N, Chingin K et al. 2013. Direct assessment of phytochemicals inherent in plant tissues using extractive electrospray ionization mass spectrometry. J. Agric. Food Chem. 61:10691–98
    [Google Scholar]
  152. 152. 
    Zhang L, Chen L, Zhang M, Liu D, Sun H et al. 2020. Quantitative and qualitative characterization of plant endo-bacteriome by plant DNA-free sequencing method. Res. Square. https://doi.org/10.21203/rs.3.rs-33863/v1
    [Crossref] [Google Scholar]
  153. 153. 
    Zhou B, Luo Y, Bauchan GR, Feng H, Stommel JR. 2018. Visualizing pathogen internalization pathways in fresh tomatoes using MicroCT and confocal laser scanning microscopy. Food Control 85:276–82
    [Google Scholar]
/content/journals/10.1146/annurev-arplant-080620-114342
Loading
/content/journals/10.1146/annurev-arplant-080620-114342
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error