Abstract
The study of plant microbiota has been stimulated by recognizing the fundamental role that the genetic capacity of the associated microbial communities has to modulate the phenotypic expression of plants, which is crucial for its health, physiology, and productivity. All genes in a metagenome can be described by whole-metagenome shotgun sequencing, but it is time-consuming, and a high level of experience is required. Alternatively, the amplification and high-throughput sequencing of the16S-rRNA gene allow describing the microbial composition. Then functional activities can be inferred by listing the abundance of each gene. Also, the identification and the quantification of microbiome transcripts are now accessible to determine the profile and changes in gene expression occurring in a microbial community in response to environmental or experimental variations. Considering the beneficial role of microbial communities in soil environments, it is important increasing the understanding of plant-microbe relationships to provide biotechnological information for control and management with sustainable practices.
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References
Almeida A, Mitchell AL, Tarkowska A, Finn RD (2018) Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments. GigaScience 7:giy054
Aßhauer KP, Meinicke P (2013) On the estimation of metabolic profiles in metagenomics. In: On the estimation of metabolic profiles in metagenomics, OASIcs-OpenAccess Series in Informatics: Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik
Aßhauer KP, Wemheuer B, Daniel R, Meinicke P (2015) Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics 31:2882–2884
Balvočiūtė M, Huson DH (2017) SILVA, RDP, Greengenes, NCBI and OTT, how do these taxonomies compare? BMC Genomics 18:114
Bejerano-Sagie M, Xavier KB (2007) The role of small RNAs in quorum sensing. Curr Opin Microbiol 10:189–198
Béné C, Arthur R, Norbury H, Allison E, Beveridge M, Bush S, Campling L, Leschen W, Little D, Squires D, Thilsted S, Troell M, Williams M (2016) Contribution of fisheries and aquaculture to food security and poverty reduction: assessing the current evidence. World Develop 79:177–196
Berendsen RL, Pieterse CM, Bakker PA (2012) The rhizosphere microbiome and plant health. Trends Plant Sci 17:478–486
Busby PE, Soman C, Wagner MR, Friesen ML, Kremer J, Bennett A, Morsy M, Eisen JA, Leach JE, Dangl JL (2017) Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol 15:e2001793
Chaparro JM, Sheflin AM, Manter DK, Vivanco JM (2012) Manipulating the soil microbiome to increase soil health and plant fertility. Biol Fertil Soils 48:489–499
Dos Santos DFK, Istvan P, Quirino BF, Kruger RH (2017) Functional metagenomics as a tool for identification of new antibiotic resistance genes from natural environments. Microb Ecol 73:479–491
Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831
Glöckner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, Bruns G, Yarza P, Peplies J, Westram R, Ludwig W (2017) 25 years of serving the community with ribosomal RNA gene reference databases and tools. J Biotechnol 261:169–176
Hayden HL, Savin K, Wadeson J, Gupta V, Mele PM (2018) Comparative metatranscriptomics of wheat rhizosphere microbiomes in disease suppressive and non-suppressive soils for Rhizoctonia solani AG8. Front Microbiol 9:859
Jones AD, Ejeta G (2016) A new global agenda for nutrition and health: the importance of agriculture and food systems. Bull World Health Organ 94:228–229
Jovel J, Patterson J, Wang W, Hotte N, O’keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL (2016) Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol 7:459
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:D480–D484
Kothari V, Kothari C, Rank J, Joshi A, Singh RP, Kothari R (2017) Metatranscriptomic 16 Rhizosphere for finding of the plant. Understanding host-microbiome interactions – an omics approach: Host-Microbiome Association 1:267p
Lakshmanan V, Selvaraj G, Bais HP (2014) Functional soil microbiome: belowground solutions to an aboveground problem. Plant Physiol 166:689–700
Lam KN, Cheng J, Engel K, Neufeld JD, Charles TC (2015) Current and future resources for functional metagenomics. Front Microbiol 6
Langille MGI, Zaneveld J, Caporaso JG, Mcdonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotech 31:814–821
Lazarevic V, François P (2013) Functional genomics of microbial pathogens. Brief Funct Genomics 12:289–290
Loman NJ, Pallen MJ (2015) Twenty years of bacterial genome sequencing. Nat Rev Microbiol 13:787
Mendes R, Garbeva P, Raaijmakers JM (2013) The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol Rev 37(5):634–663
Mendes LW, Kuramae EE, Navarrete AA, Van Veen JA, Tsai SM (2014) Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J 8:1577–1587
Morgan XC, Huttenhower C (2012) Human microbiome analysis. PLoS Comput Biol 8:e1002808
Ortiz-Estrada ÁM, Gollas-Galván T, Martínez-Córdova LR, Martínez-Porchas M (2019) Predictive functional profiles using metagenomic 16S rRNA data: a novel approach to understanding the microbial ecology of aquaculture systems. Rev Aquacult 11:234–245
Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124
Pehrsson EC, Forsberg KJ, Gibson MK, Ahmadi S, Dantas G (2013) Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs. Front Microbiol 4:145
Popovic A, Parkinson J (2018) Characterization of eukaryotic microbiome using 18S amplicon sequencing. In: Press SH (ed) Microbiome analysis. Springer, New York, pp 29–48
Qin S, Xing K, Jiang JH, Xu LH, Li WJ (2011) Biodiversity, bioactive natural products and biotechnological potential of plant-associated endophytic actinobacteria. Appl Microbiol Biotechnol 89(3):457–473
Quince C, Walker AW, Simpson JT, Loman NJ, Segata N (2017) Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 35:833–844
Ramirez KS, Lauber CL, Knight R, Bradford MA, Fierer N (2010) Consistent effects of nitrogen fertilization on soil bacterial communities in contrasting systems. Ecology 91:3463–3470
Rhoads A, Au KF (2015) PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13:278–289
Rooijers K, Kolmeder C, Juste C, Doré J, De Been M, Boeren S, Galan P, Beauvallet C, De Vos WM, Schaap PJ (2011) An iterative workflow for mining the human intestinal metaproteome. BMC Genomics 12:6
Siegwald L, Touzet H, Lemoine Y, Hot D, Audebert C, Caboche S (2017) Assessment of common and emerging bioinformatics pipelines for targeted metagenomics. PLoS One 12:e0169563
Theis KR, Dheilly NM, Klassen JL, Brucker RM, Baines JF, Bosch TC, Cryan JF, Gilbert SF, Goodnight CJ, Lloyd EA (2016) Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. Msystems 1:e00028–e00016
Vargas-Albores F, Martinez-Cordova LR, Martinez-Porchas M, Calderon K, Lago-Leston A (2018) Functional metagenomics: a tool to gain knowledge for agronomic and veterinary sciences. Biotechnol Genet Eng Rev:1–23
Wagner J, Coupland P, Browne HP, Lawley TD, Francis SC, Parkhill J (2016) Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification. BMC Microbiol 16:274
Wallenstein MD (2017) Managing and manipulating the rhizosphere microbiome for plant health: a systems approach. Rhizosphere 3:230–232
Wang Y, Tian RM, Gao ZM, Bougouffa S, Qian P-Y (2014) Optimal eukaryotic 18S and universal 16S/18S ribosomal RNA primers and their application in a study of symbiosis. PLoS One 9:e90053
Yang C, Ji Y, Wang X, Yang C, Douglas WY (2013) Testing three pipelines for 18S rDNA-based metabarcoding of soil faunal diversity. Sci China Life Sci 56:73–81
Zhu S, Vivanco JM, Manter DK (2016) Nitrogen fertilizer rate affects root exudation, the rhizosphere microbiome and nitrogen-use-efficiency of maize. Appl Soil Ecol 107:324–333
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Garibay-Valdez, E., Calderón, K., Vargas-Albores, F., Lago-Lestón, A., Martínez-Córdova, L.R., Martínez-Porchas, M. (2019). Functional Metagenomics for Rhizospheric Soil in Agricultural Systems. In: Tripathi, V., Kumar, P., Tripathi, P., Kishore, A. (eds) Microbial Genomics in Sustainable Agroecosystems. Springer, Singapore. https://doi.org/10.1007/978-981-13-8739-5_8
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