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Functional Metagenomics for Rhizospheric Soil in Agricultural Systems

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Microbial Genomics in Sustainable Agroecosystems

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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Aßhauer KP, Wemheuer B, Daniel R, Meinicke P (2015) Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics 31:2882–2884

    Article  Google Scholar 

  • Balvočiūtė M, Huson DH (2017) SILVA, RDP, Greengenes, NCBI and OTT, how do these taxonomies compare? BMC Genomics 18:114

    Article  Google Scholar 

  • Bejerano-Sagie M, Xavier KB (2007) The role of small RNAs in quorum sensing. Curr Opin Microbiol 10:189–198

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • Berendsen RL, Pieterse CM, Bakker PA (2012) The rhizosphere microbiome and plant health. Trends Plant Sci 17:478–486

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • Lakshmanan V, Selvaraj G, Bais HP (2014) Functional soil microbiome: belowground solutions to an aboveground problem. Plant Physiol 166:689–700

    Article  CAS  Google Scholar 

  • Lam KN, Cheng J, Engel K, Neufeld JD, Charles TC (2015) Current and future resources for functional metagenomics. Front Microbiol 6

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Lazarevic V, François P (2013) Functional genomics of microbial pathogens. Brief Funct Genomics 12:289–290

    Article  CAS  Google Scholar 

  • Loman NJ, Pallen MJ (2015) Twenty years of bacterial genome sequencing. Nat Rev Microbiol 13:787

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Morgan XC, Huttenhower C (2012) Human microbiome analysis. PLoS Comput Biol 8:e1002808

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Quince C, Walker AW, Simpson JT, Loman NJ, Segata N (2017) Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 35:833–844

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Rhoads A, Au KF (2015) PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13:278–289

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Wallenstein MD (2017) Managing and manipulating the rhizosphere microbiome for plant health: a systems approach. Rhizosphere 3:230–232

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

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Correspondence to Kadiya Calderón or Marcel Martínez-Porchas .

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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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