Phytomicrobiome Interactions and Sustainable Agriculture. Группа авторов

Phytomicrobiome Interactions and Sustainable Agriculture - Группа авторов


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2014). Recently, microbiome functions in the rhizospheric region were analyzed utilizing a similar approach, in which a metabolic‐network–based framework for metagenomics was used (Ofaim et al. 2017). To investigate the microbial diversity in a rhizospheric region of paddy fields, advanced molecular biology tools can be applied. It includes culture‐independent molecular techniques, 16S rRNA clone library generation along with RFLP, sequencing, and phylogenetic analysis, etc. (Arjun and Harikrishnan 2011). Furthermore, metagenomic analysis of rhizosphere of plants adapted to acid mine drainage revealed the presence of novel nickel resistance genes (Mirete et al. 2007). These approaches reveal that the soil and rhizosphere microbiomes diversity is highly underrated (Mendes et al. 2013).

Schematic illustration of the metagenomic approach to reveal the structure and function of a plant microbiome.

      (Source: Sleator et al. (2008) © 2008, Society for Applied Microbiology. Reprinted with permissions of John Wiley & Sons).

Techniques used Rhizosphere References
Amplicon gene sequencing of conserved marker genes,16S rRNA Barley and alfalfa Kumar et al. (2018b)
Rice roots Edwards et al. (2018)
Metagenome sequencing Taxus rhizosphere Hao et al. (2018)
Wheat and cucumber Ofaim et al. (2017)
Metatranscriptome sequencing Wheat Hayden et al. (2018)
Seagrass Microbiomes Crump et al. (2018)
Metaproteomic profiling Biscutella laevigata Mattarozzi et al. (2017)
Sugarcane Lin et al. (2013)
Metabolomic profiling Arabidopsis Pétriacq et al. (2017)

      The 454‐pyrosequencing platform comprising rRNA or ITS amplicon sequencing, whole‐genome sequencing, shotgun metagenomics, and transcriptional profiling is widely employed for microbial community analysis (Rastogi et al. 2012; Correa‐Galeote et al. 2018). These revolutionary technological innovations facilitate the comparative microbiome analyses and provided a deep insight into the structural and functional characteristics of phyllosphere microbiome. Furthermore, in an interesting study of netaproteogenomic analysis (includes metagenomics and metaproteomics) of rice microbiome revealed that methanol‐based methylotrophy linked to the genus Methylobacterium dominated within the protein repertoire of the phyllosphere microbiota (Knief et al. 2012). Similarly, a combined metagenomic and metaproteomic approaches were also applied for analyzing the physiology of phyllosphere bacterial communities in situ (Delmotte et al. 2009). Several reports in the public domain reveal phyllosphere microbial diversity through metagenomic approaches (Atamna‐Ismaeel et al. 2012; Ritpitakphong et al. 2016; Mukhtar et al. 2017).

      Metagenome sequencing generally ends up with the generation of a huge pool of nucleotide sequences. This vast data set of nucleotide sequences need to be analyzed thoroughly before converting it to meaningful information. The in‐silico approaches, which include several bioinformatics tools, have helped metagenome researches in data mining. These tools


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