Phytomicrobiome Interactions and Sustainable Agriculture. Группа авторов

Phytomicrobiome Interactions and Sustainable Agriculture - Группа авторов


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       Ravindra Soni1, Deep Chandra Suyal2, Balram Sahu1, and Suresh Chandra Phulara3

       1 Department of Agricultural Microbiology, Indira Gandhi Krishi Vishwavidhyalaya, Raipur, Chhattisgarh, India

       2 Department of Microbiology, Akal College of Basic Sciences, Eternal University, Baru Sahib, Sirmour, Himachal Pradesh, India

       3 Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

      Like the human body, microbes also colonize inside plants (Qin et al. 2010; Zhao 2010; Gevers et al. 2012). The collective colonization of plant‐associated microbiota is known as a plant microbiome, which is a key determinant governing plant health and its productivity (Berendsen et al. 2012).

      Recent years have seen a remarkable interest in such interactions (Lebeis et al. 2012; Turner et al. 2013). A plant’s internal microbiome, also referred to as endophytic microbes, includes members from almost all microbial communities, such as archaea, bacteria, and fungi (Turner et al. 2013; Hardoim et al. 2015). Sometimes these microbial communities colonize inside plants in such a manner that their number surpasses the number of plant's own cells (Mendes et al. 2013). It is interesting to note that soil microbiomes are now touted as a cornerstone of the next green revolution (Parnell et al. 2016). The concept of soil, microbes, and plant interface, i.e. “soil‐microbe‐plant interface” is not new. However, the “soil‐microbe–soil‐plant–microbe‐plant interface” represents plant microbiome interaction more adequately. The rhizosphere that is affected by several climatic factors influences the plant and microbiome which ultimately utilize the habitat as an information highway (Bais et al. 2004; Roume et al. 2015; Tomer et al. 2017).

      Findings have revealed that a gram of soil contains 109 microbial cells. This exhibits a great diversity level by attaining about 106 taxa. Despite such diversity and number, only 1% of microbes residing in bulk soil and about 10% from plant‐influenced zones can culture in standard laboratory conditions. The majority of the soil microbiome remain uncultured. However, they can be detected using advanced molecular‐based approaches, which are discussed in later sections (Barret et al. 2013). Metagenomics allows the detection of microbial diversity in environmental samples and enables the construction of community‐level gene catalogs (Zengler and Palsson 2012; Franzosa et al. 2015; Guo et al. 2015; Widder et al. 2016). Broadly, metagenomics represents a new advanced approach for genomic analysis. It refers to the simultaneous characterization of genomes in microbial communities from environmental samples, using molecular techniques like PCR, cloning, and DNA sequencing for identifying new sources of plant growth–promoting traits. The approach of metagenomics has also been referred to as environmental genomics, ecogenomics, and community genomics (Song et al. 2013).

      Usually, metagenomic analysis conducts 16S rRNA or 18 S rRNA gene surveys to observe microbial community composition while also informing the sequencing complexity required to access high levels of metagenome coverage (Tyson et al. 2004; Lauro et al. 2011). Complete sequencing of clones containing phylogenetic anchors can be performed by using sequence‐based metagenomics to indicate the taxonomic group. However, functional metagenomics has identified antibiotics (Hover et al. 2018; Wrighton 2018), antibiotic resistance genes (De Castro et al. 2014; López‐Pérez and Mirete 2014), degradative enzymes (Silva et al. 2013; Choi et al. 2018), and plant growth‐promoting traits (Tsurumaru et al. 2015). The clones containing metagenomic DNA can also be screened for expression activities by functional analysis. Although it holds great potential in terms of shaping ecological theory; however, due to the slow screening technology it is subsequently lagging behind shotgun sequencing (Zhou et al. 2015).


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