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1.5.2.1 Denaturing Gradient Gel Electrophoresis (DGGE)

DGGE technique allows separation of DNA fragments with the same length but different sequences which represent different individual species within the sample (Muyzer et al., 1993). DNA fragments are separated based on the decreased electrophoretic mobility of a partially melted double stranded DNA in polyacrylamide gels containing gradient of DNA denaturants (Muyzer et al., 1993). By targeting 16S rRNA and 18S rRNA gene, DGGE has been used for analysis of bacterial and fungal communities (including AMF) (Garbeva et al., 2001; Kowalchuk et al., 2002; Toljander et al., 2008; Cleary et al., 2012).

Denaturing gradient gel electrophoresis (DGGE), has been used to analyse communities of endophytic bacteria in many different samples and for different purposes. By using

29 DGGE Abreu-Tarazi et al. (2010) showed distinct endophytic bacterial communities, namely, Actinobacteria, Alphaproteobacteria and Betaproteobacteria in different organs of

Ananas comosus (pineapples) suggesting an influence of plant organ on endophytic community. Doi et al. (2007) demonstrated that greater bacterial diversity was observed in the rhizosphere of rice in upland soil compared to lowland soil using DGGE. Kowalchuk et al. (2002) used DGGE to investigate AMF associated with Ammophila arenaria in Dutch coastal sand dunes without the use of trap plant cultivation methods. This technique allowed the author to detect a putatively novel Glomus species. DGGE can be a powerful tool to assess microbial communities because of its high throughput, ability to

comparatively profile many samples and thus facilitate the spatial and temporal analysis of microbial communities in ecosystems (Nakatsu, 2007).

Although a well-recognized and widely used technique several limitations are attributed to DGGE when assessing community diversity including the inability to detect minor

components of the microbial community (Mühling et al., 2008), co-migration of DNA molecules with different sequences and the potential to produce multiple bands from a single bacterial species (Muyzer et al., 1993; Nübel et al., 1997). These facets may result in an over or under estimate of the community diversity. However, despite its limitations, several studies have demonstrated a congruent pattern of bacterial composition and diversity between DGGE and more advanced technique such as high throughput sequencing (Cleary et al., 2012; Qin et al., 2016).

1.5.2.2 DNA metabarcoding using next generation sequencing (NGS) platforms Next generation sequencing refers to massively parallel methods for DNA sequencing that allow several hundred thousand to tens of millions of sequences to be read at the same time (Shokralla et al., 2012). This field is progressing fast with pyrosequencing machines no longer being made despite only being introduced by Roche in 2005. One of the current commonly used commercial platforms is the Illumina MiSeq (Illumina Inc., San Diego, CA, USA) which is based on sequencing-by-synthesis of different template

molecules adhering to the surface of a flow cell simultaneously. In this massively parallel process a single base is added to all templates per flow cycle and the incorporated nucleotide in each cluster on the flow cell is identified before the next base is added (Shokralla et al., 2012). With the development of a dual-index sequencing strategy, the Illumina MiSeq platform can produce paired 250-nucleotide reads with good resolution for microbial taxonomic assignment (Kozich et al., 2013).

30 Metabarcoding using Illumina MiSeq platform is a powerful tool to study endophyte

communities. DNA metabarcoding targets a particular gene to describe structure (taxonomic characterization) of microbes from environmental samples (Mendoza et al., 2015). The 16S rRNA gene is commonly used for metabarcoding to characterize bacteria commuities from enviromental samples because this gene is present in all bacteria and is sufficient for taxonomic assignment (Chakraborty et al., 2014). In addition, large curated databases such as SILVA, Greengenes and Ribosomal Data Project (Bacci et al., 2015) are available to assign bacterial taxonomy. Müller et al. (2015a) used 16S rRNA

metabarcoding with Illumina MiSeq platform to show that Proteobacteria, followed by Firmicutes, Actinobacteria, and Bacteroidetes were abundant in leaves of Olea europae (olives). The author also stated that the composition of the endophytic bacteria had a strong correlation to the plant genotypes. A similar study by Barret et al. (2015)

demonstrated that during germination and emergence, the seed microbiome of 28 plant genotypes affiliated mostly to the Brassicaceae were affected by plant genotype.

Furthermore, Edwards et al. (2015) reported that root-associated microbe communities of Oryza spp. (rice) were influenced by geographical location, soil source, host genotype and cultivation practice

Microorganisms that commonly associate with a plant species and provide a key role in their physiology may be considered as forming a core microbiome (Lebeis, 2014). Using metabacoding, Winston et al. (2014) revealed a core endomicrobiome in Cannabis samples that consisted of Pseudomonas, Cellvibrio, Oxalobacteraceae,

Xanthomonadaceae, Actinomycetales and Sphingobacteriales from the endorhiza, rhizosphere and bulk soil of five distinct Cannabis cultivars. Five operational taxonomy units (OTUs) consisting of Pelomonas sp., Ralstonia sp., Nitrososphaera sp.,

Pseudomonas sp. and Actinobacter sp. were defined as putative core microbiome in O. europae (Müller et al., 2015a).

1.5.2.3 Metagenomics: DNA sequencing from environmental samples

As opposed to metabarcoding, metagenomics studies not only characterize structure but also characterize function of collective microbial genomes recovered directly from

environmental samples (Mendoza et al., 2015). This approach not only targets a specific DNA region as a gene marker (metabarcoding) but the genome as a whole. Thus, it provides functional characterization of microbes in the environment (Mendoza et al., 2015). Prior to the introduction of NGS, metagenomic studies were done by extracting DNA from environmental samples, shearing it to yield DNA with sufficient length, DNA

31 cloning and direct sequencing with Sanger sequencing (shotgun sequencing) (Riesenfeld et al., 2004; Tyson et al., 2004; Sessitsch et al., 2012). Using new massively parallel sequencing technologies this process is improved by avoiding the need to create large clone libraries (Tian et al., 2015). By using a metagenomics approach (shotgun

sequencing), Wang et al. (2008) reported-proteobacteria and Actinobacteria as the dominant endophytic bacteria from the tropic tree Mallotus nudiflorus while some of their genes associated with amino acid transport and metabolism, carbohydrate transport and metabolism and secondary metabolite biosynthesis. The author suggested that

metagenomics can reveal the potency of plant microbiota as a source of bioactive compounds. Using a similar approach Sessitsch et al. (2012) reported putative genes from endophytic bacteria of rice including those endcoding flagella, plant-polymer-

degrading enzymes, protein secretion systems, iron acquisition and storage and quorum sensing. The author suggested that these genes might be associated with their

endophytic lifestyle in plant host. Metagenomics approach can predict trait and metabolic processes from microbe in the environment (Fierer et al., 2012; Sessitsch et al., 2012).