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In our last blog article, we detailed reasons why it is interesting to study plant microbiomes. Today, we will share an overview of  the tools and techniques that scientists can leverage for this purpose. 

#1: Amplicon sequencing

A common approach to identifying and quantifying species of microorganisms is amplicon sequencing. Here, a certain region in the genome is amplified by PCR prior to sequencing, with the goal to selectively detect only this specific region. The choice of this region depends on multiple factors. The region must be: 

  1. Variable enough to identify different species or families
  2. Stable enough so it doesn't vary between individuals 
  3. Similar enough to allow for their selective enrichment prior to sequencing. 

The genes that code for ribosomal RNA meet all three of these criteria - which is why they are often used for amplicon sequencing of microbiomes.

#2: Whole genome sequencing

With the continuous decrease in the cost of sequencing, the analysis of whole genomes has become more and more accessible. Using a method termed ‘shotgun’ sequencing where the DNA from an environmental sample is randomly fragmented and then sequenced, information can be obtained about the whole genome of the species in a microbiome. 

In contrast to amplicon sequencing, this information can directly indicate whether a certain microorganism could be detrimental, neutral or beneficial to the plant. Additionally, microbiomes composed of different species could still harbour the same genomic potential if they contain similar genes. For example, metagenomics studies have revealed how microbial traits that are beneficial to plants - for example, the processing of minerals into molecules that are easily taken up by the roots - are enriched in plant microbiomes, compared to bulk soil microbial communities (Trevedi et al., 2021).

One major challenge for shotgun metagenomics (‘meta’ indicates sequencing many species from the same sample) is the assembly of DNA fragments into high-quality genomes, and the annotation of each genome to the correct species (Levy et al., 2018). Long-read sequencing techniques such as PacBio’s HiFi or Oxford Nanopore Technology (ONT) overcome the problem of fragmentation and can be used to build complete genome assemblies (Liu et al., 2022), tackling the first issue.

Looking forward, we expect that the emergence of single-cell sequencing techniques will help with grouping together all the reads from individual bacteria, enabling the association of plasmids and viruses with their hosts (Levy et al., 2018). With current single-cell techniques, genome assemblies generally are of lower quality, emphasizing the importance of combining these different techniques to leverage their respective advantages.

Another challenge in metagenomics, especially for studying endophytic communities (i.e. microbiomes living inside the plant), is the overwhelming majority of plant genetic material in the sample. Methods have been developed to selectively enrich bacterial cells or DNA during sample preparation (Nobori et al., 2018) and with ONT it is even possible to selectively reject unwanted reads from the nanopore! While this technique is still in its first stage and can currently only detect a limited number of sequences, or else requires a GPU (Martin et al., 2022), it is a promising method for metagenomics analyses. Also, it’s just really cool!

#3: The full meta-omics toolbox

While the genes in the microbiome uncover the potential effects on the plant, this does not tell us anything about the transcriptional or translational level: i.e., whether these genes are actually activated and their functions carried out by proteins. 

Root_blog2_imageMeasuring RNA expression (‘metatranscriptomics’) provides more insights into the biochemical pathways that are activated in the microbial community. Going even further down the DNA-RNA-protein ladder, proteins (‘metaproteomics’) and metabolites (‘metabolomics’) can be measured using mass spectrometry. As not all RNA gets translated, protein levels and the identification of small metabolites can lead to an even more complete picture of the processes in the microbiome. For example, studying the root metabolome has further revealed how cereals shape the microbiome in the rhizosphere, increasing their tolerance against caterpillars, and even the tolerance of their offspring (Trevedi et al., 2021; Hu et al., 2018).

While each of these meta-omics techniques are powerful on its own, combining them truly unlocks the potential to unravel the complex plant-microbiome interactions. By integrating transcriptomic, molecular and metabolomic analyses Fiorilli et al (2018) unravelled how symbiotic fungi confer resistance to bacterial leaf streak in wheat, a disease that can cause up to 40% yield loss. Inferring gene regulatory networks and adding knowledge on protein-protein interactions further helps to piece together the pieces of the plant-microbe puzzle. 

How to choose the best method?

In short, a multitude of techniques is available for identifying the species and their functions in a microbial community, each with their challenges and advantages. As with so many things in science, there is no golden rule as to which technique is best, as this depends on the experimental setup, the type of sample, the goal of the study and - last but not least - the available budget. That’s why it’s key to get an experienced partner on board!

At BioLizard, we have proven expertise in supporting our clients not only with data analytics, but also with data strategy. Not sure which of the many available tools and techniques to choose? BioLizard can perform an unbiased vendor assessment for you to ensure that your approach is aligned with your R&D goals. 

Want to learn more about data analytics tailored to the unique requirements of microbiome data?

Then stay tuned for the last blog post of this series, where we will dive into strategies for analyzing microbiome data.

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