Respect the old but seek out the new: Direct 16S rRNA-seq from bacterial communities
I think it’s fair to say that it’s an ongoing struggle to figure out what the heck microbes are doing in their natural environments, and who those microbes are. Clearly, there is no silver bullet that gives us all the easy answers. Sequencing and comparison of 16S rRNA gene sequences have become a routine method for surveying microbes in a sample, since these sequences are made up of regions that are incredibly conserved as well as others that are “hypervariable”, allowing distantly related taxa to be compared, while still resolving closely related lineages.
About ten bajillion (slight under exaggeration) studies have been published using the 16S rRNA gene, which has many times been referred to as the new “gold standard” to classify strains and identify species. The microbial ‘species’ concept thing is a can of worms we don’t need to open up right now, the main point here is that the 16S rRNA is widely used and helpful for characterizing microbial communities.
Traditional methods use PCR to amplify 16S rRNA genes from the environment to identify community members. However, there are inherent biases to this approach since it depends on the ability of the primers (that were designed based on what was already known) to amplify the 16S rRNA genes. These primers might miss sequences from previously unknown microbes. There are other pitfalls that are encountered en route to the most representative snapshot of microbial community. For example, DNA extraction method can affect results and 16S rRNA gene sequences from less abundant taxa are often not found in metagenomic datasets.
A recent study by Rosselli and colleagues presents an rRNA–seq approach that the authors suggest complements traditional DNA-based methods dependent on PCR. Their method skips the primers and the PCR and instead directly sequences 16S rRNA. Using a model anammox community, they are able to highlight taxon abundance as well as physiological activity. The two rRNA–seq libraries (taken at different time points) were sequenced on an AB SOLiD 5500 XL platform, however, at the same time, they also utilized a PCR-dependent method with universal 16S primers and pyrosequencing.
Rosselli and colleagues emphasize that this is not a comparison of methods, (good to note that different sequencing platforms and bioinformatics tools were used in downstream analysis, so definitely not comparable), and instead demonstrate that complementary approaches should be implemented to help paint a full picture of the microbial community. The direct rRNA–seq method definitely revealed a different community than was illustrated by the other, so-called “standard”, approach.
There is no one best or only way to investigate the role that microbes play in the environment and how they function in nature, and the 16S isn’t the only sequence needed from a community to identify active / important members. With all the affordable sequencing available and as new techniques develop, (hopefully) becoming less reliant on biased methods, and as we continue to develop ways to culture microbes from the environment, we are moving towards a better understanding of the organisms that surround us. The method presented by Rosselli and colleagues represents another tool to assess microbial diversity and functional activity.
Rosselli, R., Romoli, O., Vitulo, N., Vezzi, A., Campanaro, S., de Pascale, F, Schiavon, R., Tiarca, M., Poletto, F., Concheri, G, Valle, G., and Squartini, A. Direct 16S rRNA-seq from bacterial communities: a PCR-independent approach to simultaneously assess microbial diversity and functional activity potential of each taxon. Scientific Reports. (2016). Published online August 31. doi:10.1038/srep32165.
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample Proc Natl Acad Sci USA.108, 4516–4522 (2011).
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Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research 41, (2013). doi:10.1093/nar/gks808