Page 264 - Microbial methane cycling in a warming world From biosphere to atmosphere Michiel H in t Zandt

Chapter 11. Integration and outlook
methods. In a time where sequencing depth is less of a challenge due to a constant, nearly exponential decrease of the per-base costs, sequencing quality and data quantity are the main concerns (Pettersson, Lundeberg and Ahmadian 2009). Where upgrades in sequencing chemistry and increases in sequencing depth can largely overcome and correct sequencing errors, it is nowadays a major challenge to scale computation to keep pace with the enormous amount of sequencing data that is being generated (Muir et al. 2016).
Primer-based 16S rRNA gene and functional gene sequencing methods are still incredibly popular due to their low cost-throughput ratio, as well as the presence of good quality pipelines and reference databases. In classical amplicon sequencing, variable regions of the 16S rRNA gene are used to estimate bacterial diversity (Mollet, Drancourt and Raoult 1997; Dahllof, Baillie and Kjelleberg 2000; Case et al. 2007). However, there is a large body of evidence that the primers used in these studies underestimate and miss microbial diversity (Rossi, Laurion and Lovejoy 2013; Hugerth et al. 2014; Brown et al. 2015; Wurzbacher et al. 2017; de Jong et al. 2018; Vigneron et al. 2020).
In several of our studies we used 16S rRNA gene-based approaches to unravel the microbial community structure (Chapter 5 and 7). Indeed, we observed that these studies are prone to miss microbial diversity and species-specific changes. In an in-depth metagenome study on methanogenic cultures from thermokarst lake sediments (Chapter 7), we observed species- specific responses to temperature increase in substrate amendment that were previously missed with 16S rRNA gene amplicon approaches (Chapter 6). A similar observation was made in Chapter 4, where we compared end-point 16S rRNA gene amplicon versus metagenome sequencing. Where 16S rRNA gene amplicon sequencing provided a snapshot of the microbial diversity, metagenome sequencing provided the power to reconstruct the metabolic food web of the ecosystem. However, analyzing metagenome datasets is much more time consuming and requires state-of-the-art computing infrastructures and specialized knowledge on software tools and microbial metabolism.
There are several alternatives and additions in the field to overcome these limitations. For example, for amplicon-based approaches, the reverse complement PCR (RC-PCR) technique is used to amplify multiple variable regions of the 16S rRNA gene to increase sequencing resolution (Nimagen 2020). Long-read technologies, such as PacBio and Oxford Nanopore Technologies for full 16S rRNA gene sequencing, improve the resolution of this high-
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