Metagenomic insights to the functional potential of sediment microbial communities in freshwater lakes

Biessy L, Pearman JK, Waters S, Vandergoes MJ, Wood SA (2022) Metagenomic insights to the functional potential of sediment microbial communities in freshwater lakes. Metabarcoding and Metagenomics 6: e79265. https://doi.org/10.3897/mbmg.6.79265

Read Paper

Abstract

Molecular-based techniques offer considerable potential to provide new insights into the impact of anthropogenic stressors on lake ecosystems. Microbial communities are involved in many geochemical cycling processes in lakes and a greater understanding of their functions could assist in guiding more targeted remedial actions. Recent advances in metagenomics now make it possible to determine the functional potential of entire microbial communities. The present study investigated microbial communities and their functional potential in surface sediments collected from three lakes with differing trophic states and characteristics. Surface sediments were analysed for their nutrient and elemental contents and metagenomics and metabarcoding analysis undertaken. The nutrients content of the surface sediments did not show as distinct a gradient as water chemistry monitoring data, likely reflecting effects of other lake characteristics, in particular, depth. Metabarcoding and metagenomics revealed differing bacterial community composition and functional potential amongst lakes. Amongst the differentially abundant metabolic pathways, the most prominent were clusters in the energy and xenobiotics pathways. Differences in the energy metabolism paths of photosynthesis and oxidative phosphorylation were observed. These were most likely related to changes in the community composition and especially the presence of cyanobacteria in two of the three lakes. Xenobiotic pathways, such as those involving polycyclic aromatic hydrocarbons, were highest in the lakes with the greatest agricultural land-use in their catchment. These results highlight how microbial metagenomics can be used to gain insights into the causes of differences in trophic status amongst lakes.