A global perspective on bacterial diversity in the terrestrial deep subsurface

Abstract

While recent efforts to catalogue Earth's microbial diversity have focused upon surface and marine habitats, 12-20 % of Earth's biomass is suggested to exist in the terrestrial deep subsurface, compared to ~1.8 % in the deep subseafloor. Metagenomic studies of the terrestrial deep subsurface have yielded a trove of divergent and functionally important microbiomes from a range of localities. However, a wider perspective of microbial diversity and its relationship to environmental conditions within the terrestrial deep subsurface is still required. Our meta-analysis reveals that terrestrial deep subsurface microbiota are dominated by Betaproteobacteria, Gammaproteobacteria and Firmicutes, probably as a function of the diverse metabolic strategies of these taxa. Evidence was also found for a common small consortium of prevalent Betaproteobacteria and Gammaproteobacteria operational taxonomic units across the localities. This implies a core terrestrial deep subsurface community, irrespective of aquifer lithology, depth and other variables, that may play an important role in colonizing and sustaining microbial habitats in the deep terrestrial subsurface. An in silico contamination-aware approach to analysing this dataset underscores the importance of downstream methods for assuring that robust conclusions can be reached from deep subsurface-derived sequencing data. Understanding the global panorama of microbial diversity and ecological dynamics in the deep terrestrial subsurface provides a first step towards understanding the role of microbes in global subsurface element and nutrient cycling.

Keywords: 16S rRNA gene; bacterial; deep subsurface; meta-analysis.

Fig. 1.

Averaged relative abundances (coloured by increasing percentage abundance) of the most abundant taxonomic groups (y-axis) across the dataset across all analysed aquifer lithologies (x-axis).

Fig. 2.

Prevalence (number of samples in which an OTU is present, x-axis) of OTUs across the dataset and associated reads (y-axis). Colours depict classification of OTUs at the order level. The vertical line is at 20 samples on the x-axis to highlight OTUs present in 20 or more samples.