Is the genetic landscape of the deep subsurface biosphere affected by viruses?

Abstract

Viruses are powerful manipulators of microbial diversity, biogeochemistry, and evolution in the marine environment. Viruses can directly influence the genetic capabilities and the fitness of their hosts through the use of fitness factors and through horizontal gene transfer. However, the impact of viruses on microbial ecology and evolution is often overlooked in studies of the deep subsurface biosphere. Subsurface habitats connected to hydrothermal vent systems are characterized by constant fluid flux, dynamic environmental variability, and high microbial diversity. In such conditions, high adaptability would be an evolutionary asset, and the potential for frequent host-virus interactions would be high, increasing the likelihood that cellular hosts could acquire novel functions. Here, we review evidence supporting this hypothesis, including data indicating that microbial communities in subsurface hydrothermal fluids are exposed to a high rate of viral infection, as well as viral metagenomic data suggesting that the vent viral assemblage is particularly enriched in genes that facilitate horizontal gene transfer and host adaptability. Therefore, viruses are likely to play a crucial role in facilitating adaptability to the extreme conditions of these regions of the deep subsurface biosphere. We also discuss how these results might apply to other regions of the deep subsurface, where the nature of virus-host interactions would be altered, but possibly no less important, compared to more energetic hydrothermal systems.

Keywords: deep subsurface biosphere; hydrothermal vents; microbial evolution; viral ecology.

Figure 1

Schematic of fluid flux and gradient formation in (A) hydrothermal systems and (B) various regimes of the deep biosphere. Arrows represent fluid flux. Based on figures in Edwards et al. (2011), Huber et al. (2003), and Baross and Hoffman (1985).

Figure 2

Number of CRISPR loci per genome in thermophilic, mesophilic, and psychrophilic archaea and bacteria. Box boundaries represent first and third quartiles, and markers on lines represent minimum, median, and maximum, respectively. Modified from Anderson et al. (2011).

Figure 3

Maximum-likelihood phylogenetic tree of NAD-dependent DNA ligases with metagenomic reads from the marine vent virome. The “large read cluster” denotes the branch in which the majority of metagenomic reads matching ligases grouped on the tree. Numbers indicate the bootstrap values of internal nodes (where n = 100). Metagenomic reads are colored red. All ligase protein sequences were obtained from NCBI (accession numbers listed). Trees were constructed in RAxML by incorporating metagenomic sequences into a constraint tree of references sequences based on the phylogeny of Yutin and Koonin (2009). Trees imaged with TreeViewX.

Figure 4

Relative percentages of reads matching gene categories in the marine vent virome (Anderson et al., 2011) and the hydrothermal vent cellular metagenome by Xie et al. (2011). The solid line indicates a 1:1 ratio between the viral and cellular metagenomes. Metagenomes were analyzed with MG-RAST (Meyer et al., 2008), and reads were annotated with the KEGG Orthology database, release 56.

Figure 5

Relative percentages of reads matching gene categories in the marine vent virome and a set of 42 viral metagenomes isolated from other environments (listed in Dinsdale et al., 2008). The solid line indicates a 1:1 ratio between the vent metagenome and the other viral metagenomes. Metagenomes were analyzed with MG-RAST (Meyer et al., 2008) and annotated using the SEED subsystems database.