| Literature DB >> 22084639 |
Rika E Anderson1, William J Brazelton, John A Baross.
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.Entities:
Keywords: deep subsurface biosphere; hydrothermal vents; microbial evolution; viral ecology
Year: 2011 PMID: 22084639 PMCID: PMC3211056 DOI: 10.3389/fmicb.2011.00219
Source DB: PubMed Journal: Front Microbiol ISSN: 1664-302X Impact factor: 5.640
Summary of previous work on viral abundance, activity, and diversity in various environments of the deep subsurface biosphere, deep ocean, and sediments.
| Environment | Work on viruses to date | Reference |
|---|---|---|
| Surface marine sediments | High viral production in benthic ecosystems: may be responsible for up to 80% of cell mortality, thus releasing large amounts of carbon through the “viral shunt.” Viral diversity in sediments is fairly high, and showed a higher incidence of lysogenic than lytic phages | Danovaro et al. ( |
| Deep sediments | Viral and bacterial abundance and production decrease exponentially with depth in sediments, up to 96 mbsf | Middelboe et al. ( |
| Mitomycin C experiments revealed that 46% of isolates contained inducible prophage | ||
| Deep basalt | None known | |
| Deep granitic groundwater | Viruses are present and correlated with bacterial abundance (ratio of ∼10:1), similar to many surface environments | Kyle et al. ( |
| Diffuse flow hydrothermal fluid | Lysogeny appears to be a dominant lifestyle among vent viruses; viruses in diffuse flow are capable of infecting a wide range of hosts across domains and thermal regimes | Williamson et al. ( |
| Cold seeps/methane hydrates | Viral activity and abundance vary among seeps, with a virus to prokaryote ratio ranging between relatively low (<0.1) to relatively high (66.36) | Middelboe et al. ( |
| Deep-water column | Viral abundance generally tracks bacterial abundance, but the virus:cell ratio at depth varies. In some areas, the ratio increases with depth | Hara et al. ( |
| Metagenomic work characterizing viral diversity found most viral sequences had matches to bacteriophages in the |
Figure 1Schematic 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 2Number 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).
Habitat, thermal regime, number of CRISPR loci, and number of prophage for archaea and bacteria isolated from the deep-water column, marine sediments, or hydrothermal systems whose genomes have been sequenced.
| Name | No. CRISPR loci | No. prophage | Optimal growth temperature (°C) | Habitat | Reference |
|---|---|---|---|---|---|
| 2 | 2 | 70 | Hydrothermal chimney | Reysenbach et al. ( | |
| 20 | 0 | 85 | Hydrothermal chimney | Jones et al. ( | |
| 23 | 0 | 85 | Hydrothermal fluid | Mehta and Baross ( | |
| 5 | 0 | 122 | Hydrothermal fluid | Takai et al. ( | |
| 7 | 1 | 60–65 | Hydrothermal chimney | Takai et al. ( | |
| 4 | 0 | 85 | Hydrothermal chimney | Marteinsson et al. ( | |
| 3 | 3 | 88 | Hydrothermal chimney | Jolivet et al., | |
| 4 | 1 | 60–65 | Hydrothermal chimney | Takai et al. ( | |
| 0 | 3 | 43 | Hydrothermal fluid | Hou et al. ( | |
| 5 | 2 | 67.5 | Hydrothermal chimney | Sako et al. ( | |
| 0 | 1 | 41–45 | Polychaete worm | Campbell et al. ( | |
| 2 | 3 | 37 | Hydrothermal chimney | Nakagawa et al. ( | |
| 1 | 1 | 60 | Hydrothermal chimney | Miroshnichenko et al. ( | |
| 0 | 4 | 30 | Deep sea sediment | Lu et al. ( | |
| 2 | 3 | 10 | Deep sea sediment | Nogi et al. ( | |
| 1 | 5 | 15–20 | Deep sea sediment | Wang et al. ( | |
| 1 | 1 | 8 | Deep sea sediment | Kato et al. ( | |
| 0 | 1 | 25 | High temperature sediment | Inagaki et al. ( | |
| 6 | 7 | 68 | Deep sea sediment | Lee et al. ( | |
| 2 | 2 | 25–30 | Deep sea sediment | Qin et al. ( | |
CRISPR loci were identified with CRISPRFinder (Grissa et al., .
Percent of reads matching a DNA ligase and a transposase domain in 19 sequenced metagenomes.
| Metagenome | Type of metagenome | Percent of reads matching a lysogeny domain (rank) | Percent of reads matching a DNA ligase | MG-RAST ID number | Reference |
|---|---|---|---|---|---|
| Marine vent virome (subset) | Viral | 0.073 (6) | 1.245 | Subset of 4448187.3 | Anderson et al. ( |
| Marine vent virome (full) | Viral | 0.067 (7) | 0.594 | 4448187.3 | Anderson et al. ( |
| Farm soil | Microbial | 0.192 (4) | 0.131 | 4441091.3 | Tringe et al. ( |
| Acid mine drainage | Microbial | 0.468 (1) | 0.109 | 4441137.3, 444138.3 | Tyson et al. ( |
| Yellowstone hot springs | Viral | 0.104 (5) | 0.085 | 4443745.3, 4443746.3, 4443747.3, 4443762.3, 4443749.3, 4443750.3 | Bhaya et al. ( |
| Whale fall | Microbial | 0.332 (2) | 0.070 | 4441619.3, 4441656.4, 4441620.3 | Tringe et al. ( |
| Lost city hydrothermal field | Microbial | 0.291 (3) | 0.069 | 4461585.3 | Brazelton and Baross ( |
| Microbial | 0.044 (9) | 0.068 | 4441102.3 | Grzymski et al. ( | |
| Peru margin sediments, 32 mbsf | Microbial | 0.020 (11) | 0.047 | 4459940.3 | Biddle et al. ( |
| Peru margin sediments, 1 mbsf | Microbial | 0.017 (13) | 0.044 | 4440961.3 | Biddle et al. ( |
| Arctic Ocean | Viral | 0.020 (12) | 0.018 | 4441622.3 | Angly et al. ( |
| Peru margin sediments, 16 mbsf | Microbial | 0.005 (18) | 0.018 | 4440973.3 | Biddle et al. ( |
| Peru margin sediments, 50 mbsf | Microbial | 0.016 (14) | 0.016 | 4459941.3 | Biddle et al. ( |
| Fishgut | Microbial | 0.023 (10) | 0.010 | 4441695.3 | Dinsdale et al. ( |
| Gulf of Mexico | Viral | 0.008 (15) | 0.006 | 4441625.4 | Angly et al. ( |
| Bay of British Columbia | Viral | 0.006 (17) | 0.004 | 4441623.3 | Angly et al. ( |
| Microbialites | Viral | 0.063 (8) | 0.003 | 4440320.3, 4440321.3, 4440323.3 | Desnues et al. ( |
| Sargasso Sea | Viral | 0.004 (19) | 0.002 | 4441624.3 | Angly et al. ( |
| Microbialites | Microbial | 0.008 (16) | 0 | 4440061.3 | Breitbart et al. ( |
Metagenomes are ordered according to the percent of reads matching a DNA ligase, with the highest percentage at the top. Column 3 lists the percentage of reads to a lysogeny-related domain, followed by the rank according to the percentage of reads with a hit among other metagenomes. The marine vent virome subset consists of all reads within contigs with a coverage of eight or greater plus all reads within contigs labeled “unknown” (Anderson et al., .
Figure 3Maximum-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 4Relative percentages of reads matching gene categories in the marine vent virome (Anderson et al., Xie et al. (. 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 5Relative 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., . 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.