Metagenome analysis of an extreme microbial symbiosis reveals eurythermal adaptation and metabolic flexibility

Abstract

Hydrothermal vent ecosystems support diverse life forms, many of which rely on symbiotic associations to perform functions integral to survival in these extreme physicochemical environments. Epsilonproteobacteria, found free-living and in intimate associations with vent invertebrates, are the predominant vent-associated microorganisms. The vent-associated polychaete worm, Alvinella pompejana, is host to a visibly dense fleece of episymbionts on its dorsal surface. The episymbionts are a multispecies consortium of Epsilonproteobacteria present as a biofilm. We unraveled details of these enigmatic, uncultivated episymbionts using environmental genome sequencing. They harbor wide-ranging adaptive traits that include high levels of strain variability analogous to Epsilonproteobacteria pathogens such as Helicobacter pylori, metabolic diversity of free-living bacteria, and numerous orthologs of proteins that we hypothesize are each optimally adapted to specific temperature ranges within the 10-65 degrees C fluctuations characteristic of the A. pompejana habitat. This strategic combination enables the consortium to thrive under diverse thermal and chemical regimes. The episymbionts are metabolically tuned for growth in hydrothermal vent ecosystems with genes encoding the complete rTCA cycle, sulfur oxidation, and denitrification; in addition, the episymbiont metagenome also encodes capacity for heterotrophic and aerobic metabolisms. Analysis of the environmental genome suggests that A. pompejana may benefit from the episymbionts serving as a stable source of food and vitamins. The success of Epsilonproteobacteria as episymbionts in hydrothermal vent ecosystems is a product of adaptive capabilities, broad metabolic capacity, strain variance, and virulent traits in common with pathogens.

Fig. 1.

Rarefaction curves for SSU rRNA genes and selected proteins in the EM. Rarefaction curves were determined for the SSU rRNA gene (216 bases, 79 sequences at 99% similarity) and the following proteins: recombinase subunit A (recA, 280 bases, 43 sequences at 99% similarity), DNA gyrase subunit B (gyrB, 239 bases, 34 sequences at 99% similarity), heat shock protein 60 (groEL, 167 bases, 34 sequences at 98% similarity), ribosomal protein S12 (rpS12, 233 bases, 28 sequences at 99% similarity), and malate dehydrogenase (mdh, 263 bases, 20 sequences at 99% similarity).

Fig. 2.

COG distributions in the EM-C dataset compared with 3 pathogenic epsilonproteobacteria (bold, capitalized letters) and 3 free-living bacteria (lowercase letters). The letters indicate the specific COG role category.

Fig. 3.

Model of predicted metabolic processes in the episymbiont cell based on annotation of the EM-C. The KEGG database was used as a basis for metabolic reconstruction.

Fig. 4.

Homology based 3D models of the L19 ribsomal protein. Proteins are from T. thermophilus, H. pylori J99, and Sulfurovum sp. (A) and from 2 ecoparalogs in the EM (B). Each amino acid is mapped onto the structure based on its BLOSUM60 substitution probability in the alignment where blue is an identical or conserved substitution and white and increasing values of red are nonconserved amino acid changes. The alignment is Fig. S10.