Making a living while starving in the dark: metagenomic insights into the energy dynamics of a carbonate cave

Abstract

Carbonate caves represent subterranean ecosystems that are largely devoid of phototrophic primary production. In semiarid and arid regions, allochthonous organic carbon inputs entering caves with vadose-zone drip water are minimal, creating highly oligotrophic conditions; however, past research indicates that carbonate speleothem surfaces in these caves support diverse, predominantly heterotrophic prokaryotic communities. The current study applied a metagenomic approach to elucidate the community structure and potential energy dynamics of microbial communities, colonizing speleothem surfaces in Kartchner Caverns, a carbonate cave in semiarid, southeastern Arizona, USA. Manual inspection of a speleothem metagenome revealed a community genetically adapted to low-nutrient conditions with indications that a nitrogen-based primary production strategy is probable, including contributions from both Archaea and Bacteria. Genes for all six known CO2-fixation pathways were detected in the metagenome and RuBisCo genes representative of the Calvin-Benson-Bassham cycle were over-represented in Kartchner speleothem metagenomes relative to bulk soil, rhizosphere soil and deep-ocean communities. Intriguingly, quantitative PCR found Archaea to be significantly more abundant in the cave communities than in soils above the cave. MEtaGenome ANalyzer (MEGAN) analysis of speleothem metagenome sequence reads found Thaumarchaeota to be the third most abundant phylum in the community, and identified taxonomic associations to this phylum for indicator genes representative of multiple CO2-fixation pathways. The results revealed that this oligotrophic subterranean environment supports a unique chemoautotrophic microbial community with potentially novel nutrient cycling strategies. These strategies may provide key insights into other ecosystems dominated by oligotrophy, including aphotic subsurface soils or aquifers and photic systems such as arid deserts.

Figure 1

Map of Kartchner Caverns. The map indicates the location of tour trails (Rotunda and Big Room trails), the Echo Passage and Big Wall sites (BW2) used for metagenomic and qPCR analysis in the current study, and the sites sampled in previous studies (BW1 and SA). The insert shows the complex cave formation sampled for the Echo Passage speleothem metagenome analysis in Kartchner Caverns. The surfaces of stalactites a, b and c were swabbed for the Echo Passage metagenome analysis.

Figure 2

Analysis of drip-water samples collected monthly from January to December 2011 below a stalactite in the Big Wall room (Figure 1, BW2). Drip-water flow rates and drip-water dissolved organic carbon and nitrate–nitrogen concentrations are represented.

Figure 3

Taxonomic affiliations for Echo Passage metagenomic sequences. Taxonomic associations were determined by a combination of BLASTX analysis of all the Echo Passage sequences against the NCBI-NR database and the MEGAN software. (a) Domain distribution; (b) bacterial distribution; (c) archaeal distribution; and (d) eukaryotic distribution. Percentage values represent the genes assigned to a particular taxon relative to the total number of genes assigned to that specific category.

Figure 4

qPCR analysis of the SSU rRNA genes of bacteria, archaea and fungi in cave and soil samples. Cave samples included dry rock (DRW) and wet calcite-coated rock (WRW) walls and two speleothems (A and B) that were all located near the Big Wall area (BW2) of Kartchner Caverns. Soil samples were collected near the sink hole next to the natural cave entrance (SH), above the Rotunda Room portion of the cave (OR) and next to the rain gauge (RG) in the saddle between the two hills over the cave. Bars for archaea labeled with different letters represent samples with significantly different 16S rRNA copy numbers (two-tailed t-test; P<0.01). No significant differences were observed for bacteria. Fungi were below the detection level for cave samples.

Figure 5

Hierarchical cluster analysis of the 16 selected metagenomes. The tree was generated using an agglomerative algorithm based on pair-wise comparisons of the 16 metagenomes. This comparative metagenomic tool is provided by the IMG/M ER pipeline that used the predicted COG functions for each metagenome.

Figure 6

Average relative abundance of COG categories for cave, deep ocean, bulk soil and rhizosphere soil metagenomes. (a) COG categories for which cave metagenomes showed no significant difference to any of the other three habitats. Letters representing COG categories: energy production and conversion (C); cell cycle, cell division and chromosome partitioning (D); amino acid transport and metabolism (E); lipid transport and metabolism (I); cell wall, membrane and envelope biogenesis (M); cell motility (N); inorganic ion transport and metabolism (P); secondary metabolites biosynthesis, transport and catabolism (Q); function unknown (S); and intracellular trafficking, secretion and vesicular transport (U). (b) COG categories for which cave metagenomes differed significantly from at least one of the other three habitats. Letters representing COG categories: nucleotide transport and metabolism (F); carbohydrate transport and metabolism (G); coenzyme transport and metabolism (H); translation, ribosomal structure and biogenesis (J); transcription (K); replication, recombination and repair (L); post-translational modification, protein turnover and chaperones (O); general function prediction only (R); signal transduction mechanisms (T); and defense mechanisms (V). Bars represent 1 s.d. from the mean. Environments labeled with different letters within a COG category differ significantly in gene abundance (ANOVA, n=4, P<0.05).

Figure 7

Heat map showing average relative abundance of COGs representing key carbon fixation enzymes identified in the cave, deep ocean, bulk soil and rhizosphere soil metagenomes. COG IDs: COG1850, RuBisCO, key enzyme for the CBB; COG2301, ATP–citrate lyase, enzyme representing the Arnon–Buchanan cycle (rTCA) and COG2368, 4-hydroxybutyryl dehydratase, key enzyme for the HP/HB and DC/HB cycles. Values labeled with different letters within a single COG differ significantly in gene abundance (ANOVA, n=4, Tukey–Kramer HSD, P<0.05). HSD, honestly significant difference; rTCA, reverse tricarboxylic acid.

Figure 8

Heat map showing average relative abundance of COGs for putative nitrogen metabolism genes identified in the cave, deep ocean, bulk soil and rhizosphere soil metagenomes. COG IDs: COG0004, ammonia permease; COG1140, nitrate reductase beta subunit; COG1251, NAD(P)H-nitrite reductase; COG1348, nitrogenase reductase subunit NifH (ATPase); COG2146, nitrite reductase–ferredoxin; COG2223, nitrate/nitrite transporter; COG3180, putative ammonia monooxygenase; COG4263, nitrous oxide reductase; and COG5013, nitrate reductase alpha subunit. Values labeled with different letters within a single COG differ significantly in gene abundance (ANOVA, n=4, Tukey–Kramer HSD, P<0.05). HSD, honestly significant difference.