Statistical Analysis of Community RNA Transcripts between Organic Carbon and Geogas-Fed Continental Deep Biosphere Groundwaters

OBSERVATION

The deep biosphere is the largest biome on earth, where the continental subsurface alone hosts up to 6 × 10²⁹ cells from all three domains (1). Deep life has been demonstrated as active by, e.g., “viable/dead” PCR amplification (2), “omics” (3–5), and video evidence (6). A previous study at the Swedish Nuclear Fuel and Waste Management Company (SKB)-operated Äspö Hard Rock Laboratory (Äspö HRL) used a specially designed sampling device to fix cells under in situ conditions to ensure that RNA transcripts were unaffected by sampling procedures (3). In contrast, other studies used cell capture from flowing groundwater on filters over several days prior to fixation (see, e.g., reference 4). However, it is unknown if extended capture times alter the RNA transcript profile.

The extreme oligotrophy in the continental deep biosphere can limit cell numbers to 10¹ to 10⁷ cells/ml (1), while Äspö HRL groundwaters contain 10⁵ to 10⁶ cells/ml (7). Due to the difficulty of obtaining deep biosphere samples and the large water volume needed to extract sufficient RNA for sequencing, no omics studies have provided sufficient replicates for valid statistics.

In this study, we combined RNA transcript data from the sampling device (3) and from cells captured over several days on filter holders to evaluate if the two methods are comparable (see File S1 in the supplemental material). Additionally, we statistically analyzed gene transcript counts pertaining to active microbial taxa and their metabolic processes between groundwaters of various ages and origins.

The studied groundwaters were two modern marine waters (MM-171.3 and MM-415.2) that are replenished from the Baltic Sea and have a residence time of <20 years and a “thoroughly mixed” water (TM-448.4) that is composed of different waters of multiple origins and unknown age (3, 7, 8). Cells were captured, and community RNA was extracted and sequenced according to File S1. The small subunit (SSU) rRNA sequences (File S2) were annotated against the SILVA database and normalized as relative abundances (File S3). The MM-415.2 filter holder metatranscriptomes only had two replicates and thus cannot be statistically compared to the others. However, this groundwater was clearly different in both its SSU and protein-coding RNA (pcRNA) transcripts (Fig. 1) and is discussed in File S4. Nonmetric multidimensional scaling (NMDS) of SSU rRNA transcript beta diversity suggested that the three water samples were statistically different in their microbial communities (permutational multivariate analysis of variance [PERMANOVA] 9,999 permutations, P = 0.0011; Fig. 1). Previous analysis of the sampling device (SD) TM-448.4-4 sample showed it was different from the SD TM-448.4-3 sample, as it had likely been recently exposed to an electron donor (3). Repetition of the NMDS without this outlier altered the significance between the three groundwaters (P = 0.004). Without TM-448.4-4, the grouping supports the notion that (i) the two methods give highly similar RNA transcript patterns and, therefore, sampling with filter holders over several days is valid, and (ii) in the absence of periodic availability of an electron donor (as for the SD TM-448.4-4 sample [3]), the deep biosphere communities were stable for a minimum of 2 years.

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FIG 1

(A and B) Taxonomic annotation of the SSU rRNA (A) and protein-coding RNA (B) sequences showing stacked bars of the taxonomic phyla and Proteobacteria classes (Betaproteobacteriales shown separately) with a relative abundance of >0.1% for the modern marine (MM-171.3 and MM-415.2) and thoroughly mixed (TM-448.4) groundwaters. Rare taxa with a relative abundance of <0.1% are given as “other phyla.” (C and D) NMDS Bray-Curtis dissimilarity (beta diversity) plots based on the SSU rRNA at the lowest taxonomic level that could be assigned to the SILVA database using the Ribosomal Database Project classifier (C) and a second NMDS without the SD TM-448.3-4 (KA3385A-1) outlier (D). (E and F) NMDS Bray-Curtis plots based on the annotated transcripts (i.e., UniProtKB identifiers) with an E value of <10 and TPM of >100 from the full data set (E) and without the SD TM-448.4-4 outlier (F). The sampling methods are filter holders (FH) and sampling device (SD). Cand, Candidatus.

SSU rRNA-based phylogeny from all analyzed metatranscriptomes showed that a broad range of phyla from all three domains of life were viable and had protein-synthesizing potential (3) (Fig. 1). It also reinforced that the deep biosphere contains a large relative proportion of active candidate phyla from all three domains (e.g., Patescibacteria) along with many unclassified sequences. Statistically valid differences between the MM-171.3 and TM-448.4 groundwaters included sulfate-reducing bacteria (SRB) with Desulfobulbaceae in the MM-171.3 groundwater compared to Desulfobacteraceae and Desulfurivibrio in the TM-448.4 groundwater (File S5). This confirms that sulfur compound reduction is prevalent (see, e.g., references 9 and 10) with the predominantly organoheterotrophic SRB Desulfobulbaceae (11) in the MM-171.3 groundwater compared to autotrophic Desulfurivibrio spp. (12) in the ultraoligotrophic TM-448.4 water. In addition, increased 16S rRNA gene transcripts in the TM-448.4 groundwater that aligned within the Syntrophus genus demonstrated that syntrophy is likely to be an important survival strategy in these oligotrophic groundwaters (13).

Analysis of pcRNA transcripts identified 973 unique prokaryote genes (File S6). The NMDS analysis also showed that the community-level transcription profiles were statistically different (P = 0.002; Fig. 1), and further removal of the SD TM-448.4-4 outlier gave a P value of 0.004. Altogether, 410 prokaryotic genes had significant differential expression between the MM-171.3 and TM-448.4 groundwaters (false-discovery rate [FDR] < 0.05; E value < 0.001). Transcripts encoding tricarboxylic acid (TCA) cycle (mdh, fumC, and sucC) and ATP synthase (atpAG) proteins had higher transcript counts in the MM-171.3 groundwater, while increased TM-448.4 transcripts encoded, e.g., ribosomal (e.g., rpmB, rpsBK, and rplC) and stress/repair (e.g., dfx, recGN, cspAB, clpPX, dnaK, and hspC4) proteins. Additionally, a qualitative comparison of the SD TM-448.4-4 outlier (3) with the other three replicates suggested that this outlier had more transcripts involved with, e.g., replication and metabolic processes. Overall, most overexpressed transcripts were seen in the MM-171.3 groundwater, robustly demonstrating that this community was actively growing while the TM-448.4 populations were in “metabolic standby” (3).

The metabolic process with the greatest number of statistically different MM-171.3 groundwater transcripts was methanogenesis from CO₂ (fwdC, mtrACDEH, and mcrABCG genes) attributed to Methanothermobacter spp. (14) within the Euryarchaeota (Fig. 2 and File S7 and S8). Sulfur oxidation coupled to nitrate reduction was also important with increased pcRNA transcripts attributed to Sulfurimonas denitrificans (15) and Thiobacillus denitrificans (16) in the MM-171.3 and TM-448.4 groundwaters, respectively (File S9). This difference was potentially because S. denitrificans can use, e.g., formate, while T. denitrificans is an obligate sulfur compound-oxidizing chemolithoautotroph that had statistically increased cbbLS transcripts encoding CO₂ fixation via the Calvin-Benson-Bassham cycle. Consistent with the SSU rRNA data, the pcRNA transcripts had significant differences in the SRB. These included transcripts from 37 genes attributed to the Desulfobulbaceae that were only present in the MM-171.3 water, while dissimilatory sulfate-reducing genes aprA, dsrA, and dsvAB attributed to Desulfovibrio spp. were increased in the TM-448.4 groundwater (Fig. 2 and File S8). The importance of syntrophy was also further demonstrated by pcRNA transcripts in both the MM-171.3 and TM-448.4 waters attributed to Syntrophus aciditrophicus that grows alongside H₂ utilizers (17) predominantly present in the TM-448.4 groundwater (File S9). Finally, earlier observations of cyanobacteria in ancient deep terrestrial groundwaters (18, 19) were confirmed by increased Synechocystis pcRNA transcripts in the TM-448.4 water, also demonstrating their viability in these habitats.

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FIG 2

(A and B) Average of the significantly different (false-discovery rate [FDR] < 0.05; E value < 0.001) transcripts per million sequences (TPM) for the modern marine (MM-171.3) and thoroughly mixed (TM-448.4) groundwaters for protein-coding RNA transcripts assigned to methanogenic taxa (left) and genes attributed to methanogenesis from CO₂ (fwdC, mtrACDEH, mcrABCG; right) (A), as well as protein-coding RNA transcripts assigned to sulfate-reducing bacteria taxa (left) and genes attributed to sulfate reduction (cytochrome c₃, rd2, aprA, dsrA, and dsvAB; right) (B).

This work presents for the first time a statistically robust omics study of deep subsurface crystalline rock groundwaters with different depths and geochemical characteristics. We conclude that cell capture over several days does not alter RNA transcript profiles compared to rapid in situ fixation in this extremely oligotrophic environment. Importantly, this analysis of the two methods validates published studies that have used capture times prior to RNA fixation over the several days needed to obtain sufficient biomass for biomolecule extraction from low-cell-density deep groundwaters. The similarity of the data obtained by the two methods was likely due to the long-term and stable oligotrophic conditions in the respective groundwaters. These novel findings also provide evidence on how the differences in active communities and metabolic processes are influenced by organic carbon versus geogas-fed modern marine and thoroughly mixed groundwaters, respectively. This benchmarking of deep biosphere metatranscriptome analyses paves the way for future and still-needed exploration of the living deep biosphere in a statistically sound way.