TY - JOUR
AU1 - Purkamo,, Lotta
AU2 - Kietäväinen,, Riikka
AU3 - Miettinen,, Hanna
AU4 - Sohlberg,, Elina
AU5 - Kukkonen,, Ilmo
AU6 - Itävaara,, Merja
AU7 - Bomberg,, Malin
AB - ABSTRACT The diversity and metabolic functions of deep subsurface ecosystems remain relatively unexplored. Microbial communities in previously studied deep subsurface sites of the Fennoscandian Shield are distinctive to each site. Thus, we hypothesized that the microbial communities of the deep Archaean bedrock fracture aquifer in Romuvaara, northern Finland, differ both in community composition and metabolic functionality from the other sites in the Fennoscandian Shield. We characterized the composition, functionality and substrate preferences of the microbial communities at different depths in a 600 m deep borehole. In contrast to other Fennoscandian deep biosphere communities studied to date, iron-oxidizing Gallionella dominated the bacterial communities, while methanogenic and ammonia-oxidizing archaea were the most prominent archaea, and a diverse fungal community was also detected. Potential for methane cycling and sulfate and nitrate reduction was confirmed by detection of the functional genes of these metabolic pathways. Organotrophs were less abundant, although carbohydrates were the most preferred of the tested substrates. The microbial communities shared features with those detected from other deep groundwaters with similar geochemistry, but the majority of taxa distinctive to Romuvaara are different from the taxa previously detected in saline deep groundwater in the Fennoscandian Shield, most likely because of the differences in water chemistry. deep continental biosphere, granitic bedrock, Fennoscandian Shield, microbial diversity, functional genes, qPCR INTRODUCTION The deep subsurface hosts microbial life of great diversity and volume (e.g. Whitman, Coleman and Wiebe 1998; Kallmeyer et al.2012; McMahon and Parnell 2014). Although recent work has been gradually shedding light on the deep biosphere, it is still one of the least-known frontiers on Earth. The ecology and functionality of the microbial communities in terrestrial subsurface began to gain more attention only about a decade ago, whereas marine subsurface has been studied more extensively (Gihring et al.2006; Lin et al.2006; Chivian et al.2008; Pedersen 2012a; Orcutt et al.2013; Nyyssönen et al.2014). The fundamental questions are related to energy mechanisms and carbon sources of deep subsurface microorganisms, and their involvement in cycling of the essential elements of life on Earth. Chemolithotrophic metabolisms were the first to be considered to be fueling the deep biosphere, where oxygen is depleted and inorganic electron donors and acceptors are more abundant (Stevens and McKinley 1995; Pedersen 1997; Lin et al.2005; Nealson, Inagaki and Takai 2005). However, while deep crystalline bedrock environments are typically scarce in organic carbon, studies have shown that microbial communities leading a heterotrophic lifestyle are common nonetheless (Nyyssönen et al.2014; Purkamo et al.2015; Bomberg, Lamminmäki and Itävaara 2016; Wu et al.2016). Another intriguing question involves the spatial and temporal distribution of different microbial species in deep bedrock. There are studies showing that despite a clear geographic separation, some deep subsurface microbial communities share a notable number of microbial genera (Inagaki et al.2006; Magnabosco et al.2014). Furthermore, methanogenic microbes are shown to be distributed according to depth in Precambrian bedrock (Kietäväinen and Purkamo 2015), however there are also studies that show a scattered distribution (Hallbeck and Pedersen 2012). Sulfate reducing bacteria (SRB) and methanogenic archaea are commonly found in the deep terrestrial subsurface in the Fennoscandian Shield (Pedersen et al.2008; Hallbeck and Pedersen 2012; Nyyssönen et al.2012; Purkamo et al.2013; Bomberg et al.2015). Indeed, methane cycling processes such as methanogenesis and anaerobic methane oxidation have been shown to date back to the mid-Paleozoic (Drake et al.2017). Methane and methanol were shown to activate microbial metabolic activity in deep fracture water in Outokumpu, Finland (Rajala et al.2015; Rajala and Bomberg 2017). Carbon dioxide has also been shown to function as carbon source to heterotrophic bacteria in Outokumpu groundwater at 180 m depth (Bomberg et al.2017) and Purkamo et al. (2015) showed the presence of biotin carboxylase genes (accC) throughout the borehole water column to a depth of 2500 m in the Outokumpu deep scientific drill hole. Kutvonen et al. (2015) showed that at 100 m depth in Olkiluoto, Finland, bacterial communities used nitrate and ammonia as their N source. In addition, nitrate was reduced and ammonium oxidized by different bacterial groups compared to the nitrate and ammonium assimilating community. The ecology and diversity of eukaryotes in the deep biosphere is still understudied compared to prokaryotic microbial life (Colwell and D’Hondt 2013). While numerous studies have characterized the bacterial and archaeal communities at depths, eukaryotic life in these surroundings has only recently gained more attention (Borgonie et al.2011; Orsi, Biddle and Edgcomb 2013; Sohlberg et al.2015; Bengtson et al.2017; Drake and Ivarsson 2017; Drake et al.2017). Fungi have been shown to form synergistic consortia with SRB in deep granitic subsurface, where the role of the fungi is to provide H2 for the sulfate reducers (Drake et al.2017). The fungi may also collect carbohydrates for their own heterotrophic metabolism by degrading dead microbial biofilms (Drake and Ivarsson 2017). Metabolizing fungi release small carbon compounds that can function as electron donors and carbon substrates for bacteria and archaea. By detection of unspliced rRNA-ITS transcripts, it has been shown that the fungi in deep Fennoscandian bedrock groundwater are active and that the fungal communities react to changes in the concentration of carbon sources in their environment (Sohlberg et al.2015; Bomberg et al.2017). The deep biosphere of the Fennoscandian Shield has been studied in several different locations in Finland and in Sweden (Haveman, Pedersen and Ruotsalainen 1999; Chi Fru 2008; Hallbeck and Pedersen 2012; Nyyssönen et al.2012, 2014; Bomberg et al.2015; Sohlberg et al.2015; Hubalek et al.2016; Wu et al.2016; Purkamo et al.2016). Many of the previously studied sites are characterized by ancient, saline formation fluids (Outokumpu, Pyhäsalmi) (Kietäväinen et al.2013, 2014; Miettinen et al.2015). In other sites close to the shore, the evolution of the Baltic Sea can still be detected from the groundwater chemistry (Olkiluoto) (Pitkänen, Snellman and Vuorinen 1996). Groundwater affected by glaciation can be detected in great depths in both near-shore and inland locations (Olkiluoto, Hästholmen, Palmottu) (Blomqvist 1999; Stotler et al.2012). Shallow groundwater, on the other hand, can be traced back to infiltrated modern seawater and meteoric water with recharge times of some weeks to years (Äspö) (Hubalek et al.2016; Wu et al.2016). The short recharge times in Äspö are also influenced by the tunnel drawdown (Mathurin et al.2012). The common trend is that the deeper the samples originate from, the longer the residence times of the fluids (Kietäväinen et al.2014; Wu et al.2016). The studied drill hole in Romuvaara reaches a depth of 600 m in Archaean bedrock with the oldest rocks from 3.1 Ga ago, but groundwater in the drill hole is fresh, similar to deep granitic bedrock fluids at Kivetty and Lavia sites in Finland (Lahermo and Lampén 1987; Haveman, Pedersen and Ruotsalainen 1999; Kietäväinen 2017). Our goal is to shed light on the microbiology of deep granitic groundwater, recharged by percolation of meteoric water through rocks that remain particularly understudied to date (Griebler and Lueders 2009; Konno et al.2013). The aim is to describe the microbial community structure at different depths in a 600 m deep drill hole using molecular biological methods and characterize the biogeochemical drivers forming these communities. In addition, we aim to quantify important metabolic functions, such as sulfate reduction, methanogenesis and methane oxidation, dark carbon fixation and nitrate reduction and nitrification in the groundwater fluids in the Archaean bedrock in Eastern Finland. We hypothesize that due to the different formation mechanism of the groundwater and the predominant water chemistry, the microbial communities in the deep subsurface of Romuvaara would differ both in microbial community composition and metabolic functionality from the other Precambrian sites of the Fennoscandian Shield (Nyyssönen et al.2014; Bomberg et al.2015; Purkamo et al.2015, 2016). By comparing the microbial communities in deep groundwater in Romuvaara to other deep bedrock sites we expect to clarify the biogeographical trends in these environments. MATERIALS AND METHODS Site description and sample collection The 600 m deep drill hole RO-KR10, located in Eastern Finland (N 64° 13′ 5,330″, E 29° 56′ 40,721″) allowed sampling of groundwater from deep Archaean bedrock (Fig. 1). The area mainly consists of migmatitic tonalite gneisses, granitoids (tonalite, granodiorite and quartz diorite), minor amphibolites and metadiabase dykes, along with metavolcanites and metasediments of the Kuhmo greenstone belt. The age of the rocks in the area ranges between 3.1 to 2.10 Ga with major crustal growth between 2.83 and 2.68 Ga (Anttila et al. 1999; Käpyaho, Mänttäri and Huhma 2006). The lithology in the deep drill hole consists of tonalite gneisses (main minerals are plagioclases, quartz, K-feldspar and biotite), leucotonalite gneisses and amphibolites (Fig. 1). In RO-KR10, the most probable sources of the fluid flow are two fracture zones, one in the topmost part of the drill hole, matching to the shallowest sampling depth and the other in the bottom of the drill hole, equivalent of the deepest sampling depth (Luukkonen, Kuusela-Lahtinen and Front 1997). Temperature and salinity anomalies and high water conductivity were detected at these depths after the drilling of the hole (Luukkonen, Kuusela-Lahtinen and Front 1997). Based on these observations, the fracture zones are most likely affecting the dynamics of the fluid flow in the studied drill hole. Figure 1. Open in new tabDownload slide Sampling site and lithology of the drill hole (mbls: meters below land surface). Figure 1. Open in new tabDownload slide Sampling site and lithology of the drill hole (mbls: meters below land surface). Groundwater samples were collected in August 2012 with the tube sampling method described in Nurmi and Kukkonen (1986). This method has been successfully used in characterization of geochemistry and microbiology in other deep drill holes (Itävaara et al.2011; Purkamo et al.2015). The factory-clean polyamide tube consisted of twelve 50 m sections connected to each other with ball valves (Swagelock). The end of the tube was equipped with a backpressure valve. The tube was lowered into the drill hole slowly in order to allow the drill hole fluid to fill the tube. When pulling the 600 m long tube back to the ground level, the water column profile was maintained by immediately closing each ball valve as soon as it emerged at the drill hole mouth. All valves and tube sections were heat-sterilized before the sampling, and handling in the field was done with best practices to avoid any contamination from surface sources. Samples from the tube sections were divided into six 100 m sections and the upper 50 m part of each section was designated to chemical measurements and the lower 50 m part was for the molecular biology analyses. Samples were numbered 1–6 from surface to bottom. Electrical conductivity (EC), pH and oxygen concentration of the sample fluids were measured in the field using a hand held meter (WTW) equipped with applicable sensors. Alkalinity was determined by titration to pH 4.5 using a hand held device (HACH). Samples (100 mL each) for elemental analyses of cations were filtered through <0.45 µm filter in the field and acidified with 0.5 mL ultrapure HNO3. Samples (250 mL) for anion analyses were left untreated. In addition, samples (60 mL) filtered through <0.45 µm were taken for isotopic analysis of water. Biomass from duplicate 500 mL and single 100 mL groundwater samples were collected in anaerobic conditions in the field on to Sterivex filter units with 0.2 µm pore-size (Millipore). Groundwater samples were pushed directly from the sampling tube through a Sterivex filter attached to the end of the tube, using nitrogen gas pressure. Filters with biomass were placed in sterile 50 mL plastic test tubes, which were immediately frozen in the field in dry ice and subsequently kept at 80°C until the DNA extraction. In addition, 50 mL samples of groundwater for microscopy and BIOLOG-analyses were collected into sterile, anaerobic, acid-washed headspace bottles sealed with gas-tight butyl rubber stoppers (Bellco, USA) and aluminium crimp caps (Sigma). The water samples were kept on coolers at approximately +4°C during transportation to the laboratory and refrigerated upon arrival. Geochemical analyses Elemental analyses were done with ICP-MS (Perkin Elmer and Agilent Technologies) and ICP-OES (Thermo Jarrell Ash Corp.). Anions (Br, Cl, F, SO4 and NO3) were analyzed with ion chromatography. EC and pH were measured again with potentiometric analysis and alkalinity with titrimetric analysis. All geochemical laboratory analyses were conducted by an external laboratory (Labtium Oy, Espoo, Finland). The stable H and O isotopes of water were determined by a Cavity Ring-Down Spectrometer (CRDS, Picarro) at the Geological Survey of Finland. Analytical uncertainty is <0.1‰ for oxygen and <0.5‰ for hydrogen. The results are reported relative to Vienna Standard Mean Ocean Water (VSMOW). Enumeration of microbial cells with microscopy The number of microbial cells in the samples were determined with fluorescent staining with 4′-6 -diamidino-2-phenylindole (DAPI) as described in Purkamo et al. (2013). In brief, two replicate samples (5 mL each) from each sampling depth were stained with 50 µL of 2.5 mg mL−1 of DAPI (Sigma) suspended in 200 µL of 2.5% glutaraldehyde (Merck, NJ). Staining was conducted in dark at room temperature for 15 min. The stained cells were collected on a 0.2 µm GTPB membrane filter (Millipore, Billercia, MA) using a Millipore filtering unit and rinsed twice with 1 mL of filter-sterilized 0.9% NaCl. The membranes were placed on microscopy slides and examined with an epifluorescence microscope (Olympus BX60, Olympus Optical Ltd, Tokyo, Japan) with the CellP software (Olympus Optical Ltd.). Cell numbers were counted from 30 random microscopy fields and total number of cells (TNCs) in the samples was determined from an average of two replicate samples. Carbon metabolism fingerprint of microbial communities The anaerobic utilization of carbon and nitrogen sources by the microbial communities throughout the depth profile of the borehole was tested using the BIOLOG AN plate (Biolog, Inc., Hayward, CA). Altogether 95 different organic carbon and nitrogen sources were tested for their suitability to the microbial communities (Supplementary data 1, Supporting Information). Two parallel BIOLOG AN plates were prepared for each sample, and 100 µL of sample water was pipetted into each well of BIOLOG AN plate in an anaerobic chamber. The plates were inserted into gas-tight anaerobic bags (BG company) containing an oxygen scavenger pouch and oxygen indicator. The BIOLOG AN plates were protected from oxygen and light, and incubated for 38 days at +14°C . During the incubation, wells were visually observed weekly for any color change in the medium. Extraction of DNA After briefly thawing the Sterivex filter units on ice, microbial DNA was extracted directly from the filter units using the MoBio PowerWater Sterivex kit according to the manufacturer’s instructions (MoBio Laboratories, Carlsbad, CA, USA). The DNA was eluted in 100 µL of elution buffer S7. A sterile Sterivex filter was used as a negative reagent control for the DNA extraction, and was treated in the same way as the sample-containing filters. Quantitative analysis of marker gene copy numbers Microbial communities were screened for metabolic pathways possibly present in the deep subsurface by quantifying the functional marker genes of sulfur, carbon and nitrogen cycles. Sulfate reducers were targeted by their dissimilatory sulfite reductase genes (dsrB), nitrogen reducers by the gene for nitrate reductase (narG), ammonia oxidizers by the ammonia monooxygenase gene (amoA), methanogens by the gene for methyl coenzyme M reductase (mcrA), methane oxidizers by the particulate methane monooxygenase gene (pmoA) and carbon dioxide fixing microorganisms by the biotin carboxylase gene (accC). DNA extracted from 100 mL samples was used as a template for qPCR. Used primers and thermal cycling conditions are described in Supplementary data 2 (Supporting Information). The qPCR mastermix included 1x KAPA SYBR FAST Roche LightCycler480 2x qPCR Master Mix (Kapa Biosystems, Woburn, MA, USA), 2.5 pmol of each primer and 1 µL of template DNA in 10 µL reactions. The qPCR reactions were run on the Roche LightCycler480 (Roche, Basel, Switzerland) in triplicate reactions for each sample. In the end of each assay, a melting curve analysis was done (Supplementary data 2, Supporting Information). Seven-fold dilution series of plasmid DNA containing the marker gene of each assay was used to determine the marker gene copy numbers in each sample (Supplementary data 2, Supporting Information). After each run, the slope of the standard curve, efficiency and error were determined (Supplementary data 2, Supporting Information). The copy numbers of each marker gene were calculated by comparing the crossing point value of a sample to the amplification result of the standard dilution series. Amplicon library preparation Amplicon libraries for high-throughput sequencing using the Genome Sequencer 454 FLX platform (454 Life Sciences/Roche Applied Biosystems, Branford, CT, USA) were prepared for characterization of the archaeal, bacterial and fungal communities separately. The bacterial amplicons were produced using primers 8F and P2 with a distinct barcode sequence for each sample covering the V1–V3 regions of the bacterial 16S rRNA gene (Supplementary data , Supporting Information) (Edwards et al.1989; Muyzer, de Waal and Uitterlinden 1993). The archaeal 16S rRNA gene amplicons were produced using a nested PCR approach with primers A109f and Arch915R (Stahl D. A. and Amann 1991; Grosskopf, Janssen and Liesack 1998) for the first round and barcoded primers ARC344f and Ar774r (Barns et al.1994; Bano et al.2004) covering the V3–V4 variable areas for the second round PCR. The fungal ITS amplicons were produced using a semi-nested PCR, where primers ITS1F and ITS4 (White et al.1990; Buée et al.2009) were used in the first round PCR and barcoded primers ITS1F and ITS2 (Buée et al.2009) were used in the second round PCR. For each reaction, 1 µL of template DNA was used. The PCR reaction mixture concentrations and thermal cycling conditions for bacterial and archaeal amplicon library preparation are described in Purkamo et al. (2016) and for fungal library in Sohlberg et al. (2015). To rule out possible contamination, control PCR reactions with molecular biology grade water as a template were included with each amplification batch. Amplification success was confirmed by gel electrophoresis and the amplicon libraries were sent for sequencing to Beckman Coulter Genomics (Danvers, MA, USA), where amplicon libraries were purified and smallest and largest fragments were removed based on fragment analysis. Sequence processing and analysis Sequence reads were first subjected to quality control using the mothur software (v. 1.39.5) (Schloss et al.2009). Raw reads were subjected to quality trimming and sequencing noise removal with mothur’s sff.multiple-command. Minimum flows were set to 200, maximum homopolymers to 8 and primer differences to 2, otherwise default settings were used. Bacterial and archaeal sequences were aligned with Silva seed alignment (v.128). Sequences of the ITS1 region were extracted from the total fungal dataset with the ITSx software (Bengtsson-Palme et al.2013). Resulting sequences were checked for chimeras with the chimera.slayer command. Pairwise distance with a cutoff of 0.03 was calculated for bacterial and archaeal sequences. Classification of bacterial and archaeal sequences and OTUs was done with the Silva version 128 taxonomy (Quast et al.2013; Yilmaz et al.2014). Pairwise distance for ITS sequences was calculated with a cutoff of 0.10 and sequences and OTUs were classified with UNITE ITS v. 7.2 dynamic taxonomy dated December 1st 2017 (Kõljalg et al.2013). The make.biom command was used for constructing .biom-files for subsequent analyses in QIIME (Caporaso et al.2010) and MEGAN6 (Huson et al.2016). Taxonomy summaries were visualized in QIIME (summarize_data_through_plots.py command). Sequence data were submitted to European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena/) under accession numbers ERS2371769-ERS2371774 (bacterial sequences), ERS2371775-ERS2371780 (archaeal sequences) and ERS2371763-ERS2371768 (fungal sequences). Alpha-diversity and statistical analyses of sequences Species richness and coverage estimates (Chao1, ACE), and overall community diversity indices (Shannon, Simpson 1-D) in addition to the number of observed OTUs were calculated from .biom-files using the alpha_diversity.py command in QIIME (MacQIIME v. 1.9.1, Caporaso et al.2010). MEGAN6 software (Huson et al.2016) was used for visualization of the data and building rarefaction curves for each sample. The possible correlation between 25 hydrogeochemical variables and most abundant archaeal, bacterial and fungal OTUs (on average > 1% of the community at each depth) was first tested with Pearson correlation. Further analysis of the relationship between microbial communities and geochemistry at different depths was done with canonical correlation. Statistical analyses were done in PAST software (v. 3.04) (Hammer, Harper and Ryan 2001). RESULTS TNCs and enumeration of different functional groups TNC in Romuvaara deep subsurface was on average 1.17 × 105 cells mL−1, ranging from 7.58 × 104 cells mL−1 at 350–400 m depth to 2.23 × 105 cells mL−1 at 550–600 m depth (Fig. 2). The highest concentrations of all the studied marker genes were detected in the deepest part of the drill hole (550–600 m) where the cell count was also the highest. In addition to the deepest drill hole section, dsrB gene copies were abundant also at the 250–300 m depth, on average 1.65 × 103 copies mL−1, representing 0.9% of the TNC at these depths. Lowest concentration of dsrB copies (1.71 × 102 mL−1) was detected at 450–500 m depth. The highest abundance of narG gene copies, 3.24 × 103 mL−1 in the deepest sample, represented 1.5% of the TNC. Copy numbers of the amoA gene for ammonia oxidation were only detected from the deepest sample at 550–600 m (9 gene copies mL−1). Methane cycling marker gene particulate methane monooxygenase (pmoA) represented 23.2% of the TNC with 5.18 × 104 copies mL−1, while the copy number of methyl coenzyme A reductase gene (mcrA) (2.46 × 103 copies mL−1) comprised 1.1% of the TNC at the deepest depth. AccC copy numbers were below 100 mL−1 at each depth. Figure 2. Open in new tabDownload slide TNC and functional marker gene copy numbers in Romuvaara drill hole. Six samples (1–6) were taken along the depth profile of RO-KR10 drill hole, shown in the figure as bars (TNC) or data points (marker gene copy number). Figure 2. Open in new tabDownload slide TNC and functional marker gene copy numbers in Romuvaara drill hole. Six samples (1–6) were taken along the depth profile of RO-KR10 drill hole, shown in the figure as bars (TNC) or data points (marker gene copy number). Microbial community composition and correlation to geochemical parameters Archaeal community structure and diversity: In total, 24 074 archaeal 16S rRNA gene sequence reads were retrieved from the six studied depths of the Romuvaara deep groundwater, with an average of 4012 reads per sample. A total of 756 archaeal operational taxonomic units (OTUs) were observed. Archaeal communities at different depths were very similar to one another. The most abundant phylotypes belonged to Methanospirillum, Methanoregula, Methanobacterium and thaumarchaeotal Candidatus Nitrosoarchaeum. These OTUs represented 86%–96% of the total archaeal communities (Fig. 3a). The deepest sample (550–600 m) had the highest relative abundance (13.7%) of different archaeal groups representing the rare biosphere, i.e. OTUs with less than 1% abundance on average of the total archaeal sequences obtained. Of the minor groups, the proportion of thaumarchaeotal OTUs belonging to the archaeal group SAGMCG-1 increased from 2.5% to 5.6% in samples from 350 m and below. The shallowest sample (50–100 m) had the highest diversity according to the diversity indices (Shannon H’ 4.19, Simpson 0.87), and the lowest H’ was detected in the archaeal community at 350–400 m depth (H’ 3.01). This was also the sample with the lowest number of observed OTUs (69) and second lowest number of sequences (1818). According to Simpson diversity index, the least diverse archaeal community was at 250–300 m depth (Simpson 0.77). The rarefaction analyses performed at 97% sequence similarity showed that the curves reached a plateau phase already after 200 sequences, meaning that for the archaeal communities, the sequencing depth was adequate for these samples (Supplementary data 3, Supporting Information). However, on average, according to the Chao1 estimated species richness, only 51% of the total archaeal communities were captured (Table 1). Average coverage-based estimate (ACE) indicated that 47% of the richness of the communities was detected. Figure 3. Open in new tabDownload slide Community composition of (A) archaea, (B) bacteria and (C) fungi. Operational taxonomic units (OTUs) with less than 1% abundance on average have been grouped together. Figure 3. Open in new tabDownload slide Community composition of (A) archaea, (B) bacteria and (C) fungi. Operational taxonomic units (OTUs) with less than 1% abundance on average have been grouped together. Table 1. Sequencing statistics and ecology indices RO-KR10 sample Total nr of sequences Observed OTUs Shannon H΄ Simpson 1-D Chao1 ACE OTUs/Chao1 OTUs/ACE Archaeal 16S: 1 4401 254 4.19 0.87 588 612 43% 42% 2 8971 386 3.81 0.83 674 770 57% 50% 3 1207 106 3.3 0.77 338 337 31% 31% 4 1818 69 3.01 0.80 109 129 63% 54% 5 4061 122 3.23 0.83 261 244 47% 50% 6 3616 153 3.75 0.87 242 268 63% 57% Bacterial 16S: 1 2033 133 2.62 0.57 209 216 64% 62% 2 5906 503 4.89 0.84 757 791 66% 64% 3 2218 312 4.91 0.79 455 502 69% 62% 4 7286 735 5.59 0.84 1234 1282 60% 57% 5 7423 731 6.02 0.91 1200 1255 61% 58% 6 6510 629 5.92 0.90 994 1014 63% 62% Fungal ITS: 1 2951 48 1.20 0.31 62 77 77% 63% 2 8759 130 3.54 0.82 205 191 63% 68% 3 9976 137 3.10 0.72 176 189 78% 72% 4 16671 146 3.86 0.88 182 188 80% 77% 5 19761 130 3.17 0.78 175 186 74% 70% 6 12286 128 3.24 0.73 167 181 77% 71% RO-KR10 sample Total nr of sequences Observed OTUs Shannon H΄ Simpson 1-D Chao1 ACE OTUs/Chao1 OTUs/ACE Archaeal 16S: 1 4401 254 4.19 0.87 588 612 43% 42% 2 8971 386 3.81 0.83 674 770 57% 50% 3 1207 106 3.3 0.77 338 337 31% 31% 4 1818 69 3.01 0.80 109 129 63% 54% 5 4061 122 3.23 0.83 261 244 47% 50% 6 3616 153 3.75 0.87 242 268 63% 57% Bacterial 16S: 1 2033 133 2.62 0.57 209 216 64% 62% 2 5906 503 4.89 0.84 757 791 66% 64% 3 2218 312 4.91 0.79 455 502 69% 62% 4 7286 735 5.59 0.84 1234 1282 60% 57% 5 7423 731 6.02 0.91 1200 1255 61% 58% 6 6510 629 5.92 0.90 994 1014 63% 62% Fungal ITS: 1 2951 48 1.20 0.31 62 77 77% 63% 2 8759 130 3.54 0.82 205 191 63% 68% 3 9976 137 3.10 0.72 176 189 78% 72% 4 16671 146 3.86 0.88 182 188 80% 77% 5 19761 130 3.17 0.78 175 186 74% 70% 6 12286 128 3.24 0.73 167 181 77% 71% Open in new tab Table 1. Sequencing statistics and ecology indices RO-KR10 sample Total nr of sequences Observed OTUs Shannon H΄ Simpson 1-D Chao1 ACE OTUs/Chao1 OTUs/ACE Archaeal 16S: 1 4401 254 4.19 0.87 588 612 43% 42% 2 8971 386 3.81 0.83 674 770 57% 50% 3 1207 106 3.3 0.77 338 337 31% 31% 4 1818 69 3.01 0.80 109 129 63% 54% 5 4061 122 3.23 0.83 261 244 47% 50% 6 3616 153 3.75 0.87 242 268 63% 57% Bacterial 16S: 1 2033 133 2.62 0.57 209 216 64% 62% 2 5906 503 4.89 0.84 757 791 66% 64% 3 2218 312 4.91 0.79 455 502 69% 62% 4 7286 735 5.59 0.84 1234 1282 60% 57% 5 7423 731 6.02 0.91 1200 1255 61% 58% 6 6510 629 5.92 0.90 994 1014 63% 62% Fungal ITS: 1 2951 48 1.20 0.31 62 77 77% 63% 2 8759 130 3.54 0.82 205 191 63% 68% 3 9976 137 3.10 0.72 176 189 78% 72% 4 16671 146 3.86 0.88 182 188 80% 77% 5 19761 130 3.17 0.78 175 186 74% 70% 6 12286 128 3.24 0.73 167 181 77% 71% RO-KR10 sample Total nr of sequences Observed OTUs Shannon H΄ Simpson 1-D Chao1 ACE OTUs/Chao1 OTUs/ACE Archaeal 16S: 1 4401 254 4.19 0.87 588 612 43% 42% 2 8971 386 3.81 0.83 674 770 57% 50% 3 1207 106 3.3 0.77 338 337 31% 31% 4 1818 69 3.01 0.80 109 129 63% 54% 5 4061 122 3.23 0.83 261 244 47% 50% 6 3616 153 3.75 0.87 242 268 63% 57% Bacterial 16S: 1 2033 133 2.62 0.57 209 216 64% 62% 2 5906 503 4.89 0.84 757 791 66% 64% 3 2218 312 4.91 0.79 455 502 69% 62% 4 7286 735 5.59 0.84 1234 1282 60% 57% 5 7423 731 6.02 0.91 1200 1255 61% 58% 6 6510 629 5.92 0.90 994 1014 63% 62% Fungal ITS: 1 2951 48 1.20 0.31 62 77 77% 63% 2 8759 130 3.54 0.82 205 191 63% 68% 3 9976 137 3.10 0.72 176 189 78% 72% 4 16671 146 3.86 0.88 182 188 80% 77% 5 19761 130 3.17 0.78 175 186 74% 70% 6 12286 128 3.24 0.73 167 181 77% 71% Open in new tab Bacterial community structure and diversity: The total number of bacterial 16S rRNA gene sequence reads from Romuvaara RO-KR10 groundwater was 31 376, and total number of observed OTUs 1700. The number of sequences per sample was on average 5229. The lowest number of observed bacterial OTUs was detected in the samples from the shallowest depth (50–100 m) and the highest OTU number in the 350–500 m region of the drill hole, ranging from 133 to 735 bacterial OTUs. The bacterial communities inhabiting the groundwater at different depths were highly similar to one another, with the exception of the proportion of Gallionella that showed a decrease in relative abundance according to depth allowing minor bacterial groups to become more established. The uppermost part of the drill hole hosted the least diverse bacterial community, with Gallionella as the most dominant bacteria, representing 85% of the bacterial community (Fig. 3b). The Shannon diversity H’ of this community was 2.62 and Simpson diversity estimate 0.57 (Table 1). The captured species richness was 64% of the estimated richness according to the Chao1 estimate and 62% according to ACE (Table 1). At the second shallowest depth (150–200 m) the proportion of α-proteobacteria and Nitrospira (5.9% and 3.2% of the total bacterial community, respectively) was larger than at other depths. The proportion of δ-proteobacterial OTUs affiliating with known SRB (Desulfobulbus and Desulfovibrio) was highest in the 250–500 m depths of the drill hole, although sequences affiliating with δ-proteobacteria were detected throughout the borehole. The most diverse bacterial community according to both diversity indices was detected from the sample at 450–500 m (Shannon H’ 6.02, Simpson 0.91). The deepest sample hosted a bacterial community composed of 31% of Gallionella, but the second largest OTU affiliated with sphingobacterial Lentimicrobiaceae (6.5% of the total bacterial community). This phylotype was detected only in minor amounts in the upper parts of the drill hole. The deepest sampling depth also hosted a diverse bacterial community (Shannon H’ 5.92). Rarefaction analysis revealed that only the shallowest sample was exhaustively sequenced (Supplementary data , Supporting Information). The Chao1 species richness indicator showed that on average 64% of the estimated species richness was captured with pyrosequencing of the bacterial communities in Romuvaara RO-KR10 groundwater (Table 1). Average estimate of the coverage of the sequencing was 61%. Fungal community structure and diversity: A total of 70 404 reads (on average 11 734 per sample) of the fungal intragenic spacer region 1 (ITS1) were retrieved from the RO-KR10 groundwater. Of these, 1425 distinct OTUs were identified. We used the blastn -alignment tool (Altschul et al.1990) to identify the most abundant fungal OTU that could not be classified with UNITE. This OTU was affiliating with Togninia minima with 92% similarity. It represented 37%–99% of the fungal communities in different depths (Fig. 3c). Ascomycotal Penicillium was most abundant at 250–300 m. Cladosporium (11%) was also abundant at this depth. Sarocladium were more abundant in deeper samples (Fig. 3c). Wallemia-affiliating OTUs were most abundant at 450–500 m sampling depth (6.6% of the community) but absent in the shallower depths. The most diverse fungal community according to the diversity indices was detected from the 350–400 m depth (H’ 3.86, Simpson 0.88) whereas the least diverse community (H’ 1.20, Simpson 0.31) was retrieved from the shallowest depth, where 99% of the community was affiliating with Togninia (Fig. 3c, Table 1). When comparing the species richness with the Chao1 estimate, we captured on average 75% of the total fungal diversity. The average coverage estimate for the fungal communities was 70%. Fungal communities were exhaustively sequenced, as was shown by the rarefaction analysis (Supplementary data , Supporting Information). The hydrogeochemistry data collected from Romuvaara RO-KR10 are represented in Table 2. The groundwater in Romuvaara RO-KR10 drill hole is fresh; the measured total dissolved solids were on average 28 mg L−1 and also its isotopic composition (δ2H and δ18O, Table 2, Fig. 4) corresponded with typical values for shallow fresh groundwater in the region (Kortelainen 2007). The oxygen concentration of the fluids varied from 2.8 mg L−1 in the shallowest sample to 3.3 mg L−1 in the third sample spanning 250–300 m depth. The average pH was 6.5, and major anions and cations were SO4, S, Si, Na and Ca. Of prominent electron acceptors for microbial life, sulfate was available in all tested depths ranging from 0.6 to 3.2 mg L−1 with a generally increasing trend towards the bottom of the drill hole. Nitrate and total Fe were below the detection limit (<0.2 mg L−1 and <0.03 mg L−1, respectively). Electron donors such as ammonium, iron(II) and organic carbon compounds were not measured in the fluids. Principal component analysis divided the samples to three groups according to depth: the shallowest sample grouping furthest away from all other samples, second and third sampling depths grouped together and the three deepest samples formed a third group (Supplementary data , Supporting Information). Figure 4. Open in new tabDownload slide Groundwater stable isotopes from Romuvaara. LMWL: Local meteoric water line, GMWL: Global meteoric water line. Figure 4. Open in new tabDownload slide Groundwater stable isotopes from Romuvaara. LMWL: Local meteoric water line, GMWL: Global meteoric water line. Table 2. Hydrogeochemical variables of Romuvaara groundwater at different depths RO-KR10 sample 1 2 3 4 5 6 Depth, geochemistry m 0–50 100–150 200–250 300–350 400–450 500–550 Depth, microbiology m 50–100 150–200 250–300 350–400 450–500 550–600 Lithology Leucotonalite gneiss Tonalite gneiss Tonalite gneiss Amphibolite Tonalite gneiss Tonalite gneiss Fracturing % 27.4 1.4 1.4 3.2 6.5 6.5 Cr µg/L 1.9 1.59 1.11 1.17 1.29 1.18 Cu µg/L 1.58 1.86 1.07 3.43 3.16 3.91 K mg/L 1.71 1.7 1.67 1.78 1.88 1.85 Li µg/L 1.38 1.39 1.4 1.48 1.52 1.48 Mn µg/L 172.0 69.9 36.1 22.8 16.5 14.0 Mo µg/L 0.59 0.4 0.36 0.39 0.47 0.47 Ni µg/L 4.18 5.46 4.4 1.79 1.99 1.76 Ca mg/L 3.67 3.9 4.21 4.54 4.85 5.3 Fea mg/L <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 Mg mg/L 1.04 1.08 1.1 1.09 1.07 1.07 Na mg/L 3.76 3.62 3.64 3.79 3.87 4.03 S mg/L 6.48 5.74 5.17 5.5 6.44 6.33 Si mg/L 6.68 7.73 8.19 8.38 8.38 8.46 Cl mg/L 0.6 0.5 0.5 0.5 0.5 0.5 SO4 mg/L 0.7 0.6 2.0 1.5 2.9 3.2 NO3a mg/L <0.2 <0.2 <0.2 <0.2 <0.2 <0.2 TDSb mg/L 24.90 25.38 26.65 27.22 30.02 30.85 Alkalinity mmol/L 0.51 0.51 0.53 0.6 0.57 0.62 Alkalinityc mmol/L 0.26 0.25 0.27 0.23 0.26 0.28 Electrical conductivity mS/m, 25°C 4.96 5.01 5.17 5.41 5.64 5.99 Electrical conductivityc mS/m, 25°C 5.2 5.2 5.5 5.6 5.8 6.4 pH 6.6 6.5 6.4 6.5 6.6 6.6 pHc 6.7 6.2 6.2 6.2 6.3 6.3 O2c mg/L 2.80 3.15 3.38 2.99 2.82 3.08 δ2H ‰ VSMOW −100 −100 −100 −100 −100 −100 δ18O ‰ VSMOW −13.7 −13.7 −13.7 −13.8 −13.7 −13.7 RO-KR10 sample 1 2 3 4 5 6 Depth, geochemistry m 0–50 100–150 200–250 300–350 400–450 500–550 Depth, microbiology m 50–100 150–200 250–300 350–400 450–500 550–600 Lithology Leucotonalite gneiss Tonalite gneiss Tonalite gneiss Amphibolite Tonalite gneiss Tonalite gneiss Fracturing % 27.4 1.4 1.4 3.2 6.5 6.5 Cr µg/L 1.9 1.59 1.11 1.17 1.29 1.18 Cu µg/L 1.58 1.86 1.07 3.43 3.16 3.91 K mg/L 1.71 1.7 1.67 1.78 1.88 1.85 Li µg/L 1.38 1.39 1.4 1.48 1.52 1.48 Mn µg/L 172.0 69.9 36.1 22.8 16.5 14.0 Mo µg/L 0.59 0.4 0.36 0.39 0.47 0.47 Ni µg/L 4.18 5.46 4.4 1.79 1.99 1.76 Ca mg/L 3.67 3.9 4.21 4.54 4.85 5.3 Fea mg/L <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 Mg mg/L 1.04 1.08 1.1 1.09 1.07 1.07 Na mg/L 3.76 3.62 3.64 3.79 3.87 4.03 S mg/L 6.48 5.74 5.17 5.5 6.44 6.33 Si mg/L 6.68 7.73 8.19 8.38 8.38 8.46 Cl mg/L 0.6 0.5 0.5 0.5 0.5 0.5 SO4 mg/L 0.7 0.6 2.0 1.5 2.9 3.2 NO3a mg/L <0.2 <0.2 <0.2 <0.2 <0.2 <0.2 TDSb mg/L 24.90 25.38 26.65 27.22 30.02 30.85 Alkalinity mmol/L 0.51 0.51 0.53 0.6 0.57 0.62 Alkalinityc mmol/L 0.26 0.25 0.27 0.23 0.26 0.28 Electrical conductivity mS/m, 25°C 4.96 5.01 5.17 5.41 5.64 5.99 Electrical conductivityc mS/m, 25°C 5.2 5.2 5.5 5.6 5.8 6.4 pH 6.6 6.5 6.4 6.5 6.6 6.6 pHc 6.7 6.2 6.2 6.2 6.3 6.3 O2c mg/L 2.80 3.15 3.38 2.99 2.82 3.08 δ2H ‰ VSMOW −100 −100 −100 −100 −100 −100 δ18O ‰ VSMOW −13.7 −13.7 −13.7 −13.8 −13.7 −13.7 a All below detection limit shown in the table. b Total dissolved solids, calculated by adding up all analyzed cation and anion concentrations. c Field measurement. Open in new tab Table 2. Hydrogeochemical variables of Romuvaara groundwater at different depths RO-KR10 sample 1 2 3 4 5 6 Depth, geochemistry m 0–50 100–150 200–250 300–350 400–450 500–550 Depth, microbiology m 50–100 150–200 250–300 350–400 450–500 550–600 Lithology Leucotonalite gneiss Tonalite gneiss Tonalite gneiss Amphibolite Tonalite gneiss Tonalite gneiss Fracturing % 27.4 1.4 1.4 3.2 6.5 6.5 Cr µg/L 1.9 1.59 1.11 1.17 1.29 1.18 Cu µg/L 1.58 1.86 1.07 3.43 3.16 3.91 K mg/L 1.71 1.7 1.67 1.78 1.88 1.85 Li µg/L 1.38 1.39 1.4 1.48 1.52 1.48 Mn µg/L 172.0 69.9 36.1 22.8 16.5 14.0 Mo µg/L 0.59 0.4 0.36 0.39 0.47 0.47 Ni µg/L 4.18 5.46 4.4 1.79 1.99 1.76 Ca mg/L 3.67 3.9 4.21 4.54 4.85 5.3 Fea mg/L <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 Mg mg/L 1.04 1.08 1.1 1.09 1.07 1.07 Na mg/L 3.76 3.62 3.64 3.79 3.87 4.03 S mg/L 6.48 5.74 5.17 5.5 6.44 6.33 Si mg/L 6.68 7.73 8.19 8.38 8.38 8.46 Cl mg/L 0.6 0.5 0.5 0.5 0.5 0.5 SO4 mg/L 0.7 0.6 2.0 1.5 2.9 3.2 NO3a mg/L <0.2 <0.2 <0.2 <0.2 <0.2 <0.2 TDSb mg/L 24.90 25.38 26.65 27.22 30.02 30.85 Alkalinity mmol/L 0.51 0.51 0.53 0.6 0.57 0.62 Alkalinityc mmol/L 0.26 0.25 0.27 0.23 0.26 0.28 Electrical conductivity mS/m, 25°C 4.96 5.01 5.17 5.41 5.64 5.99 Electrical conductivityc mS/m, 25°C 5.2 5.2 5.5 5.6 5.8 6.4 pH 6.6 6.5 6.4 6.5 6.6 6.6 pHc 6.7 6.2 6.2 6.2 6.3 6.3 O2c mg/L 2.80 3.15 3.38 2.99 2.82 3.08 δ2H ‰ VSMOW −100 −100 −100 −100 −100 −100 δ18O ‰ VSMOW −13.7 −13.7 −13.7 −13.8 −13.7 −13.7 RO-KR10 sample 1 2 3 4 5 6 Depth, geochemistry m 0–50 100–150 200–250 300–350 400–450 500–550 Depth, microbiology m 50–100 150–200 250–300 350–400 450–500 550–600 Lithology Leucotonalite gneiss Tonalite gneiss Tonalite gneiss Amphibolite Tonalite gneiss Tonalite gneiss Fracturing % 27.4 1.4 1.4 3.2 6.5 6.5 Cr µg/L 1.9 1.59 1.11 1.17 1.29 1.18 Cu µg/L 1.58 1.86 1.07 3.43 3.16 3.91 K mg/L 1.71 1.7 1.67 1.78 1.88 1.85 Li µg/L 1.38 1.39 1.4 1.48 1.52 1.48 Mn µg/L 172.0 69.9 36.1 22.8 16.5 14.0 Mo µg/L 0.59 0.4 0.36 0.39 0.47 0.47 Ni µg/L 4.18 5.46 4.4 1.79 1.99 1.76 Ca mg/L 3.67 3.9 4.21 4.54 4.85 5.3 Fea mg/L <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 Mg mg/L 1.04 1.08 1.1 1.09 1.07 1.07 Na mg/L 3.76 3.62 3.64 3.79 3.87 4.03 S mg/L 6.48 5.74 5.17 5.5 6.44 6.33 Si mg/L 6.68 7.73 8.19 8.38 8.38 8.46 Cl mg/L 0.6 0.5 0.5 0.5 0.5 0.5 SO4 mg/L 0.7 0.6 2.0 1.5 2.9 3.2 NO3a mg/L <0.2 <0.2 <0.2 <0.2 <0.2 <0.2 TDSb mg/L 24.90 25.38 26.65 27.22 30.02 30.85 Alkalinity mmol/L 0.51 0.51 0.53 0.6 0.57 0.62 Alkalinityc mmol/L 0.26 0.25 0.27 0.23 0.26 0.28 Electrical conductivity mS/m, 25°C 4.96 5.01 5.17 5.41 5.64 5.99 Electrical conductivityc mS/m, 25°C 5.2 5.2 5.5 5.6 5.8 6.4 pH 6.6 6.5 6.4 6.5 6.6 6.6 pHc 6.7 6.2 6.2 6.2 6.3 6.3 O2c mg/L 2.80 3.15 3.38 2.99 2.82 3.08 δ2H ‰ VSMOW −100 −100 −100 −100 −100 −100 δ18O ‰ VSMOW −13.7 −13.7 −13.7 −13.8 −13.7 −13.7 a All below detection limit shown in the table. b Total dissolved solids, calculated by adding up all analyzed cation and anion concentrations. c Field measurement. Open in new tab First-order relationships between geochemistry variables, TNC and OTUs with on average abundance at least 1% were tested with pairwise Pearson correlation (Supplementary data 5, Supporting Information). Significant positive correlation (r > 0.8, P < 0.01) was found between fracturing of the bedrock, field pH, Mo and Cl concentrations and Methanoregula. Manganese concentration correlated positively with e.g. Gallionella abundance, while Si correlated negatively (r < −0.8, P < 0.01) with Gallionella. Chromium concentrations correlated negatively with some bacterial groups, such as Ignavibacteria, Opitutus and Comamonadaceae. Canonical correlation analysis was used to describe the distribution of the different sampling depths of RO-KR10 according to the relative abundance of microbial taxa in relation to geochemical parameters (Fig. 5). The first and second canonical axis explained 63% and 20% of the variance between environmental variables and the microbial community, respectively. The two shallowest samples correlated with Cl, Cr, Mo, Mn, Ni and TDS concentrations. The sample from depth of 250–300 m correlated with Mg and oxygen concentration. Fungal OTUs Penicillium and Cladosporium grouped close to this sample. Sulfur concentration and pH correlated with the sampling depth from 350–400 m (sample 4). The two deepest samples clustered together and correlated with EC, total dissolved solids, alkalinity and Ca, Cu, K, Li, Na and sulfate concentrations. Figure 5. Open in new tabDownload slide Canonical correlation of 25 geochemical variables, OTUs (blue dots) representing > 1% of the total bacterial, archaeal and fungal community and sampling depths (black stars). Field measurements are marked with asterisk (*). Figure 5. Open in new tabDownload slide Canonical correlation of 25 geochemical variables, OTUs (blue dots) representing > 1% of the total bacterial, archaeal and fungal community and sampling depths (black stars). Field measurements are marked with asterisk (*). Utilization of carbon and nitrogen substrates Common organic carbon and nitrogen compounds were tested for their applicability for carbon or nitrogen source for deep microbial communities in Romuvaara bedrock. Carbohydrates were overall the most preferred carbon substrate, as all microbial communities at 50–450 m depths used these compounds (Supplementary data 2, Supporting Information). Carboxylic acids were the second most widely used carbon substrate by the microbial communities. The microbial community at depth of 250–300 m used the most diverse range of substrates, including alcohols, amino acids, amino sugars and glycosides. The second deepest microbial community did not use any of the tested compounds. The organic carbon and nitrogen usage of the deepest microbial community remained undetermined, because all of the wells in the BIOLOG AN plate changed color immediately after adding the sample fluid. This was most likely due to a chemical reaction between the kit and the groundwater and not a result of microbial metabolism. DISCUSSION This is the first study describing the microbial community structure in groundwater in the complete drill hole water column in Romuvaara RO-KR10. The water throughout the RO-KR10 length is fresh. Without a strong light isotopic signal similar to glacial melt water of the Fennoscandian Shield areas (Blomqvist 1999), we presume that groundwater in Romuvaara originates from post-glacial meteoric water and shallow groundwater percolating through three-dimensional fracture networks, likely to form in granitic rocks (Kietäväinen 2017). However, we will not rule out the possibility of some mixing with glacial waters. The microbial cell numbers in the Romuvaara deep subsurface were within the same range as is reported from crystalline bedrock groundwater from equivalent depths at other Fennoscandian Shield sites (Haveman and Pedersen 2002; Hallbeck and Pedersen 2012; Bomberg et al.2014, 2015; Purkamo et al.2015). The most abundant bacterial phylotype detected had 97% 16S rRNA gene sequence similarity to species of the genus Gallionella, which oxidize ferrous iron at circumneutral pH. Unfortunately, specific marker genes for iron oxidation or reduction do not exist, so we could not quantify the amount of iron cycling microorganisms in the groundwater. However, according to the physiology inferred from the taxonomy of the most abundant bacterial phylotype, we assume that iron oxidation is an important physiological function for the majority of the microbial community in the deep subsurface in Romuvaara. OTUs belonging to Comamonadaceae and Nitrospira were common in the bacterial communities in Romuvaara. Previous studies have shown that these microbes, in addition to Gallionella are frequent inhabitants of the deep terrestrial subsurface (Sahl et al.2008; Konno et al.2013; Osburn et al.2014; Ben Maamar et al.2015; Larentis, Psenner and Alfreider 2015). Interestingly, the relative abundance of Comamonadaceae and Gallionella have been shown to depend on the age of the groundwater: Comamonadaceae were abundant in fluids with shorter residence times (<16 years), while Gallionellaceae were dominant in older fluids (>40 up to several thousands of years residence times) (Ben Maamar et al.2015). If this is the case also in Romuvaara, we can assume that the succession towards Gallionella dominance has already happened. In deep subsurface environments, Nitrospira form a minor component of those bacterial communities that are dominated by β-proteobacterial iron-oxidizers (Konno et al.2013; Osburn et al.2014). This was also apparent in our study, where the relative abundance of Nitrospira, albeit lower than dominant phylotype Gallionella, remained similar throughout the studied depths. The microbial community structure in the RO-KR10 drill hole is drastically different compared to the other Fennoscandian deep subsurface environments studied to date. The most abundant bacterial and archaeal phylotypes in Romuvaara (Gallionella, Methanospirillum, Candidatus Nitrosoarchaeaum) have not been detected in Outokumpu or Olkiluoto deep biosphere (Nyyssönen et al. 2014; Bomberg, Lamminmäki and Itävaara 2016; Purkamo et al.2016). In Äspö Hard Rock Laboratory, the biogenic iron oxides formed in the aerated tunnel waterways are predominantly originating from Gallionella (Anderson and Pedersen 2003). However, Gallionella appears to be absent of the fracture fluid communities (Hubalek et al.2016). Fungal abundance and diversity are still very much unexplored in the deep subsurface setting, and the collective feature of the fungal community in this and other studies is that many detected phylotypes remain unclassified (Sohlberg et al.2015; Nagano et al.2017). According to the quantified marker genes, the most abundant metabolic pathways were methane oxidation, sulfate reduction and nitrate reduction. However, compared to the total cell numbers, these represented 10% or less of the microbial community in all other depths except in the deepest sample, where studied marker gene copy numbers reached 26% of the total cells. Generalizing the physiology of the different phylotypes according to literature, phylotypes affiliating with Burkholderiales, Rhizobiales and Verrucomicrobia may represent the methanotrophs in Romuvaara deep biosphere (Chistoserdova, Kalyuzhnaya and Lidstrom 2009). Also Methylococcales-affiliating phylotypes were present in low abundance (<1% of the bacterial community). Gallionella has been shown to co-occur with methane oxidizers, although they can outnumber methanotrophs by competition over oxygen (Wang et al.2012). We speculate that this is also the case in Romuvaara, where copy numbers of methanotrophy marker gene were relatively low at other depths in contrast to the deepest sample, where methanotrophy marker gene copies represented 23% of the total cells while the relative abundance of Gallionella was the lowest. Characteristic sulfate reducers Desulfobulbus and Desulfovibrio comprised on average 2.7% of the total bacterial community in Romuvaara. The copy numbers of the marker gene for sulfate reduction (dsrB) compared to the TNCs were roughly at the same level (on average, 1% of theTNCs ). Although sulfate was readily available in RO-KR10 water, known sulfate reducers formed a minor component of the microbial community. Plausible reasons for this are the prevention of anaerobic respiration of sulfate in the presence of oxygen, and the competition with microbes specialized to a microoxic environment (Cypionka 2000; Muyzer and Stams 2008). However, using oxygen, these competing microbes may create suitable niches for sulfate reducing microbes to occupy (Muyzer and Stams 2008). Marker gene copy numbers for nitrate reduction (narG) appeared to be as common as those for sulfate reduction (dsrB). This is in accordance with the amplicon sequencing data, where 7.9% of bacterial sequence reads belonged to known nitrate reducing clades, such as Comamonadaceae and Opitutus (Willems et al.1991; Chin, Liesack and Janssen 2001). Furthermore, both Desulfobulbus and Desulfovibrio are also able to reduce nitrate (Marietou 2016), so it is likely that they contribute to the numbers of recognized nitrate reducers in the Romuvaara deep subsurface. Although ammonia oxidizers were detected in the microbial communities, amoA copy numbers were low. A significant proportion of the archaeal community belonged to ammonia-oxidizing thaumarcheaotal genus Candidatus Nitrosoarchaeum (Mosier, Lund and Francis 2012). In addition, the Nitrospira detected in abundance in RO-KR10 oxidize ammonia through the commamox process (Daims et al.2015; Van Kessel et al.2015). However, the primers we used in the qPCR assay were aimed at bacterial amoA genes, especially the α- and β-proteobacteria (Nicolaisen and Ramsing 2002), and thus did not detect the ammonia mono-oxygenase gene of thaumarchaeota (Schleper and Nicol 2010; Pjevac et al. 2017) or comammox microbes, such as Nitrospira. Methanogenesis marker gene (mcrA) copy numbers were highest in the bottom of the drill hole, where they represented 1% of the TNC. Interestingly, this depth hosted the lowest relative abundance of known methanogenic archaea (Methanoregula, Methanospirillum and Methanobacterium). However, this seemingly controversial result is explained by the overall highest cell numbers in the deepest part of the drill hole, likely to increase the yield of the biomass and thus the copy numbers of all tested functional genes. Signals of autotrophic carbon fixation with the accC were negligible throughout the water column. Nevertheless, autotrophic microbes were abundant in the Romuvaara groundwater. The dominant bacterial phylotype Gallionella incorporates CO2 via the Calvin cycle with RuBisCO form II (Emerson et al.2013; Hallbeck and Pedersen 2014). Other significant autotrophic members of the microbial communities were Nitrospira bacteria and methanogenic archaea (Methanoregula, Methanospirillum and Methanobacterium). The fungal community in RO-KR10 groundwater consisted mostly of Togninia. Penicillium-affiliating fungi have also been shown to be relatively abundant. Anaerobic fungi have been suggested to play a crucial role in the biogeochemistry of the deep biosphere. The radiolytic production of abiotic H2 may not be sufficient to sustain autotrophic microbial communities in granitic bedrock, but recently, heterotrophic fungi were shown to form synergistic consortia with sulfate reducing microbes requiring hydrogen as electron donor (Ivarsson et al.2016; Drake and Ivarsson 2017; Drake et al.2017). The fungi can also harvest essential carbohydrates for their own metabolism by degrading organic material from biofilms. Metabolic products of fungi, such as acetate, alcohols and CO2 can be exploited as electron donors and carbon substrates by bacteria and archaea (Drake and Ivarsson 2017). In addition, previous studies have shown Penicillium species, also detected from Romuvaara, capable of excreting extracellular ligands and organic acids for bioleaching of metals from different materials. Fungi are also known to produce siderophores to aid Fe assimilation (Gadd 2007, 2010). These fungal activities would undoubtedly support the growth of iron-oxidizing Gallionella in Romuvaara, where the iron concentrations were relatively low. Studies showing a strong correlation between concentrations of nitrogen compounds and diversity or abundance of fungi indicate that microeukaryotes are involved in N-cycling in deep biosphere (Orsi, Biddle and Edgcomb 2013; Sohlberg et al.2015). BIOLOGs indicated that the microbial communities were capable of using a range of organic carbon and nitrogen substrates, yet more than 90% of the tested substrates remained unused in most of the depths. From phylogeny-inferred functionality of the microbial phylotypes, mixotrophic sulfate reducers and Comamonadaceae, and anaerobic, chemoorganotrophic Lentimicrobiaceae were most likely using these organic compounds (Willems et al.1991; Muyzer and Stams 2008; Plugge et al.2011; Sun et al.2016). However, the use of the anaerobic BIOLOG kit may affect the results, as the test was done in anoxic environment, which does not entirely represent the natural conditions in the drill hole water. The used sampling method allows gathering data on geochemistry and microbial communities as a function of the full depth of the drill hole. However, there are also challenges and possible issues related to this method (Itävaara et al.2011). One major risk is the possible mixing of the drill hole fluids during assembly of the sampling tube. The disruption may have occurred although care was taken during the sampling to let the tube sink into the drill hole smoothly. When bringing the tube back to the ground level, we observed color differences in water in separate tube sections. This was assumed to indicate zoning according to different geochemical conditions (like oxygenation). Differences in the samples were verified with PCA analysis, thus fortifying the initial field observations. However, due to the high risk of mixing the different zones, we did not repeat the sampling, explaining the lack of the replicate samples. Another challenge is to interpret how well the water in the drill hole represents the fluids in the bedrock fracture system. Linked to this, it may be difficult to distinguish the deep waters from the surface waters that may have been mixed with the fracture fluids, especially in inland setting where deep fresh water resembles surface water like in Romuvaara. Previously, Haveman, Pedersen and Ruotsalainen (1999) used packer sampling combined with culturing methods to describe the biogeochemical characteristics of the fracture zone at 600 m in RO-KR10. The sampling method allowed the retrieval of unmixed fracture fluids. They observed higher pH, TDS and total Fe values compared to the drill hole water. This indicates that drill hole water does not entirely represent the situation in the bedrock fractures. In addition, according to the earlier study, the largest bacterial group cultured from the deep fracture zone were iron reducers. This is in contrast to our results, showing that in the drill hole, iron-oxidizing bacteria was the most abundant metabolic group of microorganisms. Finally, it should also be taken into account that as 0.22 µm filters were used in collecting the biomass in this study, a part of the microbial community with a smaller cell size that has recently been characterized from groundwater environments (Wu et al.2016) may have been missed. CONCLUSIONS This study sheds light on the microbial community composition of a rather understudied deep biosphere habitat, namely deep freshwater aquifers. Compared to other Fennoscandian deep subsurface sites where microbial communities have been studied, Romuvaara groundwater represents a novel case especially with its bacterial community structure. The bacterial community was most analogous to those detected from deep aerobic and/or fresh groundwater. The archaeal community structure resembled those detected from other deep terrestrial subsurface sites from comparable depths. Iron-oxidizing Gallionella dominated the microbial community, but microbial groups involved in nitrogen cycling and sulfate reduction, as well as aerobic methane oxidation and methanogenesis were detected. Nitrospira may play a key role in nitrogen cycling in nutrient-deficient environments like the deep continental bedrock fluids in Romuvaara, in addition to thaumarchaeotal ammonia oxidizers detected in abundance in the archaeal communities. Fungi were also present and may have a significant role in elemental cycling and supporting the bacterial and archaeal communities. This study provides an example of an early stage of microbial succession in deep groundwater and thus, in the evolution of deep bedrock biosphere. SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. ACKNOWLEDGEMENTS The authors acknowledge Mirva Pyrhönen and Pauliina Rajala from VTT Technical Research Centre of Finland for their skilful work with preparation and execution of laboratory analyses, Nina Hendriksson and Arja Henttinen (Geological Survey of Finland) are thanked for the water stable isotope analysis. Leea Ojala (VTT) and Arto Pullinen (Geological Survey of Finland, GTK) are acknowledged for aiding in sampling in the field. Dr. Eleanor Mare is thanked for the language editing. The two reviewers are thanked for their insightful comments and recommendations that aided to improve the manuscript. FUNDING This work was supported by the Academy of Finland (DEEP LIFE), the Finnish Research Program on Nuclear Waste Management (KYT2014 and KYT2018 grants SALAMI, GEOMIKRO and RENGAS), VTT Technical Research Centre and the Geological Survey of Finland. Conflict of interest. None declared. REFERENCES Altschul SF , Gish W , Miller W et al. Basic local alignment search tool . J Mol Biol . 1990 ; 215 : 403 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat Anderson C , Pedersen K . In situ growth of Gallionella biofilms and partitioning of lanthanides and actinides between biological material and ferric oxyhydroxides . Geobiology . 2003 ; 1 : 169 – 78 . Google Scholar Crossref Search ADS WorldCat Anttila P , Ahokas H , Front K et al. Final Disposal of Spent Nuclear Fuel in Finnish Bedrock—Romuvaara Site Report . Posiva Oy , Olkiluoto, Finland 1999 : Posiva 99-11 Google Preview WorldCat COPAC Bano N , Ruffin S , Ransom B et al. Phylogenetic composition of Arctic Ocean archaeal assemblages and comparison with Antarctic assemblages . Appl Environ Microbiol . 2004 ; 70 : 781 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat Barns SM , Fundyga RE , Jeffries MW et al. Remarkable archaeal diversity detected in a Yellowstone National Park hot spring environment . Proc Natl Acad Sci U S A . 1994 ; 91 : 1609 – 13 . Google Scholar Crossref Search ADS PubMed WorldCat Bengtson S , Rasmussen B , Ivarsson M et al. Fungus-like mycelial fossils in 2.4-billion-year-old vesicular basalt . Nat Ecol Evol . 2017 ; 1 : 0141 . Google Scholar Crossref Search ADS WorldCat Bengtsson-Palme J , Ryberg M , Hartmann M et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data . Methods Ecol Evol . 2013 ; 4 : 914 – 9 . WorldCat Ben Maamar S , Aquilina L , Quaiser A et al. Groundwater isolation governs chemistry and microbial community structure along hydrologic flowpaths . Front Microbiol . 2015 ; 6 : 1 – 13 . Google Scholar PubMed WorldCat Blomqvist R . Hydrogeochemistry of deep groundwaters in the central part of the Fennoscandian Shield. Nuclear Waste Disposal Research Report 101 , Academic Dissertation , Geological Survey of Finland , Espoo , 1999 . Google Preview WorldCat COPAC Bomberg M , Lamminmäki T , Itävaara M . Microbial communities and their predicted metabolic characteristics in deep fracture groundwaters of the crystalline bedrock at Olkiluoto, Finland . Biogeosciences . 2016 ; 13 : 6031 – 47 . Google Scholar Crossref Search ADS WorldCat Bomberg M , Nyyssönen M , Nousiainen A et al. Evaluation of molecular techniques in characterization of deep terrestrial biosphere . Open J Ecol . 2014 ; 4 : 468 – 87 . Google Scholar Crossref Search ADS WorldCat Bomberg M , Nyyssönen M , Pitkänen P et al. Active microbial communities inhabit sulphate-methane interphase in deep bedrock fracture fluids in Olkiluoto, Finland . Biomed Res Int . 2015 ; 2015 , Article ID 979530 . Google Scholar Crossref Search ADS WorldCat Bomberg M , Raulio M , Jylhä S et al. CO2 and carbonate as substrate for the activation of the microbial community in 180 m deep bedrock fracture fluid of Outokumpu deep drill hole, Finland . AIMS Microbiol . 2017 ; 3 : 846 – 71 . Google Scholar Crossref Search ADS WorldCat Borgonie G , García-Moyano a , Litthauer D et al. Nematoda from the terrestrial deep subsurface of South Africa . Nature . 2011 ; 474 : 79 – 82 . Google Scholar Crossref Search ADS PubMed WorldCat Buée M , Reich M , Murat C et al. 454 pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity . New Phytol . 2009 ; 184 : 449 – 56 . Google Scholar Crossref Search ADS PubMed WorldCat Caporaso JG , Kuczynski J , Stombaugh J et al. QIIME allows analysis of high-throughput community sequencing data . Nat Methods . 2010 ; 7 : 335 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat Chi Fru E . Constraints in the colonization of natural and engineered subterranean igneous rock aquifers by aerobic methane-oxidizing bacteria inferred by culture analysis . Geobiology . 2008 ; 6 : 365 – 75 . Google Scholar Crossref Search ADS PubMed WorldCat Chin K-J , Liesack W , Janssen PH . Opitutus terrae gen. nov., sp. nov., to accommodate novel strains of the division 'Verrucomicrobia' isolated from rice paddy soil . Int J Syst Evol Microbiol . 2001 ; 51 : 1965 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Chistoserdova L , Kalyuzhnaya MG , Lidstrom ME . The expanding world of methylotrophic metabolism . Annu Rev Microbiol . 2009 ; 63 : 477 – 99 . Google Scholar Crossref Search ADS PubMed WorldCat Chivian D , Brodie EL , Alm EJ et al. Environmental genomics reveals a single-species ecosystem deep within earth . Science . 2008 ; 322 : 275 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Colwell FS , D’Hondt S . Nature and extent of the deep biosphere . Rev Mineral Geochem . 2013 ; 75 : 547 – 74 . Google Scholar Crossref Search ADS WorldCat Cypionka H . Oxygen respiration by Desulfovibrio species . Annu Rev Microbiol . 2000 ; 54 : 827 – 48 . Google Scholar Crossref Search ADS PubMed WorldCat Daims H , Lebedeva EV , Pjevac P et al. Complete nitrification by Nitrospira bacteria . Nature . 2015 ; 528 : 504 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat Drake H , Ivarsson M , Bengtson S et al. Anaerobic consortia of fungi and sulfate reducing bacteria in deep granite fractures . Nat Commun . 2017 ; 8 : 55 . Google Scholar Crossref Search ADS PubMed WorldCat Drake H , Ivarsson M . The role of anaerobic fungi in fundamental biogeochemical cycles in the deep biosphere . Fungal Biol Rev . 2017 ; 32 : 20 – 5 . Google Scholar Crossref Search ADS WorldCat Edwards U , Rogall T , Blöcker H et al. Isolation and direct complete nucleotide determination of entire genes. Characterization of a gene coding for 16S ribosomal RNA . Nucleic Acids Res . 1989 ; 17 : 7843 – 53 . Google Scholar Crossref Search ADS PubMed WorldCat Emerson D , Field EK , Chertkov O et al. PubMed Comparative genomics of freshwater Fe-oxidizing bacteria: implications for physiology, ecology, and systematics . . Front Microbiol . ;4: : 254 PubMed WorldCat Gadd GM . Geomycology: biogeochemical transformations of rocks, minerals, metals and radionuclides by fungi, bioweathering and bioremediation . Mycol Res . 2007 ; 111 : 3 – 49 . Google Scholar Crossref Search ADS PubMed WorldCat Gadd GM . Metals, minerals and microbes: geomicrobiology and bioremediation . Microbiology . 2010 ; 156 : 609 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat Gihring TM , Moser DP , Lin L-H et al. The distribution of microbial taxa in the subsurface water of the Kalahari Shield, South Africa . Geomicrobiol J . 2006 ; 23 : 415 – 30 . Google Scholar Crossref Search ADS WorldCat Griebler C , Lueders T . Microbial biodiversity in groundwater ecosystems . Freshwater Biol . 2009 ; 54 : 649 – 77 . Google Scholar Crossref Search ADS WorldCat Grosskopf R , Janssen PH , Liesack W . Diversity and structure of the methanogenic community in anoxic rice paddy soil microcosms as examined by cultivation and direct 16S rRNA gene sequence retrieval . Appl Environ Microbiol . 1998 ; 64 : 960 – 9 . Google Scholar PubMed WorldCat Hallbeck L , Pedersen K . Culture-dependent comparison of microbial diversity in deep granitic groundwater from two sites considered for a Swedish final repository of spent nuclear fuel . FEMS Microbiol Ecol . 2012 ; 81 : 66 – 77 . Google Scholar Crossref Search ADS PubMed WorldCat Hallbeck L , Pedersen K . The family Gallionellaceae . In: Rosenberg E et al. (eds). The Prokaryotes—Alphaproteobacteria and Betaproteobacteria . Berlin, Heidelberg : Springer-Verlag , 2014 , 853 – 8 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Hammer Ø , Harper DAT , Ryan PD . Past: paleontological Statistics Software Package for education and data analysis . Paleontología Electrónica . 2001 ; 4 : 1 – 9 . WorldCat Haveman SA , Pedersen K , Ruotsalainen P . Distribution and metabolic diversity of microorganisms in deep igneous rock aquifers of Finland . Geomicrobiol J . 1999 ; 16 : 277 – 94 . Google Scholar Crossref Search ADS WorldCat Haveman SA , Pedersen K . Distribution of culturable microorganisms in Fennoscandian shield groundwater . FEMS Microbiol Ecol . 2002 ; 39 : 129 – 37 . Google Scholar Crossref Search ADS PubMed WorldCat Hubalek V , Wu X , Eiler A et al. Connectivity to the surface determines diversity patterns in subsurface aquifers of the Fennoscandian shield . ISME J . 2016 ; 10 : 2447 – 2458 . Google Scholar Crossref Search ADS PubMed WorldCat Huson DH , Beier S , Flade I et al. MEGAN community edition - interactive exploration and analysis of large-scale microbiome sequencing data . PLoS Comput Biol . 2016 ; 12 : e1004957 . Google Scholar Crossref Search ADS PubMed WorldCat Inagaki F , Nunoura T , Nakagawa S et al. Biogeographical distribution and diversity of microbes in methane hydrate-bearing deep marine sediments on the Pacific Ocean margin . Proc Natl Acad Sci U S A . 2006 ; 103 : 2815 – 20 . Google Scholar Crossref Search ADS PubMed WorldCat Itävaara M , Nyyssönen M , Kapanen A et al. Characterization of bacterial diversity down to a depth of 1500 m in the Outokumpu deep drill hole, Fennoscandian shield . FEMS Microbiol Ecol . 2011 ; 77 : 295 – 309 . Google Scholar Crossref Search ADS PubMed WorldCat Ivarsson M , Schnürer A , Bengtson S et al. Anaerobic fungi: a potential source of biological H2 in the oceanic crust . Front Microbiol . 2016 ; 7 : 1 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Kallmeyer J , Pockalny R , Adhikari RR et al. Global distribution of microbial abundance and biomass in subseafloor sediment . Proc Natl Acad Sci . 2012 ; 109 : 16213 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat Kietäväinen R , Ahonen L , Kukkonen IT et al. Characterisation and isotopic evolution of saline waters of the Outokumpu deep drill hole, Finland–Implications for water origin and deep terrestrial biosphere . Appl Geochemistry . 2013 ; 32 : 37 – 51 . Google Scholar Crossref Search ADS WorldCat Kietäväinen R , Ahonen L , Kukkonen IT et al. Noble gas residence times of saline waters within crystalline bedrock, Outokumpu deep drill hole, Finland . Geochim Cosmochim Acta . 2014 ; 145 : 159 – 74 . Google Scholar Crossref Search ADS WorldCat Kietäväinen R , Purkamo L . The origin, source and cycling of methane in deep crystalline rock biosphere . Front Microbiol . 2015 ; 6 : 725 . Google Scholar Crossref Search ADS PubMed WorldCat Kietäväinen R . Deep groundwater evolution at Outokumpu, Eastern Finland: from meteoric water to saline gas-rich fluid , Academic dissertation , Geological Survey of Finland , Espoo . 2017 . COPAC Konno U , Kouduka M , Komatsu DD et al. Novel microbial populations in deep granitic groundwater from Grimsel test site, Switzerland . Microb Ecol . 2013 ; 65 : 626 – 37 . Google Scholar Crossref Search ADS PubMed WorldCat Kortelainen N . Isotopic Fingerprints in Surficial Waters: Stable Isotope Methods Applied in Hydrogeological Studies , Academic dissertation , Geological Survey of Finland , Espoo . 2007 . COPAC Kutvonen H , Rajala P , Carpén L et al. Nitrate and ammonia as nitrogen sources for deep subsurface microorganisms . Front Microbiol . 2015 ; 6 : 1079 . Google Scholar Crossref Search ADS PubMed WorldCat Käpyaho A , Mänttäri I , Huhma H . Growth of Archaean crust in the Kuhmo district, eastern Finland: U-Pb and Sm-Nd isotope constraints on plutonic rocks . Precambrian Res . 2006 ; 146 : 95 – 119 . Google Scholar Crossref Search ADS WorldCat Kõljalg U , Nilsson RH , Abarenkov K et al. Towards a unified paradigm for sequence-based identification of fungi . Mol Ecol . 2013 ; 22 : 5271 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat Lahermo PW , Lampén PH . Brackish and saline groundwaters in Finland . In: Fritz P , Frape SK (eds). Saline Water and Gases in Crystalline Rocks . Geological Association of Canada , St John's, Newfoundland, Canada 1987 , 103 – 109 ., Special Paper 33. Google Preview WorldCat COPAC Larentis M , Psenner R , Alfreider A . Prokaryotic community structure in deep bedrock aquifers of the Austrian Central Alps . Antonie van Leeuwenhoek . 2015 ; 107 : 687 – 701 . Google Scholar Crossref Search ADS PubMed WorldCat Lin L-H , Wang P-L , Rumble D et al. Long-term sustainability of a high-energy, low-diversity crustal biome . Science . 2006 ; 314 : 479 – 82 . Google Scholar Crossref Search ADS PubMed WorldCat Lin LH , Slater GF , Sherwood Lollar B et al. The yield and isotopic composition of radiolytic H2, a potential energy source for the deep subsurface biosphere . Geochim Cosmochim Acta . 2005 ; 69 : 893 – 903 . Google Scholar Crossref Search ADS WorldCat Luukkonen A , Kuusela-Lahtinen A , Front K . Interpretation of Geophysical Logging of Boreholes RO-KR10 and RO-KR11 at the Romuvaara Site at Kuhmo and Revision of the Bedrock Model (in Finnish With an English Abstract) , Posiva Oy . Helsinki, Finland : Posiva Working report 97-18 . Google Preview WorldCat COPAC Magnabosco C , Tekere M , Lau MCY et al. Comparisons of the composition and biogeographic distribution of the bacterial communities occupying South African thermal springs with those inhabiting deep subsurface fracture water . Front Microbiol . 2014 ; 5 : 1 – 17 . Google Scholar Crossref Search ADS PubMed WorldCat Marietou A . Nitrate reduction in sulfate-reducing bacteria . FEMS Microbiol Lett . 2016 ; 363 : 2016 – 9 . Google Scholar Crossref Search ADS WorldCat Mathurin FA , Åström ME , Laaksoharju M et al. Effect of tunnel excavation on source and mixing of groundwater in a coastal granitoidic fracture network . Env Sci Tech . 2012 ; 46 : 12779 – 86 . Google Scholar Crossref Search ADS WorldCat McMahon S , Parnell J . Weighing the deep continental biosphere . FEMS Microbiol Ecol . 2014 ; 87 : 113 – 20 . Google Scholar Crossref Search ADS PubMed WorldCat Miettinen H , Kietäväinen R , Sohlberg E et al. Microbiome composition and geochemical characteristics of deep subsurface high-pressure environment, Pyhäsalmi mine Finland . Front Microbiol . 2015 ; 6 : 1203 . Google Scholar Crossref Search ADS PubMed WorldCat Mosier AC , Lund MB , Francis CA . Ecophysiology of an Ammonia-Oxidizing Archaeon Adapted to Low-Salinity Habitats . Microb Ecol . 2012 ; 64 : 955 – 63 . Google Scholar Crossref Search ADS PubMed WorldCat Muyzer G , de Waal EC , Uitterlinden AG . Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA . Appl Environ Microbiol . 1993 ; 59 : 695 – 700 . Google Scholar PubMed WorldCat Muyzer G , Stams AJM . The ecology and biotechnology of sulphate-reducing bacteria . Nat Rev Microbiol . 2008 ; 6 : 441 – 54 . Google Scholar Crossref Search ADS PubMed WorldCat Nagano Y , Miura T , Nishi S et al. Fungal diversity in deep-sea sediments associated with asphalt seeps at the Sao Paulo Plateau . Deep-Sea Res Pt II . 2017 ; 146 : 59 – 67 . Google Scholar Crossref Search ADS WorldCat Nealson KH , Inagaki F , Takai K . Hydrogen-driven subsurface lithoautotrophic microbial ecosystems (SLiMEs): do they exist and why should we care? . Trends Microbiol . 2005 ; 13 : 405 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat Nicolaisen MH , Ramsing NB . Denaturing gradient gel electrophoresis (DGGE) approaches to study the diversity of ammonia-oxidizing bacteria . J Microbiol Methods . 2002 ; 50 : 189 – 203 . Google Scholar Crossref Search ADS PubMed WorldCat Nurmi PA , Kukkonen IT . A new technique for sampling water and gas from deep drill holes . Can J Earth Sci . 1986 ; 23 : 1450 – 4 . Google Scholar Crossref Search ADS WorldCat Nyyssönen M , Bomberg M , Kapanen A et al. Methanogenic and sulphate-reducing microbial communities in deep groundwater of crystalline fock fractures in Olkiluoto, Finland . Geomicrobiol J . 2012 ; 29 : 863 – 78 . Google Scholar Crossref Search ADS WorldCat Nyyssönen M , Hultman J , Ahonen L et al. Taxonomically and functionally diverse microbial communities in deep crystalline rocks of the Fennoscandian shield . ISME J . 2014 ; 8 : 126 – 138 . Google Scholar Crossref Search ADS PubMed WorldCat Orcutt BN , LaRowe DE , Biddle JF et al. Microbial activity in the marine deep biosphere: progress and prospects . Front Microbiol . 2013 ; 4 : 1 – 15 . Google Scholar Crossref Search ADS PubMed WorldCat Orsi W , Biddle JF , Edgcomb V . Deep sequencing of subseafloor eukaryotic rRNA reveals active fungi across marine subsurface provinces . PLoS One . 2013 ; 8 : e56335 . Google Scholar Crossref Search ADS PubMed WorldCat Osburn MR , LaRowe DE , Momper LM et al. Chemolithotrophy in the continental deep subsurface: Sanford Underground Research Facility (SURF), USA . Front Microbiol . 2014 ; 5 : 610 . Google Scholar Crossref Search ADS PubMed WorldCat Pedersen K , Arlinger J , Eriksson S et al. Numbers, biomass and cultivable diversity of microbial populations relate to depth and borehole-specific conditions in groundwater from depths of 4–450 m in Olkiluoto, Finland . ISME J . 2008 ; 2 : 760 – 75 . Google Scholar Crossref Search ADS PubMed WorldCat Pedersen K . Metabolic activity of subterranean microbial communities in deep granitic groundwater supplemented with methane and H2 . ISME J . 2012b ; 7 : 839 – 49 . Google Scholar Crossref Search ADS WorldCat Pedersen K . Microbial life in deep granitic rock . FEMS Microbiol Rev . 1997 ; 20 : 399 – 414 . Google Scholar Crossref Search ADS WorldCat Pedersen K . Subterranean microbial populations metabolize hydrogen and acetate under in situ conditions in granitic groundwater at 450 m depth in the  Äspö Hard Rock Laboratory, Sweden . FEMS Microbiol Ecol . 2012a ; 81 : 217 – 29 . Google Scholar Crossref Search ADS WorldCat Pitkänen P , Snellman M , Vuorinen U . On the origin and chemical evolution of grounwater at the Olkiluoto site . Helsinki, Finland. Posiva Oy . 1996 : Working Report 96-04 . COPAC Pjevac P , Schauberger C , Poghosyan L et al. AmoA-Targeted Polymerase Chain Reaction Primers for the Specific Detection and Quantification of Comammox Nitrospira in the Environment . Front Microbiol . 2017 ; 8 : 1508 Google Scholar Crossref Search ADS PubMed WorldCat Plugge CM , Zhang W , Scholten JCM et al. Metabolic flexibility of sulfate-reducing bacteria . Front Microbiol . 2011 ; 2 : 1 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Purkamo L , Bomberg M , Kietäväinen R et al. Microbial co-occurrence patterns in deep Precambrian bedrock fracture fluids . Biogeosciences . 2016 ; 13 : 3091 – 108 . Google Scholar Crossref Search ADS WorldCat Purkamo L , Bomberg M , Nyyssönen M et al. Dissecting the deep biosphere: retrieving authentic microbial communities from packer-isolated deep crystalline bedrock fracture zones . FEMS Microbiol Ecol . 2013 ; 85 : 324 – 37 . Google Scholar Crossref Search ADS PubMed WorldCat Purkamo L , Bomberg M , Nyyssönen M et al. Heterotrophic communities supplied by ancient organic carbon predominate in deep fennoscandian bedrock fluids . Microb Ecol . 2015 ; 69 : 319 – 32 . Google Scholar Crossref Search ADS PubMed WorldCat Quast C , Pruesse E , Yilmaz P et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools . Nucleic Acids Res . 2013 ; 41 : D590 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat Rajala P , Bomberg M , Kietäväinen R et al. Rapid reactivation of deep subsurface microbes in the presence of C-1 compounds . Microorganisms . 2015 ; 3 : 17 – 33 . Google Scholar Crossref Search ADS PubMed WorldCat Rajala P , Bomberg M . Reactivation of deep subsurface microbial community in response to methane or methanol amendment . Front Microbiol . 2017 ; 8 : 431 . Google Scholar Crossref Search ADS PubMed WorldCat Sahl JW , Schmidt R , Swanner ED et al. Subsurface microbial diversity in deep-granitic-fracture water in Colorado . Appl Environ Microbiol . 2008 ; 74 : 143 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat Schleper C and Nicol GW . Ammonia-oxidising archaea--physiology, ecology and evolution . Adv. Microb. Physiol. 2010 ; 57 : 1 – 41 . Google Scholar Crossref Search ADS PubMed WorldCat Schloss PD , Westcott SL , Ryabin T et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities . Appl Environ Microbiol . 2009 ; 75 : 7537 – 41 . Google Scholar Crossref Search ADS PubMed WorldCat Sohlberg E , Bomberg M , Miettinen H et al. Revealing the unexplored fungal communities in deep groundwater of crystalline bedrock fracture zones in Olkiluoto, Finland . Front Microbiol . 2015 ; 6 : 1 – 11 . Google Scholar Crossref Search ADS PubMed WorldCat Stahl D.A , Amann R . Development and application of nucleic acid probes in bacterial systematics . In: Stackebrandt E , Goodfellow M (eds). Nucleic Acid Techniques in Bacterial Systematics . Chichester, England : John Wiley & Sons, Ltd , 1991 , 205 – 48 . Google Preview WorldCat COPAC Stevens TO , McKinley JP . Lithoautotrophic microbial ecosystems in deep basalt aquifers . Science . 1995 ; 270 : 450 – 5 . Google Scholar Crossref Search ADS WorldCat Stotler RL , Frape SK , Ruskeeniemi T et al. The interglacial–glacial cycle and geochemical evolution of Canadian and Fennoscandian Shield groundwaters . Geochim Cosmochim Acta . 2012 ; 76 : 45 – 67 . Google Scholar Crossref Search ADS WorldCat Sun L , Toyonaga M , Ohashi A et al. Lentimicrobium saccharophilum gen. nov., sp. nov., a strictly anaerobic bacterium representing a new family in the phylum bacteroidetes, and proposal of lentimicrobiaceae fam. nov . Int J Syst Evol Microbiol . 2016 ; 66 : 2635 – 42 . Google Scholar Crossref Search ADS PubMed WorldCat Van Kessel MAHJ , Speth DR , Albertsen M et al. Complete nitrification by a single microorganism . Nature . 2015 ; 528 : 555 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat Wang J , Krause S , Muyzer G et al. Spatial patterns of iron- and methane-oxidizing bacterial communities in an irregularly flooded, riparian wetland . Front Microbiol . 2012 ; 3 : 1 – 13 . Google Scholar PubMed WorldCat White TJ , Bruns S , Lee S et al. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics . In: PCR Protocols: A Guide to Methods and Applications , Academic Press, Inc , Cambridge, Massachusetts, USA. 1990 ; 315 – 22 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Whitman WB , Coleman DC , Wiebe WJ . Prokaryotes: the unseen majority . Proc Natl Acad Sci U S A . 1998 ; 95 : 6578 – 83 . Google Scholar Crossref Search ADS PubMed WorldCat Willems A , De Ley J , Gillis M et al. Comamonadaceae, a New Family Encompassing the Acidovorans . Int J Syst Bacteriol . 1991 ; 41 : 445 – 50 . Google Scholar Crossref Search ADS WorldCat Wu X , Holmfeldt K , Hubalek V et al. Microbial metagenomes from three aquifers in the Fennoscandian shield terrestrial deep biosphere reveal metabolic partitioning among populations . ISME J . 2016 ; 10 : 1192 – 203 . Google Scholar Crossref Search ADS PubMed WorldCat Yilmaz P , Parfrey LW , Yarza P et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks . Nucleic Acids Res . 2014 ; 42 : D643 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes Present address: University of St Andrews, School of Earth and Environmental Sciences, KY16 9AL, St Andrews, UK. Present address: University of Helsinki, Department of Physics, P.O. Box 64, 00014 Helsinki, Finland. © FEMS 2018. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
TI - Diversity and functionality of archaeal, bacterial and fungal communities in deep Archaean bedrock groundwater
JF - FEMS Microbiology Ecology
DO - 10.1093/femsec/fiy116
DA - 2018-08-01
UR - https://www.deepdyve.com/lp/oxford-university-press/diversity-and-functionality-of-archaeal-bacterial-and-fungal-o3XgWl9rl0
VL - 94
IS - 8
DP - DeepDyve
ER -