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Microbiology of Olkiluoto and ONKALO Groundwater– Results and Interpretations 2010–2013
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POSIVA OY
Olki luoto
FI-27160 EURAJOKI, F INLAND
Phone (02) 8372 31 (nat. ) , (+358-2-) 8372 31 ( int. )
Fax (02) 8372 3809 (nat. ) , (+358-2-) 8372 3809 ( int. )
November 2015
Working Report 2015-42
Karsten Pedersen, Andreas Bengtsson, Johanna Edlund,
Lena Eriksson, L inda Johansson, L isa Rabe
November 2015
Working Reports contain information on work in progress
or pending completion.
Karsten Pedersen, Andreas Bengtsson, Johanna Edlund,
Lena Eriksson, L inda Johansson, L isa Rabe
Microbial Analyt ics Sweden AB
Working Report 2015-42
Microbiology of Olkiluoto and ONKALO Groundwater– Results and Interpretations 2010–2013
ABSTRACT This report summarizes the microbiological research and analyses performed in Olkiluoto and ONKALO up to December 2013. Microbiology cultivation, biomass and DNA data were assembled from 28 deep drillhole groundwater samples in Olkiluoto ranging in depth from -42 to -1116 meter above sea level (masl) and 15 groundwater samples from 8 drillholes in ONKALO ranging in depth from -14.6 to -417.5 masl. Biomass was determined by counting total numbers of microbial cells (TNC) and concentrations of ATP. The aerobic cultivation method used comprised aerobic plate counts. Anaerobic MPN methods were used to determine nitrate-, iron-, manganese-, and sulphate-reducing bacteria, acetogenic bacteria, and methanogens. Molecular methods for analysis of diversity and abundance of microorganisms have been continuously developed and applied to groundwater samples. These methods included the sampling of DNA, extraction of nucleic acids, sequencing of parts of the Bacteria and Archaea 16S rDNA gene using the high throughput sequencing platforms 454 pyrotag and Illumina paired end sequencing, respectively. The results of these analyses have been merged and interpreted, and the outcome is reported here. The four different methodological approaches for biomass related analysis correlated well. The methods focus on different characters of microbial cells; TNC analyses whole cells with a microscope, ATP analyses a cell component with a biochemical method, MPN is based on cultivation and DNA sequencing shows community composition. This spread of analytical focus between the methods indicated that the biomass related information in this and previous reports from Olkiluoto and ONKALO are reliable and reflect a diverse set of biomass related characters of the analysed microorganisms. The distribution of the MPN data over depth from 2010−2013 followed the distribution found earlier. There were generally more cultivable microorganisms between depths -200 and -400 masl as compared to the shallower depths of -50 and -200 masl. These new results agree with the previous results suggesting that microorganisms are more active in the border area between the sulphate rich groundwater that overlies deeper, methane rich groundwater at about 300 m depth. The MPN analyses have shown that all the physiological groups analysed for were present in Olkiluoto deep groundwater and in ONKALO groundwater. Further, it could be shown that microbial abundance, and likely also activity, was positively correlated with the presence of methane and sulphate; in the absence of one of these groundwater components, numbers of cultivable microorganisms diminished. During 2012, analysis of molecular diversity using high throughput sequencing platforms were introduced. The molecular results agreed well with the cultivation and biomass results. Sequences belonging to iron-, manganese- and sulphate-reducing bacteria were found. Archaea sequences, representative for methane producing microorganisms were also detected and sequences that have been identified as belonging to anaerobic methane-oxidizing consortia were also found. Keywords: microbiology, Olkiluoto, ONKALO, cultivation, biomass, DNA, methods.
OLKILUODON JA ONKALON POHJAVESINÄYTTEIDEN MIKROBIOLOGI-SET TULOKSET JA TULKINNAT VUOSILTA 2010−2013 TIIVISTELMÄ Raportissa esitetään yhteenveto mikrobiologisista tutkimuksista ja analyyseistä, jotka on tehty Olkiluodossa ja ONKALOssa joulukuuhun 2013 mennessä. Mikrobiologisten viljelyiden, biomassa- ja DNA-analyysien tulokset on kerätty yhteen Olkiluodon 28 syvästä kairareikänäytteestä syvyydeltä -42...-1116 m sekä 15 ONKALO-näytteestä 8 eri näytepisteestä syvyydeltä -15...-418 m. Biomassa määritettiin laskemalla mikrobi-solujen kokonaismäärä (TNC, total number of cells) sekä ATP-pitoisuus. Aerobinen viljelymenetelmä perustui solujen laskentaan petrimaljalta. Anaerobista MPN menetelmää käytettiin nitraatin-, raudan-, mangaanin- ja sulfaatinpelkistäjäbakteerien sekä asetogeenien ja metanogeenien määrittämiseen. Molekylaariset menetelmät mikro-organismien monimuotoisuuden ja lukumäärän määrittämiseksi sekä niiden sovelta-minen pohjavesinäytteille kehittyvät jatkuvasti. Nämä menetelmät käsittivät näytteen-otot DNA:n keräystä varten, DNA:n eristyksen sekä bakteereiden ja arkeonien 16S rDNA geenin sekvensoinnin. Bakteereiden 16S rDNA geeni sekvensoitiin 454 pyrotag ja arkeonien 16 rDNA Illuminan suuren kapasiteetin sekvensointimenetelmillä. Näiden analyysien tulokset on kerätty ja arvioitu tässä raportissa. Neljä eri biomassaa määrittävää analyysimenetelmää korreloivat toistensa kanssa. Menetelmät määrittivät mikrobisolujen eri ominaisuuksia; TNC analyyseissä määri-tettiin mikroskooppisesti solumääriä, ATP analyyseissä solujen aineosia biokemial-lisella metodilla, MPN perustui viljelyihin ja DNA-sekvensoinnissa määritettiin mikrobiyhteisöjen monimuotoisuutta. Erilaisten menetelmien käyttö osoitti, että bio-massaan keskittyneet analyysimenetelmät tässä ja aiemmissa raporteissa ovat luotettavia ja heijastavat analysoitujen mikro-organismien ominaisuuksia ja monipuolisuutta. Vuosina 2010−2013 analysoidut MPN tulokset noudattivat aiemmin havaittua jakaumaa näytteenottosyvyyden mukaan. Viljelykelpoisia mikro-organismeja havaittiin enemmän syvyysvälillä -200...-400 m verrattuna syvyyteen -50...-200 m. Uudet tulokset tukivat aikaisempia tuloksia, joissa on havaittu, että mikro-organismit ovat aktiivisempia noin 300 m syvyydellä, jossa sulfaattipitoinen vesi sekoittuu syvemmällä olevan metaani-pitoisen pohjaveden kanssa. MPN analyysit osoittivat, että eri mikrobiryhmät olivat läsnä kaikissa Olkiluodon pohjavesissä. Lisäksi voitiin todeta, että mikrobien määrä ja todennäköisesti myös aktiivisuus korreloivat positiivisesti metaanin ja sulfaatin läsnäolon kanssa; vastaavasti, jos joko metaania tai sulfaattia ei vedessä ollut, niin viljeltyjen mikrobien määrä väheni. Vuonna 2012 aloitettiin molekylaaristen menetelmien käyttö suuren kapasiteetin sekvensointimenetelmillä. Molekylaariset menetelmät tukivat viljely- ja biomassa-analyysien tuloksia antaen vastaavia tuloksia. Raudan-, mangaanin- ja sulfaatinpelkis-täjäbakteerien sekvenssejä löydettiin. Lisäksi löydettiin metaania tuottavien arkeonien sekvenssejä sekä sekvenssejä, joiden on katsottu kuuluvan anaerobisiin metaanin hapetusta tekeviin mikrobiyhteisöihin. Avainsanat: mikrobiologia, Olkiluoto, ONKALO, viljely, biomassa, DNA, menetelmät.
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TABLE OF CONTENTS
1 INTRODUCTION ............................................................................................................. 5
1.1 Olkiluoto (OL) investigations, 20102013 .................................................................... 5 1.2 ONKALO (ONK) investigations, 20102013 ................................................................ 6
2 MATERIALS AND METHODS ......................................................................................... 7
2.1 Sampling procedures ................................................................................................... 7 2.1.1 Sampling ONKALO groundwater for microbiology ............................................... 7 2.1.2 Sampling deep Olkiluoto groundwater for microbiology using PAVE ................... 7
2.2 Methods for microbiological analyses .......................................................................... 7 2.2.1 Determining total number of cells ......................................................................... 7 2.2.2 ATP analysis ........................................................................................................ 8 2.2.3 Determining cultivable aerobic bacteria ............................................................... 8 2.2.4 Analysis of most probable number of cultivable anaerobic microorganisms ........ 8 2.2.5 DNA extraction from groundwater ........................................................................ 8 2.2.6 454 pyrotag sequencing, processing and analysis of bacterial DNA from groundwater ......................................................................................................... 9 2.2.7 Illumina paired end sequencing of archaeal DNA from groundwater ................... 9
3 RESULTS ...................................................................................................................... 15
3.1 Deep Olkiluoto and ONKALO groundwater microbiology .......................................... 15 3.1.1 Total number of cells .......................................................................................... 15 3.1.2 ATP .................................................................................................................... 16 3.1.3 Cultivable heterotrophic aerobic bacteria ........................................................... 18 3.1.4 Most probable number cultivation and TNC ....................................................... 18 3.1.5 Most probable number of nitrate-reducing bacteria ........................................... 18 3.1.6 Most probable number of iron- and manganese-reducing bacteria ................... 18 3.1.7 Most probable number of sulphate-reducing bacteria ........................................ 19 3.1.8 Most probable number of acetogens .................................................................. 20 3.1.9 Most probable number of methanogens ............................................................ 20 3.1.10 Diversity of cultivable microorganisms in Olkiluoto and ONKALO groundwater ................................................................................................................... 20
3.2 Relationships between microbiology and hydrogeochemistry data ........................... 35 3.3 Bacterial and archaeal genomic diversity in ONKALO groundwater .......................... 36
3.3.1 Groundwater bacterial 16S rDNA v6v4 sequence diversity ............................... 37 3.3.2 Groundwater archaeal 16S rDNA v6 sequence diversity ................................... 37 3.3.3 Distribution of sulphate-reducing bacteria over depth ........................................ 38
4 DISCUSSION................................................................................................................. 47
4.1 Diversity and numbers of microorganisms ................................................................. 47 4.1.1 Cultivable diversity ............................................................................................. 47 4.1.2 Genomic diversity ............................................................................................... 49
4.2 Sequence information, what can be concluded? ....................................................... 51 4.2.1 Change of sequencing platform ......................................................................... 51 4.2.2 Bacteria .............................................................................................................. 51 4.2.3 Archaea .............................................................................................................. 52
4.3 Numbers and activity of sulphate-reducing bacteria in Olkiluoto ................................ 52
5 ACKNOWLEDGEMENTS .............................................................................................. 55
6 REFERENCES .............................................................................................................. 57
A. APPENDIX ..................................................................................................................... 61
ABSTRACT
TIIVISTELM
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Key to abbreviations used frequently in the text Abbreviation Meaning Brief description AA Autotrophic acetogens Microbes able to produce acetate from carbon dioxide
and hydrogen
AGW Analytical-grade water Purified distilled water
AM Autotrophic methanogens Microbes able to produce methane from carbon dioxide and hydrogen
AOM Anaerobic oxidation of methane Microbes able to oxidize methane to hydrogen and carbon dioxide in oxygen-free environments
AODC Acridine orange direct count Method based on nucleic acid staining for determining cell numbers
ATP Adenosine triphosphate Energy carrier in living organisms
CFU Colony-forming unit A cell that has divided repeatedly, e.g., on an agar plate, forming a dense colony of many identical cells
CHAB Cultivable heterotrophic aerobic bacteria
Microbes able to live on oxygen and organic carbon and that grow in the laboratory
DNA Deoxyribonucleic acid The genetic code, which builds the genome unique to each organism
dsrB dissimilatory sulfite reductase β-subunit Functional gene involved in sulphate reduction, biomarker of sulphate reducer diversity
HA Heterotrophic acetogens Microbes able to produce acetate from organic carbon
HM Heterotrophic methanogens Microorganisms able to produce methane from organic carbon
IRB Iron-reducing bacteria Microbes able to reduce iron(III) in their respiration
MPN Most probable number Method for enumerating microbes
MRB Manganese-reducing bacteria Microbes able to reduce manganese(IV) in their respiration
NGS Next generation sequencing Sequecing methods that rapidly generate a large number of sequences to sequence libraries
NRB Nitrate-reducing bacteria Microbes able to reduce nitrate in their respiration
PCR Polymerase chain reaction Technique used to exponentially amplify DNA
qPCR Quantitative polymerase chain reaction Technique used to quantify DNA using the PCR method
RNA Ribonucleic acid Part of the ribosome, which constructs all the proteins in an organism
rDNA Ribosomal DNA DNA encoding for the ribosome
SRB Sulphate-reducing bacteria Microbes able to reduce sulphate in their respiration
TNC Total number of cells The number of cells in a water sample or on a solid phase, usually determined by means of microscopy using the AODC method
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1 INTRODUCTION
The main effects of microorganisms in the context of a KBS-3 type repository (SKB 2010) for radioactive waste in the bedrock of Olkiluoto are:
Bio-corrosion of construction materials,
bio-mobilization and bio-immobilization of radionuclides, and the effects of microbial metabolism on radionuclide mobility,
oxygen reduction and maintenance of anoxic and reduced conditions.
Because of the above potentially important effects of microorganisms, microbiology research initiatives constitute an important part of consecutive research, development, and technical design (YJH) programmes for Olkiluoto. Microbial processes comprise many biochemical oxidation and reduction reactions that in various ways influence the environment in which microorganisms are active. The successful and conclusive study of microorganisms and their processes at depth requires a range of methodologies. The sampling procedures must be adapted to drilling and tunnelling, and several sampling methods have been used, such as the pumping of deep drillholes, sampling with down-hole samplers, and the draining of tunnel drillholes. The microbiology programmes examining Olkiluoto and ONKALO groundwater have included methods for quantifying microorganisms determined as the total number of cells (TNC), the amount of the ubiquitous cell constituent adenosine triphosphate (ATP), the numbers of culturable heterotrophic aerobic bacteria (CHAB), and most probable numbers (MPN) of nine physiological groups. These nine groups were nitrate-, iron-, manganese-, and sulphate-reducing bacteria (NRB, IRB, MRB, and SRB, respectively), aerobic methane-oxidizing bacteria (MOB), autotrophic and heterotrophic acetate-producing bacteria (AA and HA, respectively), and autotrophic and heterotrophic methane-producing microorganisms (AM and HM, respectively). Details about the methods can be found elsewhere (Hallbeck and Pedersen 2008; Pedersen et al. 2012). The diversity of microorganisms in Olkiluoto groundwater was initially determined from the MPN analyses. During 2012, analyses of genomic DNA using high throughput sequencing were added to the diversity analysis programme. This report presents the microbiological research and analyses performed in Olkiluoto and ONKALO up from 2010 to December 2013, in comparison with to previously obtained data (Pedersen et al. 2012).
1.1 Olkiluoto (OL) investigations, 20102013
Microbiology cultivation data were assembled from 28 deep drillhole groundwater samples in Olkiluoto ranging in depth from 42 to 1116 masl. Sampling and analysis protocols had previously been adapted and tested for quality and reproducibility (Pedersen 2008) and contamination controls were also performed.
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1.2 ONKALO (ONK) investigations, 20102013
A Microbiology cultivation data were assembled from groundwater samples collected from 12 drillhole samples in ONKALO ranging in depth from 7.1 to 417.5 masl. Sampling and analysis protocols had previously been adapted and tested for quality and reproducibility (Pedersen 2008). A method for sampling of genomic DNA by on-line pressure filtration was developed and applied on 8 groundwater samples from 6 drillholes in ONKALO.
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2 MATERIALS AND METHODS
Several different methods have been employed when investigating process-related parameters such as microbial biomass, diversity, and activity and each method has different characteristic strengths and weaknesses. Therefore, a multi-pronged approach has been applied in microbiological investigations. For example, biomass analyses (i.e., the amount of living organisms) have utilized microscopic counts, analysis of biochemical components, cultivation, and nucleic acid methods, while microbial diversity has been examined using cultivation and nucleic acid analyses. A detailed review of the applied microbiological analysis methods that describes the strengths and weaknesses of each method together with information about reproducibility, resolution, detection range, and uncertainties has been presented elsewhere (Pedersen et al. 2012). Materials, methods, and analytical procedures are, therefore, only briefly presented here. This chapter then focuses on new techniques and recent development of methods that are given here in greater detail than previously described methods and procedures.
2.1 Sampling procedures
Deep groundwater samples for the analysis of microbiology were taken with the PAVE sample system as described previously (Pedersen 2008). New in 2010–2013 was sampling for complete microbiology analyses in ONKALO during the tunnel construction. New in 2013 was also the application of high-throughput sequencing by means of 454 pyrotag and Illumina paired end sequencing DNA analysis of deep groundwater from ONKALO drillholes.
2.1.1 Sampling ONKALO groundwater for microbiology
The sampled ONKALO drillholes and sampling dates are listed in Table 2-1. The drillholes were opened before and during sampling, and the flow rates adjusted to a slow flow. The samples were collected in sterilized anaerobic 120 mL glass bottles, sealed with butyl rubber and aluminium crimp seals. The bottles were shipped to the laboratory in Mölnlycke over night at 10 °C. The positions of the sampled ONKALO drillholes (ONK) are shown in Figure 2-1.
2.1.2 Sampling deep Olkiluoto groundwater for microbiology using PAVE
The deep groundwater was sampled using the PAVE system. The position of the sampled surface drillholes (OL-KR) are shown in Figure 2-2 and the sampling scheme is shown in Table 2-2.
2.2 Methods for microbiological analyses
2.2.1 Determining total number of cells
The total number of cells (TNC) was determined using the acridine orange direct count (AODC) method as devised by Hobbie et al. (1977) and modified by Pedersen and Ekendahl (1990).
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2.2.2 ATP analysis
The ATP Biomass Kit HS (no. 266-311; BioThema, Handen, Sweden) for determining total ATP in living cells was used. The ATP biomass method used in this work has been described, tested in detail and evaluated for use with Fennoscandian groundwater, including Olkiluoto (Eydal and Pedersen 2007).
2.2.3 Determining cultivable aerobic bacteria
Petri dishes containing agar with nutrients were prepared as described elsewhere (Pedersen and Ekendahl 1990) for determining the numbers of cultivable heterotrophic aerobic bacteria (CHAB) in groundwater samples. Ten-times dilution series of culture samples were made in sterile Analytical Grade Water (AGW) (Millipore Elix 3, Millipore, Solna, Sweden) with 1.0 g L1 of NaCl and 0.1 g L1 K2HPO4; 0.1 mL portions of each dilution were spread with a sterile rods on the plates in triplicate. The plates were incubated for between 7 and 9 days at 20°C, after which the number of colony forming units (CFU) was counted; plates with between 10 and 200 colonies were counted.
2.2.4 Analysis of most probable number of cultivable anaerobic microorganisms
Anaerobic media for determining the MPN of different anaerobic microorganisms in groundwater were prepared according to the procedures described by Widdel and Bak (1992). The specific media details were formulated based on previously measured chemical data from Olkiluoto. This allowed the formulation of artificial media that most closely mimicked in situ groundwater chemistry for optimal microbial cultivation (Haveman and Pedersen 2002). Media for the nitrate-reducing bacteria (NRB), iron-reducing bacteria (IRB), manganese-reducing bacteria (MRB), sulphate-reducing bacteria (SRB), autotrophic acetogens (AA), heterotrophic acetogens (HA), autotrophic methanogens (AM), and heterotrophic methanogens (HM) were autoclaved and anaerobically dispensed according to the formulations described elsewhere (Greenberg et al. 1992; Pedersen 2008). The MPN procedures resulted in protocols with tubes that scored positive or negative for growth. The results of the analyses were rated positive or negative compared with control levels. Three dilutions with five parallel tubes were used to calculate the MPN of each group, according to the calculations found in Greenberg et al.(1992). The lower and upper 95% confidence intervals for the MPN method applied to five parallel tubes equalled approximately 1/3 and 3 times the obtained values, respectively. The detection limit was 0.2 cells mL1.
2.2.5 DNA extraction from groundwater
Total genomic DNA from groundwater was extracted according to the manufacturer’s protocol using the MO BIO PowerWater DNA isolation kit (cat. no. 12888) from MO BIO Laboratories, Carlsbad, CA, USA. The extraction volume of the MO BIO extraction kits used in this work was 100 L. Groundwater was pressure filtered using high-pressure, stainless steel 47 mm filter holders (X4504700; Millipore AB, Solna, Sweden) equipped with the water filters from PowerWater kit filter units (MO BIO Laboratories). The filter holder was equipped with a pressure relief valve (Swagelok
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SS-RL3S6MM; SWAFAB, Sollentuna, Sweden) and a manometer that enabled adjustment of a pressure drop over the filter between 200 and 400 kPa relative to the ambient aquifer pressure. Groundwater was filtered at a flow rate of 0.05–0.2 L min–1 from the sampled ONK tunnel drillholes (Table 2-1). Total extracted nucleotide concentrations were measured using the ND-1000 UV-vis spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) and double-stranded (ds) DNA concentrations were measured fluorometrically using the Stratagene MX3005p fluorometer with MXPro software (Agilent Technologies, Santa Clara, CA, USA) and the Quant-it Picogreen reagent kit from Molecular Probes (cat. no. P7589; Invitrogen, San Diego, CA, USA), according to the manufacturer’s specifications. The extracted DNA was stored at −20°C and subsequently used for sequencing.
2.2.6 454 pyrotag sequencing, processing and analysis of bacterial DNA from groundwater
The degenerate forward 518F (5´-CCAGCAGCYGCGGTAA-3´) and reverse 1064R (5´-CGACRRCCATGCANCACCT-3´) primers targeting the v4v6 region of the bacterial 16S rDNA were used for pyrotag amplification on a 454 Roche GS-FLX (454 Life Sciences, Branford, CT, USA) using the Roche Titanium protocol for generating reads as part of the Census of Deep Life initiative (http://www.deepcarbon.net/content/deep-life). Each read was trimmed for primer bases from the beginning and the end of each read, and sequences likely to be of low quality based on assessment of pyrotag sequencing error rates were removed (Huse et al. 2007). The 454 pyrotag sequence processing to assign a taxonomic classification was done using the Global Alignment for Sequence Taxonomy (GAST) tag mapping methodology (Sogin et al. 2006) in which the reference database of 16S rDNA, RefSSU, was based on the SILVA database (Pruesse et al. 2007). If two-thirds or more of the full-length sequences shared the same assigned operational taxonomic unit (OTU), the tag was assigned to that OTU. Tags that did not match any reference tag according to BLAST were not given a taxonomic assignment. Further details of methodology and library construction can be found elsewhere (Marteinsson et al. 2013). The representativeness of sequences was tested by rarefaction analysis and the Chao index was used to estimate OTU richness. To statistically estimate the abundance and evenness of each sample, Shannon and Simpson indices were calculated. Distance calculations for sequence similarities were done using the Morsita–Horn algorithm. Sequences appearing at approximately 3% frequency-abundance or more were searched using BLAST against the GenBank nucleotide database and sample sites of the closest match were registered. These sequences were aligned using BioEdit 7.1.3.0 and the identity of sequences between samples was analysed. The data generated have been submitted to the NCBI Sequence Read Archive (SRA) with accession numbers: ONK-PVA1, SRX268397; ONK_PVA3, SRX540785; ONK_PVA5, SRX540786; ONK-PVA6, SRX268398; ONK_PVA06_2, SRX540783; ONK_PVA10, SRX540784; ONK-KR15, SRX268395; ONK_KR15_2, SRX540782.
2.2.7 Illumina paired end sequencing of archaeal DNA from groundwater
The degenerate forward 958F (5´- AATTGGANTCAACGCCGG -3´) and reverse 1048R (5´- CGRCRGCCATGYACCWC -3´) primers targeting the v6 region of the archaeal 16S rDNA were used for Illumina amplification. The methodology followed
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the method described by Eren et al (2013). Custom fusion primers for PCR consisted of the Illumina adaptor, 12 different inline barcodes (forward primer) or 8 dedicated indices (reverse primer), and conserved regions of the V6 sequence. This use of 96 unique barcode-index combinations allowed multiplexing 96 samples per lane. Paired indices with dual indexing reads could further increase the level of multiplexing. For each of the libraries, the PCR was carried out in triplicate 33 uL reaction volumes with an amplification cocktail containing 1.0 U Platinum Taq Hi-Fidelity Polymerase (Life Technologies, Carlsbad CA), 1X Hi-Fidelity buffer, 200 uM dNTP PurePeak DNA polymerase mix (Pierce Nucleic Acid Technologies, Milwaukee, WI), 1.5 mM MgSO4 and 0.2 uM of each primer. Approximately 10–25 ng template DNA were added to each PCR and ran a no-template control for each primer pair. Cycling conditions were: an initial 94ºC, 3 minute denaturation step; 30 cycles of 94ºC for 30s, 60ºC for 60s, and 72ºC for 90s; and a final 10 minute extension at 72ºC. The triplicate PCR reactions were pooled after amplification and purified using a Qiaquick PCR 96-well PCR clean up plate (Qiagen, Valencia CA). Purified DNA was eluted in 30 uL of Qiagen buffer EB. PicoGreen quantitation (Life Technologies, Carlsbad CA) provided a basis for pooling equimolar amounts of product. After size-selecting products of 200–240 bp on 1% agarose using Pippin Prep (SageScience, Beverly MA), we employed qPCR (Kapa Biosystems, Woburn MA) to measure concentrations prior to sequencing on one lane of an Illumina Hiseq 100 cycle paired-end run. The remaining 90% of the lane was dedicated to PhiX DNA and served as the run control. The combination of CASAVA 1.8.2 to identify reads by index and a custom Python script that resolved barcodes demultiplexed the datasets. The data generated have been submitted to the NCBI Sequence Read Archive (SRA) with accession numbers: ONK_PVA1, SRX651451; ONK_PVA3, SRX651453; ONK_PVA5, SRX651454; ONK_PVA06, SRX651455; ONK_PVA06_2, SRX651449; ONK_PVA10, SRX651452; ONK_KR15, SRX651447; ONK_KR15_2, SRX651448.
Visualization and statistical analyses
The 454 and Illumina data were evaluated using the Visualization and Analysis of Microbial Population Structure (VAMPS) website (www.vamps.ml.edu). Data graphics design and statistical analyses were performed in Statistica 10 (Statsoft, Tulsa, OK, USA).
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Figure 2-1. Tunnel drawing showing the positions of the drillholes sampled ONKALO.
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Figure 2-2. Map showing the positions of OL-KR drillholes in Olkiluoto.
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Table 2-1. ONKALO groundwater sample and analysis scheme 2010–2013.
Sample Date sampled
(Y-M-D) Posiva
number Depth in ONKALO
(masl) TNC ATP CHAB MPN
16S rDNA Sequencing
1 ONK-PVA1 2012-04-16 2211 -14.6 × × × × ×
2 ONK-PVA3 2012-04-16 2213 -78.5 × × × × ×
3 ONK-PVA5 2012-04-17 2215 -228.7 × × × × ×
4 ONK-PVA6 2010-09-23 - -327.0 × × × ×
5 ONK-PVA6 2012-04-17 2216 -327.0 × × × × ×
6 ONK-PVA6 2012-09-04 - -327.0 ×
7 ONK-PVA8 2010-09-23 - -276.4 × × × ×
8 ONK-PVA8 2012-04-18 2217 -276.4 × × × ×
9 ONK-PVA8 2013-03-19 2602 -276.4 × × × ×
10 ONK-PVA9 2011-09-20 - -417.5 × × × ×
11 ONK-PVA9 2013-12-10 2844 -417.5 × × × ×
12 ONK-PVA10 2012-04-18 2219 -366.45 × × × × ×
13 ONK-KR15 2012-01-11 2089 -399.0 × × × ×
14 ONK-KR15 2012-04-17 - -399.0 ×
15 ONK-KR15 2012-09-04 - -399.0 ×
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Table 2-2. Olkiluoto groundwater sample and analysis scheme 2010–2013.
Sample Date sampled (Y-
M-D) Posiva
number Section
(m) Depth (masl)
TNC ATP CHAB MPN
1 OL-KR6 2010-11-23 1773 125-130 -97 × × × × 2 OL-KR6 2010-09-28 1702 135-137 -102 × × × × 3 OL-KR6 2011-02-08 1823 98.5-100.5 -74 × × × × 4 OL-KR6 2013-08-22 2630 422-425 -330 × × × × 5 OL-KR11 2010-08-17 1684 415-423 -373 × × × × 6 OL-KR30 2011-03-02 1671 50.6-54.6 -42 × × × × 7 OL-KR39 2010-06-16 1585 400-409 -347 × × × × 8 OL-KR40 2011-03-09 1824 788.5-794 -707 × × × × 9 OL-KR44 2010-08-10 1681 116-120 -95 × × × ×
10 OL-KR44 2010-06-30 1621 651-655 -539 × × × × 11 OL-KR46 2012-11-06 2393 207.9-210.4 -191 × × × × 12 OL-KR46 2013-03-27 2526 493-495 -457 × × × × 13 OL-KR46 2013-11-06 2772 570.5-573.5 -530 × × × × 14 OL-KR50 2010-09-28 1703 424-429 -408 × × × × 15 OL-KR50 2010-03-02 1495 751-756 -722 × × × × 16 OL-KR50 2010-11-18 1774 363-369.5 -350 × × × × 17 OL-KR51 2010-12-20 1784 292-298 -238 × × × × 18 OL-KR51 2010-10-26 1704 422-427.5 -344 × × × × 19 OL-KR53 2011-09-27 1991 65.5-73.5 -46 × × × × 20 OL-KR53 2011-08-10 1953 153-155.5 -113 × × × × 21 OL-KR53 2011-05-30 1872 265-268.5 -198 × × × × 22 OL-KR54 2013-04-16 2527 364.5-368 -329 × × × × 23 OL-KR55 2012-08-28 2305 863-866 -675 × × × × 24 OL-KR55 2013-05-20 2528 863-866 -675 × × × × 25 OL-KR55 2013-07-02 2633 286.9-288.4 -233 × × × × 26 OL-KR56 2012-08-15 2306 1154-1158 -1116 × × × × 27 OL-KR56 2013-06-11 2558 1154-1158 -1116 × × × × 28 OL-KR57 2013-06-25 2617 57-61 -43 × × × ×
15
3 RESULTS
3.1 Deep Olkiluoto and ONKALO groundwater microbiology
A total of 28 complete analyses were performed of Olkiluoto deep groundwater samples in 2010–2013 (Table 3-1) and the complete dataset including statistical details can be found in Table A-1 to Table A-3. Ten complete and five partial analyses were performed of ONKALO groundwater samples in 2010–2013 (Table 3-1) and the complete dataset including statistical details can be found in Table A-4 to Table A-6. These new data have been merged with all previously obtained data from Olkiluoto first sampled in 1997 and from ONKALO first sampled 2005. Four consecutive sampling campaigns of shallow Olkiluoto groundwater were performed 2004–2006 and the obtained data are merged with deep Olkiluoto groundwater data when judged relevant for interpretations and discussion. Over the years sample depths have varied for deep Olkiluoto and ONKALO groundwater samples. The distribution of sample depth is shown in Figure 3-1 and Figure 3-2. Obviously, the first samples from ONKALO came from shallow depths because sampling coincided with construction and deepening of the tunnel. The results are generally presented here in a structure that distinguishes Olkuiluoto samples from ONKALO samples. It is, consequently, possible to evaluate if the distribution of microbial numbers and diversity in groundwater from the vicinity of the ONKALO tunnel correlate with equivalent observations from surface drillholes. Further, data are presented cross-wise over depths, sample years and drillholes to enable detailed comparisons, analysis and interpretation of a data set that has grown significantly over almost a decade of investigations to approximately 150 complete observations, each comprising TNC, ATP. CHAB, NRB, IRB, MRB, SRB, AA, HA, AM and HM.
3.1.1 Total number of cells
Figure 3-3 shows all TNC values obtained from Olkiluoto and ONKALO from 19972013. The distribution of the ONKALO data overlay the Olkiluoto data and, there were, consequently, not any obvious distribution difference between the sampled areas. The average of all TNC data in this figure is 1.60 105 cells mL1 (n = 143). The data set comprises three different sample areas, shallow Olkiluoto groundwater from depths ranging between 0 to 25 masl with samples from 20052006, Olkiluoto groundwater below 25 masl and groundwater sampled in ONKALO. The averages for shallow and deep Olkiluoto groundwater and ONKALO groundwater were 3.94 105 cells mL1 (n = 29), 1.07 105 cells mL1 (n = 78) and 1.05 105 cells mL1 (n = 36), respectively. The averages for the period 2010–2013 were somewhat smaller than the overall average in Olkiluoto groundwater i.e. 1.14 105 cells mL1 (n=40) in Olkiluoto groundwater and 0.95 105 cells mL1 (n=12) in ONKALO groundwater. There was no obvious trend with depth for the TNC data in Olkiluoto or ONKALO shown in Table 3-1, but there is an ambiguous profile in Figure 3-3 where the TNC show peak values in the shallow depths and the depths between 300 to –500 masl and very low values, smaller than 104 cells mL1 are absent at depths below 400 masl. The average numbers of TNC over year were similar except for 1998 and 2010 when most samples coincide with the depth range of 300 to 500 m depths where several of the TNC values reaches the highest deep groundwater values observed (Figure 3-4). In ONKALO, the TNC average values distribute evenly over year (Figure 3-5).
16
1998 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
-1200
-1000
-800
-600
-400
-200
0D
ep
th (
ma
sl)
Figure 3-1. The distribution of deep groundwater sample depths over sampling years in Olkiluoto. Squares represent yearly average and triangles represent discrete depths. Bars show standard deviation.
2005 2006 2007 2008 2010 2011 2012 2013
Year
-500
-450
-400
-350
-300
-250
-200
-150
-100
-50
0
De
pth
(m
asl
)
Figure 3-2. The distribution of sample depths over sampling years in ONKALO. Bars show standard deviation. Squares represent yearly average and triangles represent discrete depths.
3.1.2 ATP
Figure 3-6 shows all ATP values obtained from Olkiluoto and ONKALO from 2004 to 2013. The amount of ATP, and thereby the amount of biomass, peaks at two depths, in shallow groundwater and in the depth range between 300 to –450 masl. The depth profile of ATP data consequently corroborates the depth profile of TNC. There was no large variation in average ATP data over year for Olkiluoto or ONKALO (Figure 3-7 and Figure 3-8). The correlation between TNC and ATP values is strong as shown in Figure 3-9. Previously, the average amount of ATP per cell in Fennoscandian deep groundwater was calculated to 0.43 (Eydal and Pedersen 2007) and the average for all data below 25 masl in Figure 3-10 is somewhat higher, 0.67. The five outliers to the right in Figure 3-10 (>1) are randomly distributed over drillholes and sampling years. Two values, from OL-KR39 and OL-KR56, were unrealistically high, possibly due to problems with the analyses. OL-KR56 was from a large depth (-1116 masl) with high salinity that can disturb the TNC analysis. For OL-KR39 the reason for the low TNC value is less clear. The CHAB number was more than 3 times larger than the TNC. Taking ATP over CHAB returns 1.25 which is a realistic value for a metabolically active microbial population. These two values were, due to the described uncertainties, not added to Figure 3-10.
17
Table 3-1. Cell numbers and biomass (ATP) determinations for deep groundwater from Olkiluoto (OL-) and ONKALO (ONK-) sampled 20102013. TNC = total number of cells, CHAB = cultivable heterotrophic aerobic bacteria. MPN = sum of all most probable number of cells values. Statistical details can be found Table A-1 and Table A-4.
Drillhole Sampled (Y-M-D)
Depth (masl)
TNC (cells mL1)
ATP (amol mL1)
CHAB (cells mL1)
ATP/ TNC
CHAB/ TNC (%)
MPN/TNC (%)
OL-KR30 2011-03-02 -41.8 120000 38700 14600 0.32 12.17 2.62OL-KR57 2013-06-25 -42.7 110000 3900 60 0.04 0.06 0.01OL-KR53 2011-09-27 -46.5 75000 3700 227 0.05 0.30 0.19OL-KR6 2011-02-08 -74.3 49000 25500 14000 0.52 28.57 5.51OL-KR44 2010-08-10 -94.8 330000 79900 176000 0.24 53.33 15.16OL-KR6 2010-11-23 -97.0 87000 25600 8350 0.29 9.60 4.48OL-KR6 2010-09-28 -102.3 61000 7900 3000 0.13 4.92 0.92OL-KR53 2011-08-10 -113.2 43000 15900 380 0.37 0.88 0.19OL-KR46 2012-11-06 -190.9 59000 14600 5220 0.24 8.84 1.64OL-KR53 2011-05-30 -198.0 72000 20500 10900 0.28 15.14 7.05OL-KR55 2013-07-02 -233.3 32000 22300 3170 0.70 9.91 25.70OL-KR51 2010-12-20 -237.6 27000 10800 8170 0.40 30.26 30.20OL-KR54 2013-04-16 -328.5 85000 3800 790 0.05 0.93 1.30OL-KR6 2013-08-22 -329.6 30000 2400 60 0.08 0.20 0.11OL-KR51 2010-10-26 -344.3 83000 26300 24700 0.32 29.76 6.24OL-KR50 2010-11-18 -350.1 67000 6420 4530 0.10 6.76 11.98OL-KR11 2010-08-17 -373.9 510000 220000 122000 0.43 23.92 32.42OL-KR39 2010-06-16 -376.8 250000 1050000 840000 4.20 336.00 65.60OL-KR50 2010-09-28 -408.4 88000 56400 81300 0.64 92.39 34.25OL-KR46 2013-03-27 -456.7 370000 31800 2460 0.09 0.66 0.65OL-KR46 2013-11-06 -530.0 340000 11600 40 0.03 0.01 7.15OL-KR44 2010-06-30 -538.5 130000 37600 1900 0.29 1.46 10.24OL-KR55 2012-08-28 -674.8 110000 21500 19100 0.20 17.36 7.35OL-KR55 2013-05-20 -674.8 140000 7200 2960 0.05 2.11 0.11OL-KR40 2011-03-09 -708.3 73000 11500 3330 0.16 4.56 1.88OL-KR50 2010-03-02 -721.0 40000 57100 8070 1.43 20.18 225.00OL-KR56 2012-08-15 -1116 31000 35400 143 1.14 0.46 0.55OL-KR56 2013-06-11 -1116 10000 48900 440 4.89 4.40 0.01ONK-PVA1 2012-04-16 -14.6 25000 5300 <1000 0.21 0 0.69ONK-PVA3 2012-04-16 -78.5 29000 14500 140 0.50 0.48 0.53ONK-PVA5 2012-04-17 -228.7 20000 800 <1000 0.04 0 0.16ONK-PVA8 2010-09-23 -276.4 16000 5300 187 0.33 1.17 31.30ONK-PVA8 2012-04-18 -276.4 38000 10800 2300 0.28 6.05 0.14ONK-PVA8 2013-03-19 -276.4 610000 170000 930 0.28 0.15 0.46ONK-PVA6 2010-09-23 -327.0 37000 27300 16300 0.74 44.05 245.45ONK-PVA6 2012-04-17 -327.0 49000 10600 70 0.22 0.14 2.15ONK-PVA10 2012-04-18 -366.5 3200 1800 <1000 0.56 0 0.11ONK-KR15 2012-01-11 -399.0 18000 4500 2500 0.25 13.89 4.49ONK-PVA9 2011-09-20 -417.5 21000 10600 6700 0.50 31.90 5.24ONK-PVA9 2013-12-10 -417.5 270000 25400 59700 0.09 22.11 6.30
18
3.1.3 Cultivable heterotrophic aerobic bacteria
The numbers of CHAB varied 4 orders of magnitude depending on sample site but did not show a significant correlation with depth (Figure 3-11). There was an even distribution over year except for 2010 in Olkiluoto that returned a higher average CHAB number than did the other years in Olkiluoto and ONKALO (Figure 3-11 and Figure 3-12). This observation correlates well with TNC and ATP data that also showed higher average value 2010. There was a wide distribution of the percentage of the TNC that was cultivated with CHAB (Figure 3-14, Table 3-1) from less than a fraction of a percent to 90 %. The trend of TNC cultivable with CHAB was decreasing with depth (Figure 3-14).
3.1.4 Most probable number cultivation and TNC
There was a wide distribution of the percentage of the TNC that could be cultivated with MPN (Table 3-1) from less than a fraction of a percent to 65 % (Figure 3-15). In a few cases, the percentage was larger than 100% (Table 3-1) which can have several different explanations. Obviously, the TNC may have been underestimated due to problems with staining and visibility in the microscope. The data for MPN has large standard deviations and some MPN analyses may have overestimated the actual MPN of the cultivated samples.
3.1.5 Most probable number of nitrate-reducing bacteria
The MPNs of NRB show a generally increasing trend with depth in Olkiluoto groundwater, similar to what was observed for CHAB (Figure 3-11). Indeed, NRB data correlated with CHAB data, except for some shallow groundwater samples that had much lower NRB values than the corresponding CHAB values (Figure 3-16). The microorganisms in these samples were obviously strict aerobes, unlike most NRB, which are facultative anaerobes. Facultative anaerobes respire using oxygen, if present; otherwise, they can switch to using nitrogen in nitrate as the electron acceptor in their respiration. The MPN of NRB is determined in an oxygen-free environment in anaerobic tubes, while CHAB cultivation is performed in air that contains oxygen. The obtained correlation indicates that, if oxygen intrudes at depth into Olkiluoto groundwater, microorganisms able to respire and thereby remove oxygen are present at all depths. In other words, many of the bacteria cultivable using the MPN method for NRB can also be cultivated using the CHAB method, which means that the same microorganisms are possibly being counted twice. The MPN of NRB over depth displayed a range over five orders of magnitude in the groundwater samples (Figure 3-17). There was an even distribution over sampling years of NRB at 1000 cells mL1 except for 2010 in Olkiluoto groundwater samples that returned a higher average NRB number than did the other years (Figure 3-18). The MPN of NRB in ONKALO fluctuates over sample year (Figure 3-19) and that may partly be due to that very different depth were studied each year (Figure 3-2). Deeper samples shows generally more MPN of NRB in ONKALO than shallow samples, very much in agreement with the trend of increasing numbers of NRB over depth in Olkiluoto groundwater (Figure 3-17).
3.1.6 Most probable number of iron- and manganese-reducing bacteria
The MPN numbers of IRB and MRB do not correlate (not shown), suggesting that these two MPN analyses are detecting complementary, rather than similar, microorganisms able to use solid metal oxides as electron acceptors.
19
The MPN of IRB over depth displayed a range over three orders of magnitude in the groundwater samples (Figure 3-20). The general trend for the MPN of IRB was increasing during the first 100 m and the highest numbers were found in the depth interval between 100 and 400 masl. Thereafter, the MPN of IRB decreased with depth and the few available observations below 600 m were below or just above the detection limit of 0.2 cells cells mL1. IRB was low in many shallow groundwater samples with a few values above 10 cells mL1. The deeper groundwater samples displayed a peak relative to the other MPN values of IRB, with several IRB values significantly above 100 cells mL-1 at the depth interval between 100 and 400 masl. There was an even distribution of the average MPN of IRB over year in both Olkiluoto and ONKALO groundwater samples, but the averages were lower for ONKALO compared with Olkiluoto samples.
The MPN of MRB over depth displayed a range over three orders of magnitude in the groundwater samples (Figure 3-23). The general trend was distribution over this range down to approximately 600 masl where the numbers were below or just above the limit of detection (0.2 cells mL1). The average MPN over year ranged between 10 and 100 cells mL1 in Olkiluoto groundwater samples (Figure 3-24). The yearly averages for ONKALO ranged from the limit of detection up to approximately 100 cells mL1 (Figure 3-25).
3.1.7 Most probable number of sulphate-reducing bacteria
The MPN of SRB were scattered over three orders of magnitude in shallow groundwater up to approximately 1000 cells mL1 and they were below 100 cells mL1 between 100 and 300 m depth (Figure 3-26). From 300 m down to approximately 400 m depth there were 13 observations above 100 cells mL1. Deeper, the MPN of SRB decreased and approached the limit of detection (0.2 cells mL1). The average MPN over year did not change much except for the series of samples collected 1997–1999 that had an average MPN of SRB that was much larger than that of any other of the following years (Figure 3-27). This agrees with the averages of TNC and IRB that also was highest in the 1997–1999 time period (Figure 3-4 and Figure 3-21). With two exception all MPN of SRB in ONKALO groundwater samples were at or below 10 cells mL1 (Figure 3-28). Data from Äspö Underground Rock Laboratory (URL) have shown that the numbers of SRB decrease during prolonged flushing of tunnel drillholes (Pedersen 2013b). Most of the sampled drillholes in ONKALO had been standing open and flowing before sampling and this possibly caused an artificial decrease in the MPN of SRB. Therefore, TNC was determined both at start and end of the filtration of ONK-PVA6, ONK-PVA10 and ONK-KR15 groundwater 2013-04-17 (Table 3-2). The TNC numbers became significantly smaller at the end of the filtration suggesting that prolonged flushing of a drillhole aquifer will decrease the TNC and thereby also cause a decrease in all other cultivable cell numbers.
Table 3-2. TNC recovery at start and end of a prolonged flushing situation.
Drillhole TNC at start (cells mL‐1 ± SD)
TNC at end (cells mL‐1 ± SD)
Volume groundwater flushed
(L)
ONK-PVA6 1.49 × 105 ± 9.5 × 103 1.40 ×104 ± 2.5 × 103 7.5 ONK-PVA10 7.20 × 103 ± 1.4 × 103 1.30 ×103 ± 1.4 × 102 50 ONK-KR15 5.20 × 103 ± 4.7 × 102 2.00 ×103 ± 5.9 × 102 107
20
3.1.8 Most probable number of acetogens
The MPN results for AA and HA displayed similar patterns. The data were scattered over a range of four orders of magnitude (1 up to 1000 cells mL1). At a depth of approximately 300 m, there was a systematic tendency to higher AA and HA MPN values than the over-all average as was also observed for NRB and SRB. There was a decrease in the numbers of AA in Olkiluoto and ONKALO groundwater 2011 and 2012, while HA did not decrease in Olkiluoto these years (HA was not analysed after 2011 in Olkiluoto groundwater). The reasons are not clear; all cultivations of AA and HA have positive control cultures of Acetobacterium carbonolicum and these controls have grown well which excludes problems with the cultivation media.
3.1.9 Most probable number of methanogens
There have been few cultivable AM and HM in shallow groundwater and very few detectable methanogens at depth. With exception for AM in ONKALO 2007 and 2008, methanogens have not been detected in ONKALO. The absence may be due to methodological problems, some environmental methanogens are known to be very difficult to cultivate, or, there may have been very few methanogens in the sampled groundwater. These microorganisms may be sensitive to flow similarly to what has been found for sulphate reducing bacteria in the Äspö URL (Pedersen 2013b). The issue of AM and HM in the sampled groundwater will be further discussed in relation to the analysis of genomic DNA samples (3.3).
3.1.10 Diversity of cultivable microorganisms in Olkiluoto and ONKALO groundwater
The logarithms with the base 10 were calculated for the MPN values for each metabolic group and stacked in bar graphs. The obtained stacked number then represent both the diversity, i.e. how many metabolic groups could be cultivated, and the numbers of cultivable microorganisms within each metabolic group in a groundwater sample. A large stack will be obtained if both the diversity and the values of cultivated metabolic groups were large. A value for each cultivated group can be appreciated from the bar length for the respective metabolic group. The stacked profile of MPN values for all deep groundwater samples analysed from 2005 to 2012 is shown in Figure 3-41. The NRB analysis was introduced into the sampling programme during 2005, so data for drillholes OL-KR2, OL-KR7, OL-KR10, OL-KR19, and OL-KR27 have been excluded, they can be found elsewhere (Pedersen et al. 2010). In addition, IRB and MRB were not analysed for OL-KR13 in 2007 and this stack is therefore excluded as well from Figure 3-41. The stack height remained rather homogenous over depth for the first 0 to 200 masl, deeper, the frequency of samples with a large stack height increases.
21
2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0
10Log(TNC) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 20122013ONKALO
Figure 3-3. The distribution of total number of cells (TNC) versus depth in Olkiluoto and ONKALO groundwater. Data were obtained using the acridine orange direct count method. ONKALO data is from 2005 to 2013.
1998 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
10L
og
(TN
C)
(ce
lls m
L-1
)
Figure 3-4. The distribution of total number of cells (TNC) over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012 2013
Year
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
10L
og
(TN
C)
(ce
lls m
L-1
)
Figure 3-5. The distribution of total number of cells (TNC) over sampling years in ONKALO.
22
2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0
10Log(ATP) (amol mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
2004 - 20072008200920102011 - 20122013ONKALO
Figure 3-6. ATP concentration distributed over depth in Olkiluoto and ONKLAO groundwater. ONKALO data is from 2005 to 2013.
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
10L
og
(AT
P)
(am
ol m
L-1
)
Figure 3-7. The distribution of ATP over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012 2013
Year
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
10L
og
(AT
P)
(am
ol m
L-1
)
Figure 3-8. The distribution of ATP over sampling years in ONKALO.
23
2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5
10Log(TNC) (cells mL-1)
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
10L
og
(AT
P)
(am
ol m
L-1
)
Figure 3-9. The relationship between total numbers of cells (TNC) in shallow and deep Olkiluoto and ONKALO groundwater and ATP concentrations. The least squares regression line for TNC versus ATP is shown (10Log(ATP) = 0.80 10Log(TNC) + 0.53; r = 0.75, p = 0.00001, n = 123).
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
ATP/TNC (amol/cells)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
Figure 3-10. The amount of ATP (Figure 3-6) per cell (Figure 3-3) in shallow and deep Olkiluoto and ONKALO groundwater samples.
24
0 1 2 3 4 5 6
10Log(CHAB) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
2004 - 20072008200920102011 - 20122013ONKALO
Figure 3-11. The distribution of cultivable heterotrophic aerobic cells (CHAB) versus depth in Olkiluoto and ONKALO groundwater. ONKALO data is from 2005 to 2013.
2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(CH
AB
) (c
ells
mL
-1)
Figure 3-12. The distribution of CHAB over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(CH
AB
) (c
ells
mL
-1)
Figure 3-13. The distribution of CHAB over sampling years in ONKALO.
25
0 10 20 30 40 50 60 70 80 90 100
CHAB/TNC (%)
-1200
-1000
-800
-600
-400
-200
0
Dep
th (
mas
l)
Figure 3-14. The percentage of TNC that could be cultivated with CHAB.
0 10 20 30 40 50 60 70 80
Σ MPN/TNC (%)
-1200
-1000
-800
-600
-400
-200
0
Dep
th (
mas
l)
Shallow OlkiluotoDeep OlkiluotoONKALO
Figure 3-15. The percentage of TNC that could be cultivated with MPN analyses.
0 1 2 3 4 5 6
10Log(CHAB) (cells mL-1)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(NR
B)
(ce
lls m
L-1
)
Figure 3-16. The relationship between NRB and CHAB data from shallow Olkiluoto groundwater (red squares) and deep groundwater samples from Olkiluoto and ONKALO (black circles). NRB = 0.85 × CHAB + 0.15; r = 0.80, p = 0.00001.
26
0 1 2 3 4 5 6
10Log(NRB) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
2004 - 20072008200920102011 - 20122013ONKALO
Figure 3-17. The distribution of MPNs of nitrate-reducing bacteria (NRB) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2013.
2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(NR
B)
(ce
lls m
L-1
)
Figure 3-18. The distribution of NRB over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(NR
B)
(ce
lls m
L-1
)
Figure 3-19. The distribution of NRB over sampling years in ONKALO.
27
0 1 2 3 4 5 6
10Log(IRB) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 20122013ONKALO
Figure 3-20. The distribution of MPNs of iron-reducing bacteria (IRB) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2013.
1998 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(IR
B)
(ce
lls m
L-1
)
Figure 3-21. The distribution of IRB over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(IR
B)
(ce
lls m
L-1
)
Figure 3-22. The distribution of IRB over sampling years in ONKALO.
28
0 1 2 3 4 5 6
10Log(MRB) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
2004 - 20072008200920102011 - 2012ONKALO
Figure 3-23. The distribution of MPNs of manganese-reducing bacteria (MRB) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2012.
2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(MR
B)
(ce
lls m
L-1
)
Figure 3-24. The distribution of MRB over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(MR
B)
(ce
lls m
L-1
)
Figure 3-25. The distribution of MRB over sampling years in ONKALO.
29
0 1 2 3 4 5 6
10Log(SRB) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 20122013ONKALO
Figure 3-26. The distribution of MPNs of sulphate-reducing bacteria (SRB) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2013.
1998 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(SR
B)
(ce
lls m
L-1
)
Figure 3-27. The distribution of SRB over sampling years in Olkiluoto. The data for 1997–1999 has been pooled under 1998.
2005 2006 2007 2008 2010 2011 2012 2013
Year
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
10L
og
(SR
B)
(ce
lls m
L-1
)
Figure 3-28. The distribution of SRB over sampling years in ONKALO.
30
0 1 2 3 4 5 6
10Log(AA) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 20122013ONKALO
Figure 3-29. The distribution of MPNs autotrophic acetogens (AA) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2013.
1998 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(AA
) (c
ells
mL
-1)
Figure 3-30. The distribution of AA over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012 2013
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(AA
) (c
ells
mL
-1)
Figure 3-31. The distribution of AA over sampling years in ONKALO.
31
0 1 2 3 4 5 6
10Log(HA) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 2012ONKALO
Figure 3-32. The distribution of MPNs of heterotrophic acetogens (HA) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2012.
1998 2004 2005 2006 2007 2008 2009 2010 2011
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(HA
) (c
ells
mL
-1)
Figure 3-33. The distribution of HA over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012
Year
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
10L
og
(HA
) (c
ells
mL
-1)
Figure 3-34. The distribution of HA over sampling years in ONKALO.
32
0 1 2 3 4 5 6
10Log(AM) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 2012ONKALO
Figure 3-35. The distribution of MPNs of autotrophic methanogens (AM) versus depth in Olkiluoto groundwater. ONKALO data is from 2005 to 2012.
1998 2004 2005 2006 2007 2008 2009 2010 2011
Year
-0.5
0.0
0.5
1.0
1.5
10L
og
(AM
) (c
ells
mL
-1)
Figure 3-36. The distribution of AM over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012
Year
-0.10
-0.05
0.00
0.05
0.10
10L
og
(AM
) (c
ells
mL
-1)
Figure 3-37. The distribution of AM over sampling years in ONKALO.
33
0 1 2 3 4 5 6
10Log(HM) (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0
De
pth
(m
asl
)
1997 - 19992004 - 20072008200920102011 - 2012ONKALO
Figure 3-38. The distribution of MPNs of heterotrophic methanogens (HM) versus depth in Olkiluoto groundwate. ONKALO data is from 2005 to 2012.
1998 2004 2005 2006 2007 2008 2009 2010 2011
Year
-0.5
0.0
0.5
1.0
1.5
10L
og
(HM
) (c
ells
mL
-1)
Figure 3-39. The distribution of HM over sampling years in Olkiluoto.
2005 2006 2007 2008 2010 2011 2012
Year
-0.10
-0.05
0.00
0.05
0.10
10L
og
(HM
) (c
ells
mL
-1)
Figure 3-40. The distribution of HM over sampling years in ONKALO.
34
HM AM HA AA SRB MRB IRB NRB
OL-
KR
32_5
0_1
OL-
KR
30_5
0_2
OL-
KR
30_5
0.6
OL-
KR
53_6
5.5
OL-
KR
8_77
_1
OL-
KR
47_7
7_1
OL-
KR
43_9
6_1
OL-
KR
33_9
5_1
OL-
KR
46_8
2_1
OL-
KR
6_98
_8
OL-
KR
6_98
.5
OL-
KR
39_1
08_1
OL-
KR
6_12
5_6
OL-
KR
44_1
16
OL-
KR
45_1
17_1
OL-
KR
6_12
5
OL-
KR
6_13
5_8
OL-
KR
6_13
5
OL-
KR
47_1
31_1
OL-
KR
37_1
66_1
OL-
KR
47_1
45_1
OL-
KR
46_1
31_1
OL-
KR
31_1
43_1
OL-
KR
42_1
75_1
OL-
KR
46_1
75_1
OL-
KR
43_2
14_1
OL-
KR
47_2
17_1
OL-
KR
41_2
13_1
OL-
KR
53_2
65
OL-
KR
41_2
57_2
OL-
KR
51_2
92
OL-
KR
45_2
95_1
OL-
KR
40_2
82_1
OL-
KR
8_30
2_2
OL-
KR
13_3
62_3
OL-
KR
6_39
3_1
OL-
KR
10_3
26_2
OL-
KR
6_42
2_5
OL-
KR
47_4
13_1
OL-
KR
51_4
22
OL-
KR
39_4
03_1
OL-
KR
39_4
00
OL-
KR
50_3
63
OL-
KR
11_4
15
OL-
KR
50_4
24
OL-
KR
49_6
14_1
OL-
KR
44_6
51
OL-
KR
47_7
08_1
OL-
KR
40_7
86_1
OL-
KR
40_7
88.5
OL-
KR
50_7
51
Drillhole
Sta
cked
10lo
g(M
PN
) va
lues
HM
AM
HA
AA
SRB
MRB
IRB
NRB
PVA1
PVA1
PVA1
PVA1
ONK‐PVA 2
ONK‐PVA 3
ONK‐PVA8
ONK‐PVA6
ONK‐KR15
ONK‐PVA9
Sta
cke
d 10
log
(MP
N)
valu
es
Figure 3-41. Stacked 10Log(MPN) values of most probable numbers of physiological groups of microorganisms analysed for in deep Olkiluoto groundwater (top figure) and ONKALO groundwater (bottom figure), 2005–2012. Only complete analyses with 8 MPN results are shown. The drillholes are listed from left to right in order of increasing depth. NRB = nitrate-reducing bacteria, IRB = iron-reducing bacteria, MRB = manganese-reducing bacteria, SRB = sulphate-reducing bacteria, AA = autotrophic acetogens, HA = heterotrophic acetogens, AM = autotrophic methanogens, and HM = heterotrophic methanogens. The height of each stack represents both the diversity i.e., how many metabolic groups could be cultivated and values of cultivable organisms in each metabolic group in a drillhole. The values for each cultivated group can be gauged from the bar length for each metabolic group. Each scale step indicated by a tick represents a 10log10 unit = 1; two steps thus equal 100 cells mL–1 and three steps 1000 cells mL–1.
35
3.2 Relationships between microbiology and hydrogeochemistry data
Previous pair-wise comparison of hydrogeochemistry and microbiology data revealed no significant correlation (Pedersen et al. 2012). The activity of SRB and the concomitant sulphide production is a great safety concern, because sulphide is corrosive to copper. As pointed out previously (Pedersen 2008) the processes of sulphide production and precipitation depend on several controlling parameters, such as the availability of energy sources, carbon sources, sulphate, and ferrous iron, the presence of viable SRB and bacteriophages, and the redox potential. From this perspective, it was found inadequate to compare data case-wise (Pedersen et al. 2012). However, a correlation was found when the combined concentrations of methane and sulphate where pair-wise compared with the numbers of cultivable microorganisms. This comparison has been updated with new data from 2010 2013 (Figure 3-42). A distance-weighted least squares model confirmed that more microorganisms could be cultivated from groundwater with significant concentrations of methane and sulphate (Figure 3-43).
-3.0-2.5
-2.0-1.5
-1.0-0.5
0.00.5
1.01.5
2.0
10 Log(CH 4) (mM)
01
23
45
67
8
SO4 (mM)
0.5
1.0
1.5
2.0
2.5
3.0
3.5
10Log(MP
N) (cells m
L -1)
Figure 3-42. The relationship between MPN numbers in the dataset, and the concentrations of sulphate and methane expressed as a pair-wise correlation graph. The number of data triplets (CH4, SO4 and MPN) was 528.
36
> 3 < 2.7 < 2.2 < 1.7 < 1.2 < 0.7 < 0.2
-3.0-2.5
-2.0-1.5
-1.0-0.5
0.00.5
1.01.5
2.0
10 Log(CH4) (mM)
01
23
45
67
8
SO4 (mM)
0.5
1.0
1.5
2.0
2.5
3.0
3.5
10Log(MP
N) (cells m
L -1)
Figure 3-43. The relationship between MPN numbers in a dataset consisting of 528 data triplets (CH4, SO4 and MPN), and the concentrations of sulphate and methane in Figure 3-42 represented in a three-dimensional graph according to a distance-weighted least squares model.
3.3 Bacterial and archaeal genomic diversity in ONKALO groundwater
New methods for the analysis of genomic DNA in microbial groundwater populations were introduced during 2013. Because the amount of DNA can be very small in deep groundwater, and because all DNA extraction methods have losses, we did filter groundwater to increase the amount of cells for extraction. In addition to obtain more DNA, filtering of a large volume of groundwater, > 1L, will reduce possible variations in cell numbers and diversity over volume compared to smaller volumes, < 1 L. The less DNA recovered from a sample, the larger is the risk that reagent and laboratory contamination impact the sequence results (Salter et al. 2014). Therefore, procedures should be adopted that ensure as large DNA samples as possible. In the case of groundwater sampling, filtering of large groundwater volumes will reduce the risk for bias of the results due to contamination by trace DNA in reagents and laboratory materials. In this work, we filtered large volumes of groundwater before applying our DNA extraction protocols. The method was based on the filtering of volumes of groundwater, 50–150 L, under in situ pressure, extraction of captured genomic DNA and subsequent sequencing of the diversity indicator gene 16S rDNA with high throughput techniques, sometimes referred to as next generation sequencing (NGS). The strategy to filter groundwater under in situ pressure was successful in that the total amounts of extracted DNA was large and ranged from 18 to 1502 × 10–9 g (Table 3-3). The quality of the DNA was good and it amplified very well also in small quantities. This range of recovered DNA was found sufficient in the work by Salter et al. (2014) to avoid laboratory contamination with a MoBIO kit.
37
3.3.1 Groundwater bacterial 16S rDNA v6v4 sequence diversity
The 454 sequencing method generated bacterial sequences with read numbers between 400 to 500 bases. The total abundance i.e. >0% representation of unique OTU in the DNA libraries ranged from 112 to 220 (Table 3-3). The largest diversity numbers were obtained from the two shallow samples ONK-PVA1 and ONK-PVA3. Diversity indexes such as abundance-based coverage estimator (ACE) and unbiased richness estimate (CHAO) both indicated similar diversities. When OTUs in ≥1 % abundance in the sequence libraries were calculated, from 7 to 18 OTUs were identified. The distribution of these OTUs over the drillhole sample DNA libraries is shown in Figure 3-44. The OTU data could be grouped according to a tree based on similarities between the sample libraries. The tree shows that the DNA libraries of ONK-PVA1 and ONK-PVA3 were closely related. In other words, the bacterial populations in groundwater of the respective aquifer(s) intersected by the drillholes were closely related and probably of similar origin. Two of the drillholes, ONK-PVA6 and ONK-KR15, were sampled twice within approximately a 5 months interval. The respective DNA libraries were more related to each other than to any other of the sample libraries (Figure 3-44). This result suggests that the bacterial populations in the aquifers intersected by each of these drillhole were fairly constant over time.
Many of the OTUs detected in ≥1 % abundance were found in only one of the drillhole groundwater DNA sample libraries (Figure 3-45). Other OTUs such as those related to Acholeplasma, Hoeflea, Hydrogenophaga, Nitrospiraceae, Pseudomonas, Sphingobacteriales and Thiobacillus occurred in four or more of the drillhole groundwater DNA sample libraries.
3.3.2 Groundwater archaeal 16S rDNA v6 sequence diversity
The Illumina sequencing method generated archaeal sequences with read numbers between 50 - 60 bases. The total abundance i.e. >0% representation of unique OTU in the DNA libraries ranged from 87 to 101 (Table 3-4). The total diversity did not differ markedly between the DNA libraries. Diversity indexes such as abundance-based coverage estimator (ACE) and unbiased richness estimate (CHAO) both indicated similar diversities over all sample libraries, except for ONK-PVA6_1 that had a markedly larger DNA library diversity than the other libraries. When OTUs in ≥1 % abundance in the sequence libraries were calculated, from 7 to 13 OTUs were identified. The distribution of these OTUs over the drillhole sample DNA libraries is shown in Figure 3-46. The OTU data could be grouped according to a tree based on similarities between the sample libraries. The tree shows that the archaeal DNA libraries grouped differently compared to the Bacterial DNA libraries. Two of the drillholes, ONK-PVA6 and ONK-KR15, were sampled twice within approximately a 5 months interval. The DNA libraries from ONK-KR15 were closely related to each other while ONK-PVA6 grouped differently.
Many of the OTUs detected in ≥1 % abundance were found in only one of the drillhole groundwater samples (Figure 3-47). Other OTUs such as those related to Chrenarcaeota Halobacteriales, Methanosarcinales and Thermoplasmatales occurred in four or more of the drillhole DNA libraries.
38
3.3.3 Distribution of sulphate-reducing bacteria over depth
The distribution of 10Log numbers of SRB over depth as shown in Figure 3-26 indicates that there was a maximum in cultivable SRB from 300 m down to approximately 400 masl with 13 observations above 100 cells mL1. If the absolute numbers of cultivable SRB is plotted versus depth, this maximum appears more obvious than in the plot of log SRB numbers (Figure 3-48). The MPN cultivation method returns a value of cultivable SRB in the groundwater samples. However, there may be other groups of SRB present that escape the cultivation methodology. The analysis of genomic DNA is complementary to cultivation and does return a more complete diversity estimate than does MPN cultivation, but the method is not quantitative although relative abundance of different groups of microorganisms is indicated. When the percentage of SRB related OTUs in the sample libraries are plotted versus depth, a profile similar to the one obtained with MPN cultivation is found (Figure 3-49). Two independent lines of evidence consequently report a maximum in SRB abundance between 300 and 400 masl depth in Olkiluoto. There were 9 different OTUs in the sequence libraries related to sulphide-producing bacteria of which 6 are related to SRB (Table 3-5). The dominating OTUs were related to Desulfobulbaceae and to Desulfobacula.
39
Table 3-3. Amounts of extracted double-stranded DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated diversity at total Bacteria OTU level (>0% sequence abundance) in groundwater and biofilm sequence libraries.
Sample Sample
date Amount of extracted
DNA (g × 109)
Sampling depth, i.e., number of sequences
Number of OTU at
>0% abundance
Number of OTU at ≥1%
abundance
ACE1 CHAO2
Shannon-Weaver diversity
Index
ONK-PVA1 2012-04-16 1502 13403 210 7 248 301 1.93
ONK-PVA3 2012-04-16 84 20704 220 13 250 291 2.17
ONK-PVA5 2012-04-17 12 19228 112 16 124 159 2.17
ONK-PVA6_1 2012-04-17 243 12795 117 12 134 159 1.86
ONK-PVA06_2 2012-09-04 28 12818 150 14 168 210 2.32
ONK-PVA10 2012-04-18 64 16232 133 14 159 247 1.98
ONK-KR15_1 2012-04-16 63 18134 136 14 158 214 2.04
ONK-KR15_2 2012-09-04 18 17405 146 18 167 214 2.19 1 Abundance-based coverage estimator; 2 Unbiased richness estimate
40
Table 3-4. Amounts of extracted double-stranded DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated diversity at total Archaea OTU level (>0% sequence abundance) in groundwater and biofilm sequence libraries.
Sample Sample date Amount of
extracted DNA
(g × 109)
Sampling depth, i.e., number of sequences
Number of OTU at
>0% abundance
Number of OTU at ≥1%
abundance
ACE1 CHAO2 Shannon-Weaver diversity
Index
ONK-PVA1 2012-04-16 1502 647388 101 11 104 118 1.82
ONK-PVA3 2012-04-16 84 681345 100 15 104 104 1.9
ONK-PVA5 2012-04-17 12 477839 87 10 91 95 1.6
ONK-PVA6_1 2012-04-17 243 403651 101 13 106 157 2.03
ONK-PVA06_2 2012-09-04 28 217372 89 13 93 97 1.89
ONK-PVA10 2012-04-18 64 110867 92 16 97 106 2.06
ONK-KR15_1 2012-04-16 63 6651830 101 5 102 103 1.27
ONK-KR15_2 2012-09-04 18 1159183 100 7 103 112 1.45 1 Abundance-based coverage estimator; 2 Unbiased richness estimate
41
Acholeplasma
Acidovorax
Acinetobacter
Alishewanella
Anaerovorax
Aquabacterium
Bacteria OD1
Bacteria OP11
Bacteria OP3
Bacteroidetes
Brevundimonas
Chlorobiales
Coriobacteriaceae
Cyanobacteria
Deferribacterales
Dehalogenimonas
Deltaproteobacteria
Desulfobacterium
Desulfobacula
Desulfobulbus
Desulfosporosinus
Desulfobulbaceae
Desulfuromonadales
Desulfuromonas
Dethiosulfatibacter
Erysipelothrix
Ferribacterium
Fusibacter
Gallionellaceae
Hoeflea
Hydrogenophaga
Lachnospiraceae
Lutibacter
Methylomonas
Methylophilus
NA-1
NA-2
Nitrospiraceae
Pseudidiomarina
Pseudomonas
Rhodocyclaceae
Rhodoferax
Roseovarius
Simplicispira
Sphingobacteriales
Sphingopyxis
Syntrophaceae
Syntrophus
Thermoanaerobacterales
Thermoplasmata
Thiobacillus
< 1%ONK‐KR15_1
ONK‐KR15_2
ONK‐PVA10
ONK‐PVA6_1
ONKPVA06_2
ONK‐PVA5
ONK‐PVA3
ONK‐PVA1
0
20
40
60
80
100
Fre
quen
cy (
%)
Figure 3-44. Composition of v4v6 pyrotag sequencing Bacteria libraries for samples of ONKALO groundwater samples. OTUs with ≥1% abundance frequency are shown. Details can be found in Table A-7. NA: not annotated. The tree on top of the bar graph shows a Morisita–Horn distance measure illustrated using an unweighted pair group method with arithmetic mean (UPGMA) for the tree construction with taxonomic depth at species level for all observed sequences.
42
ONK-PVA1 ONK-PVA3 ONK-PVA5 ONK-PVA6_1 ONK-PVA6_2 ONK-PVA10 ONK-KR15_1 ONK-KR15_2
Acholeplasma
Acidovorax
Acinetobacter
Alishewanella
Anaerovorax
Aquabacterium
Bacteria OD1
Bacteria OP11
Bacteria OP3
Bacteroidetes
Brevundimonas
Chlorobiales
Coriobacteriaceae
Cyanobacteria
Deferribacterales
Dehalogenimonas
Deltaproteobacteria
Desulfobacterium
Desulfobacula
Desulfobulbus
Desulfosporosinus
Desulfobulbaceae
Desulfuromonadales
Desulfuromonas
Dethiosulfatibacter
Erysipelothrix
Ferribacterium
Fusibacter
Gallionellaceae
Hoeflea
Hydrogenophaga
Lachnospiraceae
Lutibacter
Methylomonas
Methylophilus
NA‐1
NA‐2
Nitrospiraceae
Pseudidiomarina
Pseudomonas
Rhodocyclaceae
Rhodoferax
Roseovarius
Simplicispira
Sphingobacteriales
Sphingopyxis
Syntrophaceae
Syntrophus
Thermoanaerobacterales
Thermoplasmata
Thiobacillus
< 1%
Figure 3-45. Distribution of Bacteria OTUs obtained with v4v6 pyrotag sequencing over ONKALO groundwater drillhole samples. OTUs with ≥1% abundance frequency are shown. NA: not annotated. The values for each sequenced OTU can be gauged from the bar length for each borehole. Each scale step indicated by a tick represents 5% of the total numbers of OTU; two steps thus equal 10 % and three steps 15%.
43
Chrenarchaeota Chrenarchaeota G-B Chrenarchaeota G-C3 Chrenarchaeotic G Halobacteriales-1 Halobacteriales-2 Halobacteriales-3 Methanobacteriaceae Methanocella Methanolobus Methanomicrobia Methanosarcinales Methanoscarcinales GOM Methermicoccus Thermoplasma Thermoplasmata CCA47 Thermoplasmata MBG Thermoplasmata SAG Thermoplasmatales Thermoplasmatales AMOS1A Thermoplasmatales ASC21 Thermoplasmatales T. Unknown < 1%
ONK‐KR15
ONK‐KR15_2
ONK‐PVA06_2
ONK‐PVA3
ONK‐PVA10
ONK‐PVA6
ONK‐PVA1
ONK‐PVA5
0
20
40
60
80
100
Fre
qu
en
cy (
%)
Figure 3-46. Composition of v6 pyrotag sequencing Archaea libraries for samples of ONKALO groundwater samples. OTUs with ≥1% abundance frequency are shown. Details can be found in Table A-8. NA: not annotated. The tree on top of the bar graph shows a Morisita–Horn distance measure illustrated using an unweighted pair group method with arithmetic mean (UPGMA) for the tree construction with taxonomic depth at species level for all observed sequences.
44
ONK-PVA5 ONK-PVA1 ONK-PVA6 ONK-PVA10 ONK-PVA3 ONK-PVA06_2 ONK-KR15_2 ONK-KR15
ChrenarchaeotaChrenarchaeota G‐BChrenarchaeota G‐C3
Chrenarchaeotic GHalobacteriales‐1Halobacteriales‐2Halobacteriales‐3
MethanobacteriaceaeMethanocellaMethanolobus
MethanomicrobiaMethanosarcinales
Methanoscarcinales GOMMethermicoccusThermoplasma
Thermoplasmata CCA47Thermoplasmata MBGThermoplasmata SAGThermoplasmatales
Thermoplasmatales AMOS1AThermoplasmatales ASC21
Thermoplasmatales T.Unknown
< 1%
Figure 3-47. Distribution of Archaea OTUs obtained with v4v6 pyrotag sequencing over ONKALO groundwater drillhole samples. OTUs with ≥1% abundance frequency are shown. NA: not annotated. The values for each sequenced OTU can be gauged from the bar length for each borehole. Each scale step indicated by a tick represents 20% of the total numbers of OTU; two steps thus equal 40 % and three steps 60%.
45
0 200 400 600 800 1000 1200 1400
SRB (cells mL-1)
-1200
-1000
-800
-600
-400
-200
0D
ep
th (
ma
sl)
Figure 3-48. The distribution of MPNs of sulphate-reducing bacteria (SRB) versus depth in Olkiluoto groundwater.
0 10 20 30 40 50 60 70
SRB abundance (%)
0
200
400
600
800
1000
1200
Dep
th (
m)
ONK‐KR15_1
ONK‐KR15_2
ONK‐PVA10 ONK‐PVA6_1
ONK‐PVA06_2
ONK‐PVA5ONK‐PVA3
ONK‐PVA1
Figure 3-49. Distribution of the percentage of SRB related OTUs. obtained with v4v6 pyrotag sequencing over depth in Olkiluoto groundwater samples (confer Figure 3-44).
46
Table 3-5. Percent occurrence of sulphide-producing operational taxonomic units (OTU) (Deltaproteobacteria and Firmicutes) in sequence libraries from ONKALO groundwater at ≥1% abundance and the electron accepting sulphur compound being reduced by most organisms related to the listed OTU.
OTU Electron acceptor
ONK-KR15_1 ONK-
KR15_2 ONK-PVA10
ONK-PVA6_1
Desulfobacterium SO42 - - - 1.4
Desulfobacula SO42 - - - 33.3
Desulfobulbus SO42 - - - -
Desulfosporosinus SO42 - 1.3 1.89 -
Desulfobulbaceae SO42 - - - 23.2
Desulfuromonadales S - - - - Desulfuromonas S 2.94 - 3.31 - Dethiosulfatibacter S2O3
2 - - 1.68 1.07 Sum 2.94 1.3 6.88 58.9
OTU Electron acceptor
ONK-PVA06_2 ONK-PVA5 ONK-PVA3 ONK-PVA1
Desulfobacterium SO42 1.46 4.75 - -
Desulfobacula SO42 7.61 - - -
Desulfobulbus SO42 - - 1.51 -
Desulfosporosinus SO42 - 19.15 - -
Desulfobulbaceae SO42 4 5.55 - -
Desulfuromonadales S 3.07 - - - Desulfuromonas S - 13.47 - - Dethiosulfatibacter S2O3
2 - - - - Sum 16.1 42.9 1.51 0.0
47
4 DISCUSSION
4.1 Diversity and numbers of microorganisms
4.1.1 Cultivable diversity
From 1997 up to the end of 2013, 153 samples were analysed for microbial numbers and cultivable diversity in Olkiluoto and ONKALO. The first set of samples was collected during the site investigations for the future Finnish repository for high level radioactive wastes (HLRW) during 1997 and 1999 (Haveman and Pedersen 2002; Haveman et al. 1999). In 2004, when Olkiluoto was selected, a still on-going, long-term investigation started of cultivable diversity in deep Olkiluoto drillholes. Starting approximately at the same time, 39 samples were collected from shallow groundwater during 2004 to 2006, before start of the ONKALO construction. All data from these investigations have been summarized in Figure 4-1 in comparison with similarly obtained data from the Swedish site investigations for a future HLRW repository (Hallbeck and Pedersen 2012). This figure consequently summarises the results presented in this report in chapter 3 distributed over analysis site and type of observation. The average TNC were approximately similar for all the deep sites, significantly higher for the shallow Olkiluoto. Shallow groundwater was expected to have higher number than deeper groundwater because of the vicinity to the photic biosphere and the root zone of trees and plants that will produce organic carbon to the groundwater and the microorganisms living there. However, just as for TNC in the deeper groundwater sites, there was a very large variation in the TNC for shallow groundwater as discussed previously (Pedersen 2008). Data from Äspö URL (Pedersen 2013b) and ONKALO (Table 3-2) have shown that the TNC decrease up to one order of degree during prolonged flushing of tunnel drillholes. Many of the sampled drillholes in ONKALO had been standing open and flowing before sampling and this possibly caused an artificial decrease in the observed TNC as observed elsewhere (Pedersen 2013b). As the TNC method does not distinguish dead or inactive bacteria from active and metabolizing bacteria, the analysis of ATP was introduced. ATP is an energy transporting molecule that is present in all living and active cells. In other words, presence of ATP attests to the presence of active and metabolizing cells. The method has been carefully tested and evaluated on a large number of groundwater samples. It was found that ATP generally correlated well with TNC in deep granitic groundwater (Eydal and Pedersen 2007).
48
-2 0 2 4 6 8
10Log(number mL
TNC
ATP
CHAB
NRB
IRB
MRB
SRB
AA
HA
AM
HM
MOB
Ana
lysi
s
Mean Laxemar Mean Forsmark Mean Shallow Olkiluoto Mean deep Olkiluoto Mean ONKALO
Figure 4-1. Distribution of the 10logarithm of each analysis result performed on groundwater samples from Laxemar and Forsmark in Sweden, shallow and deep groundwater in Olkiluoto and groundwater from ONKALO drillholes and the mean of the log numbers for each analysis. TNC, total number of cells; ATP, adenosine triphosphate; CHAB, culturable aerobic heterotrophic bacteria; NRB, nitrate-reducing bacteria; IRB, iron-reducing bacteria; MRB, manganese-reducing bacteria; SRB, sulphate-reducing bacteria; AA, autotrophic acetogens; HA, heterotrophic acetogens; AM, autotrophic methanogens; HM, heterotrophic methanogens; and MOB, methane oxidizing bacteria. Data below the detection limits for the MPN analyses (<0.2 cells mL1) were set to 10log(0.1) = 1. Whiskers indicate standard deviations of the means.
49
When TNC data from Olkiluoto and ONKALO (Figure 3-3) were compared with corresponding ATP sample data (Figure 3-6) a good correlation was found (Figure 3-9), which confirmed that the microorganisms observed using the TNC method indeed were active and metabolizing. The averages of 10log ATP in Figure 4-1 agrees well with the average 10log TNC data.
The observed range of TNC, from 103 to 106 cells mL–1 (Figure 4-1), is typical of most deep granitic groundwater samples. Similar averages of TNC and ATP were observed in Laxemar, Forkmark and Olkiluoto. This range is likely kept under control by viruses that attack, kill, and disintegrate microorganisms and thereby regulate TNC to numbers below 106 cells mL–1 as discussed in detail elsewhere (Eydal et al. 2009; Kyle et al. 2008; Pedersen et al. 2012).
4.1.2 Genomic diversity
The genomic diversity, represented by 16S rDNA sequence libraries, correlates with the cultivation results. Although it is difficult to compare directly, some general conclusions can be drawn. Sequences related to several different genera of sulphide producing bacteria (Table 3-5) were mainly found in ONKALO groundwater between 200 and 366 masl (Figure 3-45). A similar diversity was previously observed in sequence libraries from Olkiluoto drillholes using 454 pyrosequencing targeting the functional gene dsrB (dissimilatory sulfite reductase β-subunit). The genera Desulfobacula, Desulfobulbus, Desulfurivibrio and Desulfovibrio were reported and these genera were also observed in ONKALO groundwater (Nyyssönen et al. 2012; Pedersen et al. 2012).
The genus Pseudomonas was found from 200 masl and deeper. This genus may include IRB, MRB and NRB but, in opposite to SRB related sequences, it is not possible to specifically point out the respiratory processes of the bacteria that were represented these sequences. Methylomonas and Methylophilus typically oxidize methane and such bacteria were cultured previously from shallow groundwater (Figure 4-1). Sequences related to Hydrogenophaga were found at all depths. This genus is known for its ability to use H2 as source of electrons and energy (Willems et al. 1989) and it was most frequent in the libraries from one of the deepest drillhole in ONKALO, ONK-KR15. Many bacteria compete for H2 and many SRB have a low Km for H2 (<1 µM, Pedersen 2012) which may explain why observations of H2 seldom reaches above this concentration in groundwater with sulphate and SRB. Figure 4-2 shows that the concentration of H2 never exceeds 1 µM in groundwater above 300 masl. Below this depth the concentration of sulphate diminishes and most groundwater samples in Olkuiluoto below 400 masl are sulphate-free. At depth in Olkiluoto, in sulphate-free groundwater, the concentration of H2 increases over depth. There, Hydrogenophaga does not need to compete for H2 with SRB which may explain why sequences related to this genus appeared more frequently in the sequence library of sulphate-free ONK-KR15 groundwater and biofilms.
Much of the phylogenetic diversity in microbial ecosystems arises from a long list of rare taxa with ≥1% abundance frequency. Repeated sampling and 454 sequencing of samples from a salt marsh showed reliably similar diversity patterns in bacterial community composition (Bowen et al. 2012). The drillholes ONK-PVA6 and ONK-KR15 were sampled twice with a 5 months interval for both Bacteria and Archaea. Phylogenetic trees showed that the genomic diversity of these microbial communities was pair-wise more related to each other than to any other sample, with exception for the Archaea in ONK-PVA6 (Figure 3-44 and Figure 3-46). These results suggest that the microbial diversity in deep groundwater is stable and do not change rapidly. The two shallowest drillholes also had very similar Bacteria diversity profiles which suggest
50
ONK-PVA1 and ONK-PVA3 groundwater to be related in geochemical composition thereby attracting akin microorganisms. A similar conclusion was previously drawn from genomic analysis of shallow groundwater during the infiltration experiment (Jägevall 2012).
Figure 4-2. The distribution of H2 versus depth in Olkiluoto groundwater.
The sequence libraries for Archaea contain 4 major groups, Chrenarchaeota, Halobacteriales, methanogens and Thermoplasmatales. Again, the observations in Figure 3-46 correlate well with previous observations where Thermoplasmatales, which are heterotrophic and often found under thermal and acidic conditions, were found in large proportions of the 454 sequence libraries from Olkiluoto groundwater (Pedersen et al. 2012). However, the sequence relation to Thermoplasmatales was very distant and other thermophilic, but not necessarily acidofilic, Arachea may equally well be represented by them. Methanosarcinales and Methanobacteria were also found previously and in this investigation.
The use of high throughput sequencing using 454 and Illumina platforms generates very large sequence libraries. The sequences in these libraries can be compared with sequences in a number of different databases and the diversity profiles may differ more or less depending on what is in the libraries and the data bases. In other words, results such as those in Figure 3-44 and Figure 3-46 may look different if another database is used for identification. This means that the interpretation of sequence data is a very laborious and time consuming task that luckily is getting easier along with the development of new bioinformatic in silico tools. The genomic diversity results in this report are convincing and as the sequence libraries from samples in ONKALO and Olkiluoto grows, our understanding of microbial communities in
-3 -2 -1 0 1 210Log(H2) µM
0
-200
-400
-600
-800
-1000
-1200
Dep
th (
m)
2004-2007 2008 2009 2010 2011 2012 2013 ONKALO
51
Olkiluoto will grow as well. Complementing methods such a qPCR and fluorescent in situ hybridization (FISH) can be applied on samples for quantification as discussed elsewhere (Pedersen et al. 2012).
4.2 Sequence information, what can be concluded?
4.2.1 Change of sequencing platform
During execution of the sequencing work presented in this report, there has been a change from use of the 454 sequencing platform to use of the Illumina sequencing platform. This was because the 454 sequencing platform was recently bought by Hoffman LaRoche who soon after that announced the discontinuation of the 454 sequencing platform in 2013 in favor for their own Illumina platform.
4.2.2 Bacteria
The genomic sequence information for Bacteria was obtained with 454 sequencing platform and is represented by stretches of approximately 450 bases. When these stretches are run versus DNA sequences in international databases, similarities with reported sequences are obtained. It is possible to conclusively identify SRB from such sequence comparison because microorganisms with the ability to produce sulphide from oxidized sulphur compounds are a relatively well coherent phylogenetic group of related microorganisms. In opposite, the capability for nitrate, iron and manganese reduction is distributed over many different types of unrelated species and a phylogenetic position inferred by a sequence is not enough for a conclusive judgment of physiological respiratory characteristics. The sulphide producing OTUs identified in Table 3-5 have specific physiological characteristics as shown in Table 4-1. It is now possible to compare the MPN results with the observed OTUs and identify missing cultivation strategies. All OTU related to SRB except one OTU, Desulfobacula, can use lactate as source of carbon and lactate was used as carbon source in the SRB medium. Because a large percentage of the OTUs from ONK-PVA6 belonged to Desulfobacula that does not use lactate (or acetate), this genus would not be detected by the present MPN analysis. However, because sequence information about the SRB diversity in the sampled ONKALO groundwater now is at hand, modified MPN media can be designed for quantification of SRB. In addition, it will also be possible to design genomic probes that can be used for quantitative PCR for different species and fluorescent in situ hybridisation analysis of various groups of planctonic and attached microorganisms. Likewise, it will be possible to design media and genomic probes for other groups of microorganisms indicated by the 454 sequencing results to be present in Olkiluoto/ONKALO groundwater.
52
Table 4-1. Physiological characteristics of microorganism groups detected with 454 sequencing (Table 3-5).
Microorganism Electron acceptor
Acetate Lactate H2 Other carbon
sources Comments
Desulfobacterium SO42 + + +
Many different organic acids
Desulfobacula SO42
Pyruvate, toluene
Desulfobulbaceae SO42 + +
Pyruvate, ethanol
Desulfobulbus SO42 + +
Pyruvate, ethanol
Desulfosporosinus SO42 + +a -
Homoacetogenic growth
Desulfuromonadales S + +a +a -
Desulfuromonas S + +a +a -
Dethiosulfatibacter S2O32 + pyruvate, serine,
a Some species
4.2.3 Archaea
The genomic sequence information for Archaea was obtained with the Illumina sequencing platform and is represented by stretches of approximately 50 bases. As such, these stretches are fairly short but still informative. The detection of sequences distantly related to Thermoplasmata suggests the presence of thermophilic Archaea in the sampled groundwater. The only reasonable source of thermophiles is the deep hot biosphere (Gold 1992) but it is not possible at the present stage in this research to be conclusive. Off much more importance is the possible presence of Archaea known to be one part of anaerobic methane oxidizing consortia, mainly together with specific SRB. The Illumina results showed presence of Methanosarcinales and this order is often observed in environments where anaerobic oxidation of methane (AOM) is on-going (Knittel and Boetius 2009). The data presented in Figure 3-46 comprises sequences in ≥1% abundance frequency, i.e. 5 to at most 16 OTUs (Table 3-4), but the total number of observed OTUs ranges from 87 to 101. This part of the data is commonly referred to as the rare biosphere (Bowen et al. 2012). Several sequences related to ANME-1, ANME-2 and ANME-3 were found when this rare biosphere part of the sequence library was explored (data not shown). Results from other environments suggest that the rare biosphere part of sequence libraries represents reliable patterns of community. It is, consequently, likely that Archaea capable of AOM can be active in deep Olkiluoto groundwater. This conclusion is corroborated by the SURE experiments where results typical for a microbial AOM process were obtained (Edlund et al. 2015; Pedersen 2013a; Pedersen et al. 2014).
4.3 Numbers and activity of sulphate-reducing bacteria in Olkiluoto
Cultivation using the MPN methodology has indicated that microorganisms representative of each of the cultivated physiological groups were generally present in most samples (Figure 4-1). The distribution of numbers over depth was generally fairly scattered, although some trends were obvious, as exemplified by SRB (Figure 3-26). The highest SRB values were observed at
53
depths of 200 to –400 masl. This is where brackish sulphate groundwater mixes with methane-rich saline Na-Ca-Cl groundwater and where a peak in sulphide concentration is observed (See figures with pertinent text 4-16 and 4-17 in Pedersen 2008). DNA analysis of groundwater at depths of 200 to –400 masl presented in this report (Figure 3-49) and elsewhere (Pedersen et al. 2012) strongly supports the conclusion that SRB are more abundant and active in this than in any other depth range analysed in Olkiluoto.
The activity of SRB and the concomitant sulphide production is of great safety concern, because sulphide is corrosive to copper. The processes of sulphide production and precipitation depend on several controlling parameters, such as availability of energy, carbon sources, sulphate, and ferrous iron, the presence of viable SRB, and the activity of attacking phages. From this perspective, correlation analysis using pair-wise data comparison appears inadequate. This was indeed found to be correct previously, as pair-wise comparisons between hydrogeochemistry and microbiology data did not reveal any good significant relationships (Pedersen et al. 2012). When several factors influencing sulphide production were instead evaluated together, however, useful information about the importance of each factor for sulphide formation and precipitation was obtained. When all the MPN numbers from deep Olkiluoto groundwater were plotted against sulphate or methane, the data did not correlate. However, if both sulphate and methane were plotted against MPN numbers, a clear relationship developed. The data presented in Pedersen et al. (2012) were here reinforced with data obtained during 2010 2013. It was again found that microbial populations were much more abundant and diverse in groundwater that contained both methane and sulphate, but were low or below detection in many of the samples containing low concentrations of either methane or sulphate (Figure 3-43).
54
55
5 ACKNOWLEDGEMENTS
The research leading to these results received funding from Posiva Oy. The authors are grateful to Björn Hallbeck and Jessica Johansson at Microbial Analytics Sweden for their excellent laboratory work. The assistance of Maarit Yli-Kaila, Pauliina Alho and Lauri Parviainen at the ONKALO site was first rate and indispensable. The pyrotag sequencing data were made possible by the Deep Carbon Observatory’s Census of Deep Life programme supported by the Alfred P. Sloan Foundation. Pyrotag sequencing was performed at the Marine Biological Laboratory (Woods Hole, MA, USA), where we received excellent assistance from Sharon Grim, Hilary Morrison, Susan Huse, Mitch Sogin, Joseph Vineis, and Andrew Voorhis.
56
57
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A. APPENDIX
Table A-1. Biomass determinations for groundwater in Olkiluoto, sampled in 2010 2013. TNC = total number of cells, SD = standard deviation, n = number of observations, CHAB = cultivable heterotrophic aerobic bacteria, and MPN = sum of all most probable number of cells values (see Tables A-2 and A-3).
Drillhole
Sampled (Y-M-D)
Depth (masl)
TNC (cells mL1)
SD
n
ATP (amol mL1)
SD
n
CHAB (cells mL1)
SD nCHAB/
TNC (%)
ATP/ TNC
MPN/ TNC (%)
OL-KR50 2010-03-02 -721.0 40000 8700 3 57100 9650 8 8070 577 3 20.18 1.43 225.00 OL-KR39 2010-06-16 -376.8 250000 41000 3 1050000 228000 9 840000 85000 2 336.00 4.20 >65.60 OL-KR44 2010-06-30 -538.5 130000 24000 3 37600 13400 9 1900 460 3 1.46 0.29 10.24 OL-KR44 2010-08-10 -94.8 330000 36000 3 79900 16200 9 176000 9000 3 53.33 0.24 15.16 OL-KR11 2010-08-17 -373.9 510000 40000 3 220000 13000 9 122000 18400 2 23.92 0.43 >32.42 OL-KR6 2010-09-28 -102.3 61000 7100 3 7900 1740 9 3000 265 3 4.92 0.13 0.92 OL-KR50 2010-09-28 -408.4 88000 41000 3 56400 35600 9 81300 5510 3 92.39 0.64 34.25 OL-KR51 2010-10-26 -344.3 83000 18000 3 26300 4320 9 24700 5130 3 29.76 0.32 6.24 OL-KR50 2010-11-18 -350.1 67000 3000 3 6420 1220 9 4530 208 3 6.76 0.10 11.98 OL-KR6 2010-11-23 -97.0 87000 24000 3 25600 4370 9 8350 636 2 9.60 0.29 4.48 OL-KR51 2010-12-20 -237.6 27000 3100 3 10800 840 9 8170 666 3 30.26 0.40 30.20 OL-KR6 2011-02-08 -74.3 49000 5800 3 25500 2140 9 14000 850 3 28.57 0.52 5.51 OL-KR30 2011-03-02 -41.8 120000 24000 3 38700 5550 9 14600 1570 3 12.17 0.32 2.62 OL-KR40 2011-03-09 -708.3 73000 42000 3 11500 3060 9 3330 208 3 4.56 0.16 1.88 OL-KR53 2011-05-30 -198.0 72000 1300 3 20500 960 9 10900 1190 3 15.14 0.28 7.05 OL-KR53 2011-08-10 -113.2 43000 7800 3 15900 15100 9 380 79 3 0.88 0.37 OL-KR53 2011-09-27 -46.5 75000 11000 3 3700 1430 9 227 67 3 0.30 0.05 0.19 Ol-KR56 2012-08-15 -1116 31000 4700 3 35400 5000 9 143 5.77 3 0.46 1.14 0.55 OL-KR55 2012-08-28 -674.8 110000 26000 3 21500 2730 9 19100 3150 3 17.36 0.20 7.35 OL-KR46 2012-11-06 -190.9 59000 4900 3 14600 2020 9 5220 356 3 8.84 0.24 1.64 OL-KR46 2013-03-27 -456.7 370000 190000 3 31800 5010 9 2460 867 3 0.66 0.09 0.65 OL-KR54 2013-04-16 -328.5 85000 6900 3 3800 280 9 790 690 3 0.93 0.04 1.29 OL-KR55 2013-05-20 -674.8 140000 18000 3 7200 1100 9 2960 84.9 3 2.11 0.05 0.11 OL-KR56 2013-06-11 -1115.7 10000 4700 3 48900 2820 9 440 20 3 4.40 4.89 0.01 OL-KR57 2013-06-25 -42.7 110000 12000 3 3900 460 9 60 30 3 0.05 0.04 0.01 OL-KR55 2013-07-02 -233.3 32000 11000 3 22300 1950 9 3170 820 3 9.91 0.70 25.69 OL-KR6 2013-08-22 -329.6 30000 14000 3 2400 510 9 60 30 3 0.20 0.08 0.11 OL-KR46 2013-11-06 -530.0 340000 80000 3 11600 720 9 40 17 3 0.01 0.03 7.15
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Table A-2. The most probable numbers of nitrate-, iron-, manganese-, and sulphate-reducing bacteria (NRB, IRB, MRB, and SRB, respectively) in groundwater from Olkiluoto sampled in 2010–2013. L and U limits are the 95% confidence values.
Drillhole Sampled (Y-M-D)
Depth (masl)
NRB (cells mL1)
L limit
U limit
IRB (cells mL1)
L limit
U limit
MRB (cells mL1)
L limit
U limit
SRB (cells mL1)
L limit
U limit
OL-KR50 2010-03-02 -721.0 90000 30000 290000 1.1 0.4 2.9 <0.2 - - <0.2 - - OL-KR39 2010-06-16 -376.8 >160000 - - 900 300 2900 1600 - - 500 200 1700 OL-KR44 2010-06-30 -538.5 13000 5000 39000 70 30 210 220 100 580 5.0 2.0 17.0 OL-KR44 2010-08-10 -94.8 50000 20000 200000 0.2 0.1 1.1 22.0 10 58.0 0.2 0.1 1.1 OL-KR11 2010-08-17 -373.9 >160000 - - 1600 600 5300 1600 1300 500 3900 OL-KR6 2010-09-28 -102.3 500 200 1700 24.0 10 94.0 30 10 120 2.3 0.9 8.6 OL-KR50 2010-09-28 -408.4 30000 10000 130000 0.6 0.2 1.8 80 30 250 <0.2 - - OL-KR51 2010-10-26 -344.3 5000 2000 17000 3.0 1.0 12.0 170 70 480 0.2 0.1 1.1 OL-KR50 2010-11-18 -350.1 8000 3000 25000 0.2 0.1 1.1 23.0 9.0 86.0 <0.2 - - OL-KR6 2010-11-23 -97.0 3000 1000 12000 8.0 3.0 24.0 500 200 2000 80 30 250 OL-KR51 2010-12-20 -237.6 8000 3000 25000 0.8 0.3 2.4 22.0 10 58.0 130 50 390 OL-KR6 2011-02-08 -74.3 2300 900 8600 80 30 250 80 30 250 7.0 3.0 21.0 OL-KR30 2011-03-02 -41.8 3000 1000 12000 0.7 0.2 2.1 140 60 360 0.4 0.1 1.7 OL-KR40 2011-03-09 -708.3 1300 500 3900 14.0 6.0 36.0 3.0 1.0 12.0 30 10 120 OL-KR53 2011-05-30 -198.0 5000 2000 17000 5.0 2.0 17.0 70 30 210 0.8 0.3 2.4 OL-KR53 2011-08-10 -113.2 80 30 250 1.7 0.7 4.6 0.8 0.3 2.4 0.2 0.1 1.1 OL-KR53 2011-09-27 -46.5 140 60 360 0.4 0.1 1.7 <0.2 - - <0.2 - - Ol-KR56 2012-08-15 -1116 170 80 410 <0.2 - - <0.2 - - <0.2 - - OL-KR55 2012-08-28 -674.8 8000 3000 25000 80 30 250 1.7 0.7 4.6 1.3 0.5 3.8 OL-KR46 2012-11-06 -190.9 700 300 2100 130 50 390 130 50 390 13 5 39 OL-KR46 2013-03-27 -456.7 800 300 2500 1600 600 5300 n.a. 8.0 3.0 25.0 OL-KR54 2013-04-16 -328.5 1100 400 3000 0.2 0.1 1.1 n.a. 0.4 0.1 1.7 OL-KR55 2013-05-20 -674.8 130 50 390 14.0 6.0 36.0 n.a. 11.0 4.0 30.0 OL-KR56 2013-06-11 -1115.7 0.8 0.3 2.4 0.0 - - n.a. - - OL-KR57 2013-06-25 -42.7 8 3.0 25 2.7 1.2 6.7 n.a. 0.0 - - OL-KR55 2013-07-02 -233.3 8000 3000 25000 220 100 580 n.a. 2.3 0.9 8.6 OL-KR6 2013-08-22 -329.6 24 10 94 8.0 3.0 25 n.a. 0.0 0.0 0.0 OL-KR46 2013-11-06 -530.0 280 120 690 30 10 120 n.a. 24000 10000 94000
62
Table A-3. The most probable numbers of autotrophic acetogens (AA) and methanogens (AM), heterotrophic acetogens (HA) and methanogens (HM), and methane-oxidizing bacteria (MOB) in groundwater from Olkiluoto, sampled in 2010 2013. L and U limits are the 95% confidence values.
Drillhole Sampled (Y-M-D)
Depth(masl)
AA (cells mL1)
L limit
U limit
HA (cells mL1)
L limit
U limit
AM (cells mL1)
L limit
U limit
HM (cells mL1)
L limit
U limit
OL-KR50 2010-03-02 -721.0 <0.2 - - <0.2 - - <0.2 - - <0.2 - - OL-KR39 2010-06-16 -376.8 500 200 1700 500 20 1700 <0.2 - - <0.2 - - OL-KR44 2010-06-30 -538.5 5.0 2.0 17.0 11.0 4.0 30.0 2.3 0.9 8.6 <0.2 - - OL-KR44 2010-08-10 -94.8 0.8 0.3 2.4 <0.2 - - 0.0 <0.2 - - OL-KR11 2010-08-17 -373.9 17.0 7.0 48.0 800 300 2500 0.0 <0.2 - - OL-KR6 2010-09-28 -102.3 1.1 0.4 2.9 0.8 0.3 2.4 0.2 0.1 1.1 <0.2 - - OL-KR50 2010-09-28 -408.4 50.0 20.0 170.0 0.4 0.1 1.7 11.0 4.0 30.0 <0.2 - - OL-KR51 2010-10-26 -344.3 2.3 0.9 8.6 3.0 1.0 12.0 <0.2 - - <0.2 - - OL-KR50 2010-11-18 -350.1 0.8 0.3 2.4 2.3 0.9 8.6 <0.2 - - <0.2 - - OL-KR6 2010-11-23 -97.0 7.0 3.0 21.0 300 100 1200 <0.2 - - <0.2 - - OL-KR51 2010-12-20 -237.6 0.2 0.2 2.1 0.2 0.2 2.1 <0.2 - - <0.2 - - OL-KR6 2011-02-08 -74.3 2.2 0.9 5.6 230 90 860 <0.2 - - <0.2 - - OL-KR30 2011-03-02 -41.8 <0.2 - - 0.2 0.1 1.1 <0.2 - - <0.2 - - OL-KR40 2011-03-09 -708.3 <0.2 - - 1.1 0.4 2.9 13.0 5.0 38.0 8.0 3.0 24.0 OL-KR53 2011-05-30 -198.0 0.2 0.1 1.1 2.3 0.9 8.6 <0.2 - - <0.2 - - OL-KR53 2011-08-10 -113.2 <0.2 - - <0.2 - - <0.2 - - n.a. - - OL-KR53 2011-09-27 -46.5 0.2 0.1 1.1 <0.2 - - <0.2 - - <0.2 - - Ol-KR56 2012-08-15 -1116 <0.2 - - - - - - - - - - - OL-KR55 2012-08-28 -674.8 <0.2 - - - - - - - - - - - OL-KR46 2012-11-06 -190.9 0.4 0.1 1.5 - - - - - - - - - OL-KR46 2013-03-27 -456.7 n.a. n.a. n.a. n.a. OL-KR54 2013-04-16 -328.5 n.a. n.a. n.a. n.a. OL-KR55 2013-05-20 -674.8 n.a. n.a. n.a. n.a. OL-KR56 2013-06-11 -1115.7 0.0 - - n.a. n.a. n.a. OL-KR57 2013-06-25 -42.7 n.a. n.a. n.a. n.a. OL-KR55 2013-07-02 -233.3 n.a. n.a. n.a. n.a. OL-KR6 2013-08-22 -329.6 n.a. n.a. n.a. n.a. OL-KR46 2013-11-06 -530.0 1.3 0.5 3.8 n.a. n.a. n.a.
n.a. not analysed
63
Table A-4. Biomass determinations for groundwater in ONKALO, sampled in 2010 2013. TNC = total number of cells, SD = standard deviation, n = number of observations, CHAB = cultivable heterotrophic aerobic bacteria, and MPN = sum of all most probable number of cells values (see Tables A-2 and A-3).
Drillhole
Sampled
(Y-M-D)
Depth
(masl)
TNC (cells mL1)
SD
n
ATP (amol mL1)
SD
n
CHAB(cells mL1)
SD nCHAB/
TNC (%)
ATP/ TNC
MPN/ TNC (%)
ONK-PVA6 2010-09-23 -327 37000 6100 3 27300 3170 9 16300 3210 3 44.05 0.74 245.45
ONK-PVA8 2010-09-23 -276.4 16000 50 3 5300 870 9 187 82 3 1.17 0.33 31.30
ONK-PVA9 2011-09-20 -417.5 21000 3100 3 10600 1080 9 6700 361 3 31.90 0.50 5.24
ONK-KR15 2012-01-11 -399 18000 5000 3 4500 810 9 2500 436 3 13.89 0.25 4.49
ONK-PVA1 2012-04-16 -14.6 25000 9600 3 5300 2420 9 <1000 - 3 0.00 0.21 0.69
ONK-PVA3 2012-04-16 -78.5 29000 8900 3 14500 2810 9 140 17 3 0.48 0.50 0.53
ONK-PVA5 2012-04-17 -228.7 20000 3800 3 800 460 9 <1000 - 3 0.00 0.04 0.16
ONK-PVA6 2012-04-17 -327 49000 10000 3 10600 2230 9 70 17 3 0.14 0.22 2.15
ONK-PVA8 2012-04-18 -276.4 38000 5300 3 10800 710 9 2300 120 3 6.05 0.28 0.14
ONK-PVA10 2012-04-18 -366.45 3200 1300 3 1800 640 9 <1000 - 3 0.00 0.56 0.11
ONK-PVA8 2013-03-19 -276.4 610000 35000 3 170000 42000 9 930 82 3 0.15 0.28 0.46
ONK-PVA9 2013-12-10 -417.5 270000 31000 3 25400 4950 9 59700 5510 3 22.11 0.09 6.30
64
Table A-5. The most probable numbers of nitrate-, iron-, manganese-, and sulphate-reducing bacteria (NRB, IRB, MRB, and SRB, respectively) in groundwater from ONKALO sampled in 2010–2013. L and U limits are the 95% confidence values.
Drillhole Sampled(Y-M-D)
Depth (masl)
NRB(cells mL1)
L limit
U limit
IRB (cells mL1)
L limit
U limit
MRB(cells mL1)
L limit
U limit
SRB(cells mL1)
L limit
U limit
ONK-PVA6 2010-09-23 -327 90000 30000 290000 500 200 2000 220 100 580 80 30 250
ONK-PVA8 2010-09-23 -276.4 5000 2000 20000 1.1 0.4 2.9 0.4 0.1 1.5 1.4 0.6 3.5
ONK-PVA9 2011-09-20 -417.5 1100 400 3000 <0.2 - - <0.2 - - 0.8 0.3 2.4
ONK-KR15 2012-01-11 -399 800 300 2500 <0.2 - - 8.0 3.0 25.0 <0.2 - -
ONK-PVA1 2012-04-16 -14.6 <0.2 - - 1.2 0.5 2.9 0.2 0.1 1.1 <0.2 - -
ONK-PVA3 2012-04-16 -78.5 130 50 390 22.0 9.0 56.0 <0.2 - - <0.2 - -
ONK-PVA5 2012-04-17 -228.7 <0.2 - - 30 10 120 0.9 0.3 2.5 <0.2 - -
ONK-PVA6 2012-04-17 -327 35.0 16.0 82.0 900 300 2900 110 40 300 7.0 3.0 21.0
ONK-PVA8 2012-04-18 -276.4 23.0 9.0 86.0 0.8 0.3 2.4 0.7 0.2 2.1 0.2 0.1 1.1
ONK-PVA10 2012-04-18 -366.45 3.0 1.0 12.0 0.4 0.1 1.7 0.2 0.1 1.1 <0.2 - -
ONK-PVA8 2013-03-19 -276.4 2800 1200 6900 1.7 0.7 4.6 n.a. 2.3 0.9 8.6
ONK-PVA9 2013-12-10 -417.5 17000 7000 48000 0.0 0.0 0.0 n.a. 0.0 0.0 0.0
n.a. not analysed
65
Table A-6. The most probable numbers of autotrophic acetogens (AA) heterotrophic acetogens (HA) autothropic methanogens (AM) and heterotrophic methanogens (HM), in groundwater from ONKALO, sampled in 2010 2013. L and U limits are the 95% confidence values.
Drillhole Sampled (Y-M-D)
Depth (masl)
AA(cells mL1)
L limit
U limit
HA(cells mL1)
L limit
U limit
AM(cells mL1)
L limit
U limit
HM(cells mL1)
L limit
U limit
ONK-PVA6 2010-09-23 -327 17.0 7.0 48.0 110 40.0 300 <0.2 - - <0.2 - -
ONK-PVA8 2010-09-23 -276.4 5.0 2.0 17.0 1.1 0.4 2.9 <0.2 - - <0.2 - -
ONK-PVA9 2011-09-20 -417.5 0.4 0.1 1.7 2.3 0.9 8.6 <0.2 - - <0.2 - -
ONK-KR15 2012-01-11 -399 <0.2 - - <0.2 - - <0.2 - - <0.2 - -
ONK-PVA1 2012-04-16 -14.6 <0.2 - - n.a. n.a. n.a.
ONK-PVA3 2012-04-16 -78.5 <0.2 - - n.a. n.a. n.a.
ONK-PVA5 2012-04-17 -228.7 <0.2 - - n.a. n.a. n.a.
ONK-PVA6 2012-04-17 -327 <0.2 - - n.a. n.a. n.a.
ONK-PVA8 2012-04-18 -276.4 <0.2 - - n.a. n.a. n.a.
ONK-PVA10 2012-04-18 -366.45 m.d. - - n.a. n.a. n.a.
ONK-PVA8 2013-03-19 -276.4 13.0 5.0 39.0 n.a. n.a. n.a.
ONK-PVA9 2013-12-10 -417.5 0.0 0.0 0.0 n.a. n.a. n.a.
n.a. not analysed
66
Table A-7. Percent occurrence of operational taxonomic unites (OTU) in Bacteria sequence libraries from ONKALO. Sequences with ≥1% abundance frequency are shown.
OTU ONK-KR15_1 ONK_KR15_2 ONK_PVA10 ONK_PVA6 ONK_PVA 6_2 ONK_PVA5 ONK_PVA3 ONK_PVA1
Sample date 2012-04-16 2012-09-04 2012-04-18 2012-04-17 2012-09-04 2012-04-17 2012-04-16 2012-04-16 Acholeplasma 1.26 2. 7 1.42 2.96 Acidovorax 23.96 1.38 Acinetobacter 1.11 Alishewanella 1.38 Anaerovorax 1.21 Aquabacterium 1. 5 1.37 Bacteria OD1 2 .14 35.9 Bacteria OP11 1.22 Bacteria OP3 22.78 22.4 Bacteroidetes 16.65 Brevundimonas 1.66 8.92 Chlorobiales 5.36 Coriobacteriaceae 3.21 Cyanobacteria 9.65 Deferribacterales 1.38 Dehalogenimonas 3.21 2.22 Deltaproteobacteria 4.19 3. 1 Desulfobacterium 1.46 1.4 4.75 Desulfobacula 7.61 33.27 Desulfobulbus 1.51 Desulfosporosinus 1.3 1.89 19.15 Desulfobulbaceae 4 23.2 5.55 Desulfuromonadales 3. 7 Desulfuromonas 2.94 3.31 13.47 Dethiosulfatibacter 1.68 1. 7 Erysipelothrix 8.21 2.55
67
Ferribacterium 4.26 Fusibacter 5.18 7.92 1 .52 Gallionellaceae 1 . 9 Hoeflea 1 .15 4.93 5.39 3.67 Hydrogenophaga 9. 2 3 .4 6.39 5. 7 2.51 6.54 Lachnospiraceae 2.41 1.82 Lutibacter 8.3 5.51 4.57 4.39 Methylomonas 1.84 Methylophilus 19.9 NA-1 2.28 5.72 1.97 3.76 NA-2 3.81 3.79 4. 2 1. 1 3. 9 1 .5 3.32 3.31 Nitrospiraceae 1.22 1.92 2.95 1.89 1.75 Pseudidiomarina 6.47 3.61 Pseudomonas 7 9.91 3.97 1.19 1.93 Rhodocyclaceae 1 .27 Rhodoferax 2.18 Roseovarius 1.41 Simplicispira 2.23 Sphingobacteriales 1.92 2.62 1.94 1.36 Sphingopyxis 1.11 Syntrophaceae 1. 1 Syntrophus 3.51 9.87 Thermoanaerobacterales 1.61 Thermoplasmata 3.47 Thiobacillus 4.34 8.75 24.77 12. 7 < 1% 1 .33 8.63 8.1 18.11 8.73 13. 1 18.48 16.56
68
Table A-8. Percent occurrence of operational taxonomic unites (OTU) in Archaea sequence libraries from ONKALO. Sequences with ≥1% abundance frequency are shown.
OTU ONK-KR15 ONK-KR15_2 ONK-PVA10 ONK-PVA6 ONK-PVA06_2 ONK-PVA5 ONK-PVA3 ONK-PVA1
Sample date 2012-04-16 2012-09-04 2012-04-18 2012-04-17 2012-09-04 2012-04-17 2012-04-16 2012-04-16
Chrenarchaeota 3.53 6.7
Chrenarchaeota G-B 1.77 Chrenarchaeota G-C3 10.93 3.28 1.49 2.64 2.83
Chrenarchaeotic G 4.65 14.78 20.98 8.48 3.55 13.6 14.37
Halobacteriales-1 1.16 2.26 7.71 4.71 4.53 13.91 8.88
Halobacteriales-2 1.11 3.13 1.77 4.95 3.81
Halobacteriales-3 2.15 1.12 2.68 1.6 Methanobacteriaceae 5.61 2.88 1.88 1.12
Methanocella 3.24
Methanolobus 1.24 8.32 11.19 1.07
Methanomicrobia 4.38
Methanosarcinales 1.32 1.82
Methanoscarcinales GOM 3.83 6.5 6.28 52.19 1.83 33.11
Methermicoccus 8.31 8.56 1.5 4.44 2.89 1.63 1.7 Thermoplasma 2.28 Thermoplasmata CCA47 2 Thermoplasmata MBG 1.49 1.18 Thermoplasmata SAG 68.00 56.54 12.51 11.83 34.48 17.56 24.79 15.97
Thermoplasmatales 4.81 6.16 19.55 9.16 5.16 2.6 12.75 5.86
Thermoplasmatales AMOS1A 1.66 Thermoplasmatales ASC21 1.06 1.6 1.45
Thermoplasmatales T. 14.37 17.63 6.69 6.93 8.93 7.83 10 5.61
Unknown 1.09 1.77 1.6 2.87
< 1% 3.19 3.48 8.36 6.12 5.51 4.3 3.98 3.39
69
70
