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Working Report 2015-12
The Diversity of Microbial Communities
in Olkiluoto Bedrock Groundwaters 2009–2013
Hanna Miettinen, Malin Bomberg, Mari Nyyssönen,
Heikki Salavirta, Elina Sohlberg, Minna Vikman, Merja Itävaara
May 2015
POSIVA OY
Olkiluoto
FI-27160 EURAJOKI, FINLAND
Phone (02) 8372 31 (nat.), (+358-2-) 8372 31 (int.)
Fax (02) 8372 3809 (nat.), (+358-2-) 8372 3809 (int.)
Working Report 2015-12
The Diversity of Microbial Communities
in Olkiluoto Bedrock Groundwaters 2009–2013
Hanna Miettinen, Malin Bomberg, Mari Nyyssönen,
Heikki Salavirta, Elina Sohlberg, Minna Vikman, Merja Itävaara
VTT
May 2015
Working Reports contain information on work in progress
or pending completion.
The Diversity of Microbial Communities in Olkiluoto Bedrock Groundwaters 2009–2013
ABSTRACT
Deep groundwater samples were collected from Olkiluoto between December 14th,
2009 and August 21st, 2013. A total of 19 samples were collected from packer-isolated
bedrock fractures between 296 m to 798 m below surface level. The archaeal, bacterial,
fungal, sulphate reducer and methanogen communities were studied based on RNA
fraction providing information of active microbial communities and DNA fractions
showing the total microbial groups. The analysis was performed by pyrosequencing
using 454 FLX Titanium technology (454 Life Sciences Ltd.). The number of sulphate
reducing bacteria (SRB), ammonium oxidisers, denitrifying bacteria and methanogenic
archaea were analysed based by quantification of the marker genes dsrB, amoA, narG
and mcrA, respectively.
According to this study, fungi, archaea and bacteria appeared to play a versatile role in
major ecological processes in terrestrial deep subsurface environments. The dominating
bacteria belonged to groups capable of both heterotrophic and autotrophic (i.e. fix CO2
as carbon source) life styles, whereas the majority of the identified characterized
archaea were associated with methane metabolism. In the identified total (DNA based)
and in the active (RNA based) fungal community the Ascomycota phylum dominated in
almost all samples. In comparison to the bacterial communities, the fungal and archaeal
communities were less diverse. The total number of microbial cells, sulphate reducers,
ammonia oxidisers and methanogens decreased with the depth. The role of fungi in
deep biosphere environments is largely unknown, but especially Ascomycota have been
shown to play an important role in denitrification in deepsea environments, and may
thus have a role in the N cycle in deep groundwater in Olkiluoto.
The nitrogen cycle in deep subsurface environments is still not well understood.
Common genes involved in the nitrogen cycle, such as amoA and narG, were detected
at lower concentrations as expected. However, nitrogen cycling microorganisms not
previously detected in Olkiluoto groundwater were found as well, as novel ANME-2D
archaea probably involved in the methane and nitrogen cycle.
SRB were present throughout the studied depths in Olkiluoto, with exception of the
deepest water layers. In general the SRB communities were dominated by
Desulfobacterales like SRB. The highest SRB diversity index occurred in brackish
sulphate containing water collected above 415 m vertical depth. This suggests that in
brackish water the different SRB groups are present in even proportions and no group
has selective advantage under the prevailing environmental conditions. This may be due
to the higher diversity and availability of organic carbon and electron acceptors in the
brackish water. In the mixed samples OL-KR6/422m, OL-KR23/425m and OLKR5/457m higher amount of sulphate and sulphide and dsrB transcripts indicate active
sulphate reduction.
The number of methanogens throughout the depth profile was low. The most frequently
detected mcrA sequences belonged to a Methanosarcinaceae which generally are able to
utilize many different small carbon compounds for methanogenesis, such as H2-CO2,
acetate, methanol, methylamines and methyl sulphides. It is even possible that the
detected mcrA sequences belong to the ANME-2D, which are able to oxidize methane
anaerobically.
The microbial community of OL-KR13/360m and OL-KR6/422m were sampled at two
different time points, in 2010 and 2012 (OL-KR13/360m) and in 2010 and 2013 (OLKR6/422m). In OL-KR13/360m the community had changed dramatically between the
two sampling times, and some changes in the in the hydrogeochemistry of groundwater
was detected, especially the sulphide concentration decreased. In OL-KR6/422m
changes in microbial community profiles were also seen, but the hydrogeochemistry did
not noticeably vary between time points. Epsilonproteobacteria were especially
dominating in samples from mixed fracture zones obtained between 2009 and 2011. The
epsilonproteobacteria are especially S oxidizers, a process which may become activated
when the sulphate-rich and methane-rich water layers mix.
Due to the high number of uncultured or unculturable microorganism present in nature,
classification to lower taxonomic levels, such as genus and species is mainly not
possible. This was also seen in the present analysed data set, where the majority of the
microbial taxa were not classified to lower taxonomical levels. Nevertheless, the
sequences matching most closely to the Olkiluoto sequences often belonged to deep
subsurface and deepsea uncultured microorganisms, especially concerning the archaea
and fungi.
Keywords: qPCR, Sulphate reducer, Pyro Sequencing, Microbial Community, Nitrogen
cycle, Methanogen.
Mikrobilajistojen diversiteetti Olkiluodon kalliopohjavesissä 2009–2013
TIIVISTELMÄ
Pohjavesinäytteitä otettiin Olkiluodon alueelta 14.12.2009 ja 21.8.2013 välisenä aikana.
Yhdeksäntoista vesinäytettä otettiin sekä tulpatusta että avoimesta rakovyöhykkeestä
296-798 metrin syvyydeltä maan pinnasta. Bakteerien, arkeonien, sienten,
sulfaatinpelkistäjien ja metanogeenien diversiteettiä tutkittiin RNA-pohjaisilla
menetelmillä aktiivisen mikrobiyhteisön ja DNA-pohjaisilla menetelmillä koko
mikrobiyhteisön analysoimiseksi hyödyntäen pyrosekvensointia 454 Titanium
laitteistolla (454 Life Sciences Ltd.). Sulfaatinpelkistäjien, ammoniakin hapettajien,
denitrifioivien ja metanogeenien määrät ja aktiivisuus arvioitiin kvantitoimalla
merkkigeenejä dsrB, amoA, narG ja mcrA.
Tässä tutkimuksessa saadut tulokset osoittavat, että sienillä ja bakteereilla on
moninainen rooli syväbiosfäärin ekologisissa prosesseissa. Suurin osa bakteereista
kuuluu heterotrofisiin ryhmiin, jotka kykenevät kuitenkin myös autotrofisiin
toimintoihin, esim. käyttämään CO2:a hiilenlähteenä. Tunnistetut karakterisoidut
arkeonit liittyivät enimmäkseen metaanin metaboliaan. Sienten roolista syväbiosfäärissä
ei tiedetä paljon, mutta erityisesti Ascomycota-sienillä on raportoitu olevan merkittävä
osa syvämeren pohjan denitrifikaatiossa ja ne voivat olla merkittävässä roolissa myös
Olkiluodon pohjavedessä. Lähes kaikissa näytteissä Ascomycota oli merkittävin
pääjakso tunnistetuista sienten kokonais- ja aktiivipopulaatiosta. Sieni- ja
arkeoniyhteisöt eivät olleet niin monimuotoisia kuin bakteeriyhteisöt. Mikrobien
kokonaismäärä sekä sulfaatinpelkistäjien, ammoniakin hapettajien ja metanogeenien
määrä väheni syvemmälle mentäessä.
Typen kiertoon liittyviä tekijöitä syväbiosfäärissä ei vielä täysin ymmärretä. Yleisiä
typen kiertoon liittyviä geenejä, amoA:ta and narG:ta, havaittiin pienempiä
konsentraatioita kuin etukäteen odotettiin. Tässä tutkimuksessa löydettiin kuitenkin
Olkiluodon pohjavesistä typen kiertoon liittyviä mikrobeja, joita ei aikaisemmin ole
havaittu. Lisäksi saatiin uutta tietoa esimerkiksi ANME-2D-arkeonien toiminnasta
metaanin ja typen kierrossa.
Sulfaatinpelkistäjiä löydettiin kaikista Olkiluodon pohjavesikerroksista lukuun
ottamatta syvimmältä otettuja näytteitä. Yleisesti ottaen SRB yhteisöjä hallitsivat
Desulfobacterales-bakteerit. Korkein SRB yhteisön monimuotoisuusindeksi havaittiin
murtovedestä, joka oli otettu yli 415 metrin syvyydeltä. Tämä viittaa siitä, että
murtovedessä erilaisia SRB ryhmiä on yhtä suuria määriä ja mikään ryhmä ei hyödy
muita enemmän vallitsevista ympäristöolosuhteista. Tämä voi joutua mikrobiyhteisön
monimuotoisuudesta ja saatavilla olevasta eliöperäisestä hiilestä ja elektronin
vastaanottajista. Sekoittuneissa näytteissä OL-KR6/422m, OL-KR23/425m ja OLKR5/457m oli suuri määrä sulfaattia, sulfidia ja dsrB transkriptejä viitaten aktiiviseen
sulfaatin pelkistykseen.
Metanogeenien määrä oli vähäinen kaikissa syvyyksissä. Yleisimmin havaitut mcrA
sekvenssit kuuluivat Methanosarcinaceae-arkeoneihin, jotka ovat yleensä kykeneviä
hyödyntämään metanogeneesissä monia erilaisia pieniä hiilisubstraatteja kuten H2-
CO2:a, asetaattia, metanolia, metyyliaminia ja metyylisulfideja. On jopa mahdollista että
löydetyt Methanosarcinaceae mcrA sekvenssit kuuluvat äskettäin löydettyyn ANME-2D
ryhmään, joka voi hapettaa metaania anaerobisesti.
Näytepisteiden OL-KR13/360m and OL-KR6/422m mikrobiyhteisöt tutkittiin kahtena
eri vuotena, 2010 ja 2012 (OL-KR13/360m) sekä 2010 ja 2013 (OL-KR6/422m).
Näytepisteessä OL-KR13/360m mikrobiyhteisö muuttui merkittävästi näytteenottojen
välillä ja huomattavia muutoksia havaittiin myös hydrogeokemiassa. Myös
näytepisteessä OL-KR6/422m mikrobiprofiili muuttui näytteenottojen välillä, mutta
muutoksia hydrogeokemiassa ei havaittu. Epsilonproteobakteerit olivat hallitsevia
sekoittuneissa rakovyöhykkeissä ajanjaksolla 2009-2011. Epsilonproteobakteerit ovat
erityisesti rikin hapettajia ja tämä prosessi voi aktivoitua, kun sulfaattia ja metaania
sisältävät vesikerrokset sekoittuvat.
Taksonominen luokittelu suku- ja lajitasolla on usein mahdotonta, koska merkittävää
määrää luonnon mikro-organismeista ei ole viljelty tai ne eivät kasva viljelemällä. Tämä
havaittiin myös tämän tutkimuksen aineistossa, jossa merkittävää osaa mikrobeista ei
pystytty luokittelemaan alemmalle taksonomiselle tasolle. Olkiluodosta löydetyt
mikrobisekvenssit olivat kuitenkin usein samankaltaisia kuin syväbiosfääristä tai
syvämeren pohjasta löydettyjen mikrobien sekvenssit ja erityisesti tämä koski arkeoneja
ja sieniä.
Avainsanat: qPCR, sulfaatinpelkistäjä, pyrosekvensointi, mikrobiyhteisö, typen kierto,
metanogeeni.
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TABLE OF CONTENTS
TABLE OF CONTENTS .................................................................................................. 1
GLOSSARY..................................................................................................................... 5
PREFACE .......................................................................................................................7
1. INTRODUCTION ......................................................................................................... 9
1.1 Nitrogen cycle ...................................................................................................... 9
1.1.1 Nitrogen Fixation ................................................................................... 10
1.1.2 Nitrification ............................................................................................. 11
1.1.3 Denitrification ......................................................................................... 12
1.1.4 Dissimilatory nitrite reduction to ammonium (DNRA) ............................ 15
1.1.5 Anaerobic ammonium oxidation (ANAMMOX) ...................................... 16
1.2 Deep subsurface microbial carbon cycle ........................................................... 16
1.2.1 CO2 fixation ........................................................................................... 16
1.2.2 Calvin cycle ........................................................................................... 18
1.2.3 The reductive citric acid cycle................................................................ 19
1.2.4 Reductive acetyl-CoA pathway.............................................................. 20
1.2.5 3-Hydroxypropionate bicycle ................................................................ 21
1.2.6 Hydroxypropionate-hydroxybuturate cycle ............................................ 21
1.2.7 Dicarboxylate-hydroxybutyrate cycle ..................................................... 22
1.2.8 Aerobic methane oxidation .................................................................... 22
1.2.9 Anaerobic methane oxidation ................................................................ 25
1.2.10 Methanogens ....................................................................................... 27
1.2.11 Fermentation ....................................................................................... 27
1.3 Terminal electron acceptors and donors in deep subsurface ............................ 28
1.3.1 Sulphur metabolism ............................................................................... 29
1.3.2 Iron metabolism ..................................................................................... 31
1.3.3 Manganase metabolism ........................................................................ 34
1.4 Description of the site ...................................................................................... 36
2 MATERIALS AND METHODS ................................................................................... 39
2.1 Sampling ............................................................................................................ 39
2.2 Geochemistry..................................................................................................... 40
2.3 Nucleic acid isolation ......................................................................................... 40
2.4 Total Number of Cells (TNC) ............................................................................. 40
2.5 Amplification library preparation ........................................................................ 41
2.6 Sequence processing and analysis ................................................................... 41
2.6.1 Small subunit ribosomal (16S) RNA gene, archaea and bacteria ......... 41
2.6.2 Fungal ITS ............................................................................................. 42
2.6.3 dsrB and mcrA genes and transcripts ................................................... 42
2.6.4 Metabolic predictions ............................................................................. 43
2.6.5 Heatmap generation .............................................................................. 43
2.7 Real-time quantitative PCR (qPCR) .................................................................. 43
2
3 RESULTS ................................................................................................................... 45
3.1 Total Number of Cells (TNC) ............................................................................. 45
3.2 Number of sulphate reducers, ammonium oxidisers, denitrifiers and
methanogens determined by qPCR ................................................................ 46
3.3 Bacterial diversity ............................................................................................... 49
3.3.1 Overview of the dominant bacterial classes .......................................... 51
3.3.2 Proteobacteria ....................................................................................... 57
3.3.3 Other bacterial phyla ............................................................................. 58
3.3.4 Metabolic predictions ............................................................................. 61
3.3.5 The influence of sampling time and sampling depth ............................. 63
3.4 Archaeal diversity .............................................................................................. 65
3.4.1 Overview of the dominant archaeal taxa ............................................... 67
3.4.2 Metabolic predictions ............................................................................. 73
3.5 Fungal diversity .................................................................................................. 74
3.5.1 Overview of the dominant fungal taxa ................................................... 73
3.6 Sulphate reducers - diversity ............................................................................. 73
3.7 Methanogens - diversity ..................................................................................... 73
4 CONLUSIONS............................................................................................................ 79
5 REFERENCES ........................................................................................................... 85
APPENDIX A: Geochemical analysis methods. .......................................................... 111
APPENDIX B: Geochemical analysis results (B=baseline, nondisturbed), T=monitoring
sample) .................................................................................................................. 113
APPENDIX C: Taxonomic classification heatmap of the Proteobacteria .................... 115
APPENDIX D: Taxonomic classification heatmap of the Firmicutes ........................... 117
APPENDIX E: Taxonomic classification heatmap of the Actinobacteria sequence
reads obtained by high throughput sequencing of the total (DNA) and active
(RNA) bacterial communities presented at family-level. The colouring of the
heatmap as in App. C. ........................................................................................... 119
APPENDIX F: Taxonomic classification heatmap of the Bacteroidetes, Clorobi,
Tenericutes, Thermi and Chloroflexi ...................................................................... 121
APPENDIX G: Taxonomic classification heatmap of the Elusimicrobia, Nitrospirae,
Planctomycetes, Spirochatetes, Tenericutes and Verrucomicrobia sequence
reads obtained by high throughput sequencing of the total (DNA) and active
(RNA) bacterial communities presented at family-level. The colouring of the
heatmap as in App. C. ........................................................................................... 123
APPENDIX H: Taxonomic classification heatmap of the OD1, OP3, TM6, TM7 and
WS3 ....................................................................................................................... 125
APPENDIX I: Characteristics of the main bacterial taxa found from Olkiluoto
groundwater samples. ........................................................................................... 127
APPENDIX J: General statistics of bacterial 16S rDNA and 16S rRNA sequences
in the samples, and sample alpha diversity estimates. OTUs reported at 97%
clustering identity ................................................................................................... 129
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APPENDIX K: General statistics of archaeal 16S rDNA and 16S rRNA sequences
in the samples, and sample alpha diversity estimates. OTUs reported at 97%
clustering identity. .................................................................................................. 147
APPENDIX L: General sequence statistics of fungal ITS sequences in the
samples, and sample alpha diversity estimates. OTUs reported at 97%
clustering identity. .................................................................................................. 149
APPENDIX M: General statistics of dsrB sequences in the samples, and sample
alpha diversity estimates. OTUs reported at 97% clustering identity. .................... 151
APPENDIX N: General statistics of mcrA sequences in the samples, and sample
alpha diversity estimates. OTUs reported at 97% clustering identity. .................... 153
APPENDIX O. Pyrosequencing fact sheet. ................................................................. 155
APPENDIX P: Characteristics of the main characterized archaeal taxa found from
Olkiluoto groundwater samples. ............................................................................ 157
APPENDIX Q. Bacterial, Archaeal and Fungal taxa with classification ....................... 159
4
5
GLOSSARY
amoA
a gene involved in the oxidation of ammonia to hydroxylamine,
used as a marker gene for the detection of ammonia oxidation
bacteria
ANAMMOX
Anaerobic ammonium oxidation; under anoxic conditions,
anammox bacteria are able to gain energy by the formation of
nitrogen gas from nitrite and ammonium
ANME
ANaerobic Methane-oxidising archaea
AOA
Ammonia oxidising archaea
AOB
Ammonia oxidising bacteria
Assimilation
the conversion of absorbed nutrients into the substance of the
cell in constructive metabolism
bsl
below sea level
DAPI
4’,6-diamidino-2-phenylindole
Dissimilation
the metabolic breakdown of molecules into simpler ones,
resulting in a release of energy
DNA
a nucleic acid that contains the genetic instructions used in the
development and functioning of all known living organisms
DNRA
Dissimilatory Nitrite Reduction to Ammonium
dsrB
a fragment of a gene involved in sulphate reduction, used as a
marker gene for the detection of sulphate reducing bacteria
mcrA
a gene involved in the production of methane and used as a
marker gene fro the detection of methanogens
Methanotroph
a group of bacteria and archaea possessing a specialised
metabolism restricted to the utilization methane and methanol
Methylotrophs
a group of bacteria and archaea able to utilize as their sole
source of carbon and energy reduced carbon substrates with no
carbon-carbon bonds
narG
a gene involved in nitrate reduction, used as a marker gene for
the detection of nitrate to nitrite reducing bacteria
6
NOB
Nitrite oxidising bacteria
OTU
Operational taxonomic unit, concept used in hierarchial
classification when pre-defined groups are being compared.
Here, used to indicate groups of sequences or species, which
share a defined degree of similarity
PCR
polymerase chain reaction, amplification method for fragments
of DNA
qPCR
quantitative polymerase chain reaction
RNA
ribonucleic acid, the constituent of e.g. ribosomes and
messenger-RNA (mRNA)
TNC
Total number of cells
7
PREFACE
The work was carried out at VTT Technical Research Centre of Finland Oy.
The contact person at Posiva Oy was Tiina Lamminmäki and at VTT Merja Itävaara.
The research work at VTT was done by research scientists Malin Bomberg, Mari
Nyyssönen, Hanna Miettinen, Elina Sohlberg, Heikki Salavirta and Minna Vikman.
Laboratory technician Tarja Eriksson assisted with sampling and laboratory technician
Mirva Pyrhönen with the qPCR analysis.
8
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1. INTRODUCTION
The microbial communities of Olkiluoto groundwater from depths between 0 to 900 m
have been examined since the 1990s (Havemann et al., 1999). Microorganisms from deep
subsurface environments are often difficult and laborious to culture and characterize in
laboratory conditions. The culture-independent techniques and the development of high
through-put sequencing methods have given novel tools to characterize microbial groups
and their functions as well as estimate their numbers. Microbial communities in
Olkiluoto’s groundwater are described in several reports and publications (Bomberg and
Itävaara, 2013; Itävaara et al., 2008; Itävaara et al., 2011; Nyyssönen et al., 2012).
The aim of the work was to study the diversity of microbial communities in Olkiluoto
groundwater during the years 2009 and 2013. Especially microbial groups related nitrogen
cycle (ammonium oxidiser, denitrifying bacteria), sulphate reducers and methanogens are
studied.
1.1 Nitrogen cycle
Nitrogen fixation, the biocatalytic conversion of gaseous nitrogen to ammonium, is an
exclusive property of prokaryotes. The enzymes responsible for this reaction are
nitrogenases (Martinez-Romero, 2006). Nitrogen fixers are encountered in bacteria and in
some groups of archaea.
Nitrification, the biological oxidation of reduced forms of inorganic nitrogen to nitrite and
nitrate is catalysed by two physiological groups of bacteria. Ammonia-oxidising bacteria
gain energy from oxidation of ammonia to nitrite. Nitrite-oxidising bacteria thrive by
oxidising nitrite to nitrate (Bock and Wagner, 2006). Nitrite is usually found in trace
amounts in aerobic habitats and it only accumulates at low oxygen partial pressure.
Under anoxic or oxygen-limited conditions the nitrogen cycle contains two different
processes where nitrate is used as electron acceptor: dissimilatory nitrate reduction,
(denitrification) and dissimilatory nitrate reduction to ammonium (DNRA or respiratory
ammonification) (Shapleigh, 2006; Rütting et al., 2011). DNRA recycles nitrogen in a
bioavailable form instead of removing it from the ecosystem. In denitrification the initial
step is the reduction of nitrate to nitrite, in the next step nitrite is reduced to nitric oxide, a
gaseous nitrogen oxide. Once nitric oxide is produced, it is further reduced to nitrous oxide
and then to nitrogen gas (Shapleigh, 2006).
Anammox process is the anaerobic oxidation of ammonia to Nitrogen gas with nitrate as
the electron acceptor (Op den Camp et al., 2007). Anammox bioreactors are used to treat
wastewaters with high ammonium concentration at mesophilic temperatures (Op den Camp
et al., 2007). In marine environments anaerobic oxidation of ammonium coupled to nitrate
reduction, contributes substantially to N2 production in sediments (Thamdrup and
Dalsgaard, 2002).
D
N
RA
!
10
Figure 1. Microbial nitrogen cycle (modified from Kartal et al., 2011).
1.1.1 Nitrogen Fixation
Biological nitrogen fixation is the main source of nitrogen in soil, marine environments,
subtropical and tropical open ocean habitats and hydrothermal vent ecosystems. Nitrogen
fixers inhabit a wide range of exterior environments including soils, seas and the oceans
and interior environments including insects, cow rumen, human intestines and faeces
(Martinez-Romero, 2006). Nitrogen fixation has in recent years been shown to happen also
in suboxic or oxygen minimum zones of marine waters (Fernandez et al., 2011; Farnelid et
al., 2013).
The taxonomic distribution of nitrogen fixation in Archaea is limited to the Euryarchaeota
phylum (Dos Santos et al., 2012). Nitrogen fixation has been found among 6 phyla
(Actinobacteria, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes and Proteobacteria)
within the Bacteria domain. In addition to these, nitrogen fixation has been predicted to
occur in 7 different phyla by a computational approach (Dos Santos et al., 2012).
Many diazotrophs (nitrogen fixers) are associated with the roots of plants where they
exchange fixed nitrogen for the products of photosynthesis. In addition many
microorganisms fix nitrogen symbiotically by partnering with a host plant. Free-living
heterotrophic bacteria live in the soil and water and fix significant levels of nitrogen
without the direct interaction with other organisms. Examples of this type of nitrogenfixing bacteria include species of Azotobacter, Bacillus, Clostridium, and Klebsiella
(Wagner, 2012). Nitrogenase enzyme is extremely oxygen sensitive. This imposes
considerable physiological constraints on diazotrophs as there is an obligation to protect
the enzyme from oxygen damage. In most diazotrophs, a diverse array of physiological
strategies are used to provide protection from oxygen, including simple avoidance of
oxygen through anaerobic growth, consumption of excess oxygen by respiration, oxygen
diffusion barriers, or compartmentation of the enzyme spatially or temporally (Dixon and
Kahn, 2004).
Fixation of N2 to NH4+ by the nitrogenase enzyme requires input of ATP at a high
energetic cost (Martinez-Romero, 2006). However part of the cost is due to the fact that
11
nitrogenase is highly sensitive to oxygen and is irreversibly inactivated on contact with
molecular oxygen. For this reason nitrogenase needs to be protected from molecular
oxygen (Großkopf and LaRoche, 2012).
All known nitrogen fixers contain at least one of the three closely related sub-types of
nitrogenase: Nif, Vnf, and Anf. Despite differences in their metal content, these
nitrogenase sub-types are structurally, mechanistically, and phylogenetically related. Most
phylogenetic studies of nitrogen fixing organisms have used NifH and/or NifD sequences
as proxies to assess diversity (Dos Santos et al., 2012).
1.1.2 Nitrification
Initially, ammonia oxidation, the first step in the globally important process of
nitrification, was thought to be performed almost exclusively by bacteria and especially
within phylum Proteobacteria (β- and γ-Proteobacteria). Metagenomic studies, followed by
laboratory isolation, then demonstrated the potential for significant ammonia oxidation by
mesophilic crenarchaea (Prosser and Nicol, 2008).
Traditionally lithotrophic bacterial ammonia oxidisers (AOB) are characterised by the
prefix Nitroso- and the nitrite oxidisers (NOB) are characterised by the prefix Nitro- (Bock
and Wagner, 2006). AOB include Nitrosococcus species belonging into γ–subclass of
Proteobacteria. Other autotrophic AOB are classified below β–subclass of Proteobacteria.
This lineage encompasses the genera Nitrosomonas and Nitrosospira (Bock and Wagner,
2006).
NOB are a phylogenetically diverse functional group. Known representatives belong to the
phylum Nitrospira and Chloroflexi (genus Nitrolancetus; Sorokin et al., 2012) and
proteobacterial genera Nitrobacter (α-Proteobacteria), Nitrotoga (β-Proteobacteria),
Nitrooccus (γ-Proteobacteria) and Nitrospina (δ-Proteobacteria), which is recently
suggested to form a novel phylum Nitrospinae (Lücker et al., 2013). These slow-growing,
autotrophic bacteria use ammonia or nitrite oxidation as their sole source of energy.
Ammonia oxidation has also been found among heterotrophic bacteria and yeast.
Heterotrophic nitrification does not produce energy like autotrophic nitrification, but
consumes it (Boethe et al., 2007).
Ammonia oxidation is initiated by the enzyme ammonia monooxygenase (AMO), which
oxidises ammonia to hydroxylamine. The intermediate hydroxylamine is further oxidised to
nitrite by hydroxylamine oxidoreductase (HAO) (Bock and Wager, 2006). The AmoA
protein is assumed to contain the active site of AMO (Hyman and Arp, 1992). Both
ammonia oxidising archaea (AOA) and AOB contain the amoA gene encoding the alpha
subunit of the AMO. In studies of microbial community structure and abundance, different
primer sets for archaeal and bacterial amoA gene need to be used.
AOA have been found in recent years based on the findings of amo-like genes associated
with archaeal scaffolds from marine water and soil. Currently it is known that archaeal
ammonia monooxygenase gene sequences are ubiquitously distributed in the environment
and outnumber their bacterial counterparts in many habitats (Hatzenpichler, 2012). AOA
have also been found not only in fully aerated soils and oxic marine waters but also in
12
suboxic marine waters, sediments, and oxygen-depleted hotsprings (Erguder et al., 2009;
Schleper and Nicol, 2010). AOA can also be found over a wide range of pH, temperature,
salinity, and phosphate concentrations with some AOA being adapted to sulphidic
environments (Erguder et al., 2009; Schleper and Nicol, 2010).
AOA are now generally recognized to exert primary control over ammonia oxidation in
terrestrial, marine, and geothermal habitats. Pure culture, marine and terrestrial-based studies
indicate that AOA have unusually high affinity for ammonia (Schleper and Nicol, 2010;
Stahl and de la Torre, 2012) Initial comparative genomics and physiological studies have
revealed a novel, and as yet unresolved, primarily copper-based pathway for ammonia
oxidation, as AOA do not contain a homologue of the bacterial hydroxylamine
oxidoreductase (HAO), and respiration distinct from that of known ammonia-oxidizing
bacteria and possibly relevant to the production of atmospherically active nitrogen oxides.
Comparative studies also provide compelling evidence that the lineage of archaea with
which the AOA affiliate is sufficiently divergent to justify the creation of a novel phylum,
the Thaumarchaeota (Stahl and de la Torre, 2012).
Fernández-Guerra and Casamayour (2012) explored from over 300 isolation sources almost
6,000 amoA gene sequences. Phylogenetic richness was larger in AOA than in AOB, and
sediments contained the highest phylogenetic richness whereas marine plankton the lowest.
Freshwater ammonia oxidizers were phylogenetically richer than their marine counterparts.
AOA communities were more dissimilar to each other than those of AOB. The phylogeny of
amoA genes was largely congruent with the picture derived by 16 S rRNA genes analysis,
and therefore the habitat-phylogeny distribution patterns found for the amoA genes may
provide strong hints for the diversity (richness and evenness) of AOB and AOA in global
scale (Fernández-Guerra and Casamayor, 2012).
Bacterial AMO is a multifunctional enzyme, oxidizing methane, carbon monoxide and a
range of organic compounds (Hatzenpichler, 2012). Archaeal AMO that also belongs to the
family of copper-containing membrane-bound monooxygenases (CuMMO) has a wide
substrate range and functions. The presence of high abundance of a functional gene does
not always mean that the function is operating. Both in bacteria and archaea the gene
product may give alternative ecosystem functions to ammonia oxidation. Bacteria and
archaea are phylogenetically distant, with significant differences in cell physiology and
structure, and AOA appear to be smaller than AOB. Activities per cell may therefore differ
significantly (Prosser and Nicol, 2008).
1.1.3 Denitrification
Denitrification, the reduction of nitrate or nitrite to nitrous oxide or nitrogen gas is the major
mechanism by which fixed nitrogen returns to the atmosphere from soil and water. Oxidised
nitrogen compounds are used as electron acceptors for energy production. The ability to
denitrify has been found in microorganisms belonging to numerous groups of bacteria and
archaea (Philippot, 2002). In prokaryotes and in a few filamentous fungi, the reduction of
nitrate is a respiratory process. Reduction of nitrate is coupled to ATP synthesis via electron
transport chains. Denitrifiers can also respire with oxygen as the terminal electron acceptor
with only few exceptions (Shapleigh, 2006). Some prokaryotes contain partial denitrification
chains and metabolise nitrate into nitric oxide or nitrous oxide. Some of the isolated archaea
13
are denitrifiers and except for at least one (Ferroglobus placidus), capable of aerobic
respiration. Anaerobic F. placidus can couple Fe++ oxidation to nitrate reduction (Shapleigh,
2006). As few as 26 archaeal species are known to possess marker genes of denitrification at
the moment (Rusch, 2013).
Denitrification consists of four reaction steps by which nitrate (NO3-) is reduced into
nitrogen gas by the metalloenzymes: nitrate reductase (Nar), nitrite reductase (Nir), nitric
oxide reductase (Nor) and nitrous oxide reductase (Nos) (Figure 4). These enzymes are
usually induced sequentially under anaerobic conditions.
Bacteria can express two types of dissimilatory nitrate reductase, which differ in their
location: a membrane-bound (Nar) and a periplasmic-bound (Nap) nitrate reductase. The
membrane-bound nitrate reductase is composed of three subunits, of which α- subunit
encoded by narG is the catalycic part. The copy amount of narG varies between one and
three in genomes (Philippot and Hallin, 2005). NarG phylogeny appears to correlate well
with the 16S rRNA phylogenetic tree. The near congruence of 16S rRNA and NarG
nitrate-reducing bacteria and archaea suggests that the gene histories of the NarG subunits
represent the phylogeny of the organisms. Both bacterial and archaeal Nar are membrane
associated protein complexes and show a high percentage of sequence similarity (Cabello
et al., 2004; Martínez-Espinosa et al., 2007).
1
NO3Nar
narG
napA
Nap
nirK
Cu-Nir
norZ
qNor
nosZ
Nos
NO2-
2
Cd-Nir
nirS
NO
3
Nor
norB
N2O
4
N2
= genes encoding
enzymes
= liquid
= enzymes
= gas
Figure 2. Denitrification pathway. 1) Reduction of soluble nitrate to nitrite is catalysed either
by a membrane-bound (Nar) or a periplasmic nitrate reductase (Nap). 2) Reduction of
soluble nitrite to nitric oxide gas is catalysed by either a copper (Cu-Nir) or a cytochrome cd1
nitrite reductase (Cd-Nir). 3) Reduction of NO to N2O is catalyzed by either a two-component
type (Nor) or single-component type (qNor) of nitric oxide reductase. Lastly, 4) reduction of
nitrous oxide to dinitrogen is catalysed by the nitrous oxide reductase (Nos). (Modified from
Philippot and Hallin, 2005).
14
The second nitrate reductase is periplasmic heterodimer encoded by the napA and napB
genes. NapA is the large subunit containing a molybdopterin cofactor catalytic subunit.
The genes encoding the periplasmic nitrate reductase have been identified only in some
Alfa-, Beta- and Gammaproteobacteria (Philippot, 2002; Van Spanning et al., 2005). No
clear correlation with the 16S rDNA phylogenic tree was observed for NapA (Philippot,
2002).
Two nitrite reductases that are evolutionary unrelated and different in terms of structure
and the prosthetic metal, catalyse the reduction of nitrite to nitric oxide: a copper- (CuNir)
and a cytochrome cd1-nitrite reductase (cd1Nir). The nirK gene and the nirS gene encode
the copper- and cd1 nitrite reductases, respectively (Philippot, 2002). The two forms are
functionally equivalent, but have not been detected within the same organism and show
different environmental distributions (Zumft, 1997). Both types of respiratory nitrite
reductase genes, nirK and nirS, have been found in archaea (Jones et al., 2008).
Comparison of the nirS clusters between bacterial genomes has revealed the highest degree
of genomic plasticity of the denitrifying clusters. Differential gains and losses of genes are
found even between closely related species. Within the NirS group, there is no clear
correlation with the 16S rDNA phylogenic tree (Philippot, 2002). Characterization of
denitrifying communities by the genes encoding the cytochrome cd1 and copper-containing
nitrite reductases (nirS and nirK, respectively) is the most commonly used approach
(Philippot and Hallin, 2005).
Nitric oxide (NO) is a radical, which reacts unspecifically with many other molecules.
Therefore, its accumulation into cells needs to be prevented. Nitric oxide reductase
(NOR) is a membrane-integrated enzyme belonging to the heme-copper oxidase
superfamily. The active site is a binuclear center with a heme group and a non-heme metal
ion, which is iron (Blomberg and Siegbahn, 2012). There are two types of bacterial nitric
oxide reductases: one is a cytochrome bc complex (cNOR) that can use a c-type
cytochrome as an electron donor also referred as short-chain respiratory Nor. The second
type lacks a cytochrome c component and accepts electrons from quinols and is called
qNOR (Hendriks et al., 2000) or long-chain respiratory Nor. The norB gene and the norZ
gene encode the cNOR- and qNOR nitric oxide reductases, respectively. The nitric oxide
reductase of the archaeon P. aerophilum is a qNor-type protein with menaquinol as
electron donor and also other archaeal nor genes appear to be encoded by qNor (Cabello et
al., 2004).
The multicopper enzyme nitrous oxide reductase (NOS) is composed of two identical
subunits and contains eight copper ions. Typically, the nos genes are arranged in three
transcriptional units consisting of the nosZ gene, which encodes the catalytic subunit, the
nosR and nosDFYL genes (Phillippot, 2002). NOS is a periplasmic enzyme in gramnegative bacteria and a membrane bound in gram-positive such as Bacillus azotoformas
(Shapleigh, 2006). Archaeal NOS is membrane bound and (in Pyrobaculum aerophilum)
receives electrons from menaquinol (Kraft et al., 2011). An unknown percentage of
denitrifying bacteria lack the genes encoding nitrous oxide reductase and will emit N2O as
an end product in denitrifying conditions. On the other hand the presence of nitrous oxide
reductase can be identified in several bacteria that are not complete denitrifiers because
many organisms can grow with nitrous oxide as sole terminal oxidant (Shapleigh, 2006).
15
A novel archaeal nosZ-targeted primer pair that showed sensitivity and phylogenetic
specificity, performed reliably in real-time PCR. The primer pair was considered suitable
for the full range of applications targeting archaeal denitrifiers. When used in
environments of very low archaeal abundance, some bacterial byproducts are possible,
though (Rusch, 2013).
1.1.4 Dissimilatory nitrite reduction to ammonium (DNRA)
In contrast to denitrification, dissimilatory nitrate reduction to ammonium (DNRA) is
assumed to occur when nitrate in comparison to organic carbon is limiting (Cole and
Brown, 1980). In DNRA nitrate and nitrite are reduced to ammonium and nine or six
electrons are transferred, respectively. The reduction of nitrate to nitrite is assumed to
mostly being catalysed by the periplasmic nitrate reductase complex Nap (Kraft et al.,
2011). However, a membrane-bound nitrate reductase, Nar may also be present in the same
organism. The entire reduction of nitrite to ammonium is catalysed by NrfA, a pentaheme
cytochrome c nitrite reductase without the release of any intermediate (Einsle et al., 2002).
There are some indications that N2O is also released as a byproduct of DNRA and not all
N2O originates from denitrification (and detoxification) as generally assumed (Kraft et al.,
2011) or that some bacteria may carry both nrfA and dissimilatory nitrite reductase (nirK)
genes on the same genome (Giblin et al., 2013). The ability to carry out DNRA is
phylogenetically widespread. DNRA is performed both by heterotrophic organisms, which
use organic carbon as the electron donor (fermentative DNRA), and by
chemolithoautotrophic organisms, which use nitrate to oxidize sulfide or other reduced
inorganic substrates. The functional gene nrfA occurs in diverse groups of bacteria. It has
been found in Gamma-, Delta- and Epsilonproteobacteria (Smith et al., 2007) and in
members of the Bacteroides, Planctomycetes and Firmicutes (Mohan et al., 2004). Many
sulphate reducing Deltaproteobacteria are able to perform DNRA in the presence of nitrate
(Pereira et al., 1996) although there has been recent evidence that sulphate reduction is
preferred over DNRA if both electron acceptors are present (Marietou et al., 2009). The
key organisms carrying out DNRA in aquatic and terrestrial environments remain to be
identified. Furthermore the ecological niche of DNRA in comparison to denitrification and
the conditions that favour the one or the other nitrate respiration pathway have to be
determined (Kraft et al., 2011). Nevertheless slurry incubations with 15N-nitrate showed
that DNRA occurs under oxic conditions and is not inhibited by oxygen (Morley and
Baggs, 2010).
DNRA may not be restricted to the bacteria carrying the nrfA genes. A metal-reducing
bacterium, Shewanella oneidensis MR-1, carries octaheme tetrathionate reductase (Otr),
which also catalyses nitrite reduction to ammonium (Atkinson et al., 2007). In addition,
octaheme cytochrome c nitrite reductase (Onr) was found in Thioalkalivibrio
nitratireducens, a nitrate-reducing obligate chemolithoautotrophic sulphur oxidizing
bacterium (Tikhonova et al., 2006). Homologues genes encoding Onr are found in various
bacteria, but their physiological involvements in nitrite respiration are unknown. Future
studies are required to identify the enzymes and genes involved in the
chemolithoautotrophic DNRA pathway (Giblin et al., 2013).
16
There are applications of nrfA used as a marker gene and new primers are designed (Kraft
et al., 2011; Giacomucci et al., 2012). In future studies on nitrate reduction nrfA should
also be addressed more commonly as the significance of DNRA in different environments
may be higher than previously assumed (Kraft et al., 2011).
1.1.5 Anaerobic ammonium oxidation (ANAMMOX)
Under anoxic conditions, anammox bacteria are able to gain energy by the formation of
nitrogen gas from nitrite and ammonium (Strous et al., 1999). Several bacteria that were
able to perform the anammox pathway have been characterized. They all belong to the
order of Planctomycetales (Kraft et al., 2011). They form a monophyletic order branching
off deep inside the planctomycete lineage and have a large evolutionary distance among
the genera described so far: Candidatus Brocadia, Kuenenia, Scalindua, Anammoxoglobus
and Jettenia. They are slow-growing organisms with a doubling time of eleven days up to
three weeks (Strous et al., 1999). Anammox bacteria have been detected in almost every
type of aquatic habitat that contains oxygen-depleted zones, including marine and
freshwater sediments, sea ice, and wastewater treatment plants. The chemolithoautotrophic
anammox pathway is advantageous in oxygen-depleted environments that are limited in
organic substrate. Furthermore, anammox bacteria depend on the presence of ammonium
and nitrite. (Kraft et al., 2011).
Next to known enzymes for nitrate reduction (Nar), nitrite reduction (Nir), and the
oxidation of hydrazine to dinitrogen by hydroxylamine oxidoreductase (HAO), novel
enzymes were suggested for anammox process. Hydrazine hydrolase (HH) produces
hydrazine from ammonium and nitric oxide (Strous et al., 2006). Shimamura et al. (2007)
showed that next to expressing high amounts of the HAO enzyme anammox bacteria also
express an enzyme that oxidizes hydrazine but not hydroxylamine. It was named
hydrazine-oxidizing enzyme (HZO). Since both anammox HAO and HZO oxidize
hydrazine, are similar in their gene sequences, and can be differentiated from hao genes of
aerobic ammonium-oxidizing bacteria, they are mostly referred to as HZO or HAO/HZO.
Amplification of 16S rRNA genes is usually the first step for the identification of
anammox bacteria in the environment. Next to the amplification of 16S rRNA sequences,
the hao/hzo gene is used as an additional genetic marker for anammox bacteria (Kraft et
al., 2011; Li et al., 2010). When both markers were studied, a greater phylogenetic
diversity was found for hao/hzo genes compared to 16S rRNA genes (Li et al., 2010).
There is considerable variety among the hao/hzo genes (Kraft et al., 2011).
1.2 Deep subsurface microbial carbon cycle
1.2.1 CO2 fixation
Autotrophs are organisms that can grow using carbon dioxide (CO2) as their sole source of
carbon. Among them are plants, algae, cyanobacteria, purple and green bacteria, and also
some bacteria and archaea that do not obtain energy from light. Autotrophs generate the
biomass on which all other organisms thrive (Thauer, 2007). At the moment six
mechanisms that assimilate CO2 into cellular material have been identified: i) Calvin cycle
ii) Reductive citric acid cycle iii) Reductive acetyl-coenzyme A pathway iv) 3-
17
Hydroxypropionate bicycle v) Hydroxypropionate-hydroxybutyrate cycle vi)
Dicarboxylate-hydroxybutyrate cycle (Berg et al., 2010a) (Figure 3). The observed
diversity in the pathways reflects the variety of the organisms and the ecological niches
existing in nature (Berg, 2011).
In order to fix one molecule of CO2 (oxidation state of +4) to the level of cell carbon
(oxidation state about 0), four electrons are required (Fuchs, 1999). An input of energy is
also required for the reductive conversion of CO2 to cell carbon and is provided by ATP
(Berg, 2011). Anaerobes often use low-potential electron donors like reduced ferredoxin
for CO2 fixation (Berg, 2010b), whereas aerobes usually rely on NAD(P)H (nicotinamide
adenine dinucleotide phosphate) as a reductant (Berg, 2011). A carboxylating enzyme links
either CO2 or HCO3− with an organic acceptor molecule, which must be regenerated in the
following steps of the pathway. The bicarbonate concentration under slightly alkaline
conditions (e.g., in marine water) is much higher than the concentration of dissolved CO2,
(the apparent acid dissociation association constant of [HCO3−/ CO2] is 6.3) and the usage
of bicarbonate instead of CO2 may be advantageous (Zarzycki et al., 2009) The solubility
of CO2 is also affected by the salt concentration and temperature (Berg, 2011).
18
The pathway of acetyl-CoA assimilation to
pyruvate, phosphoenolpyruvate,
and oxaloacetate is shown as well.
Acetol-CoA
2
1
CO2
CO2
Acetyl-CoA
CO2
Puryvate
RuBisCO
Ribulose-1,5-biphosphate
(RuBP)
Acetoacetyl-CoA
Phosphoenolpuryvate
3-phosphoglycerate
(PGA)
ATP
ATP
PRK
ADP
ADP
NADPH
Glyceraldehyde
3-phosphate
ATP
Oxaloacetate
H2O
Isocitrate
Malate
4-hydroxybutyryl-CoA
CO2
Fumarate
ATP
2-oxoglutarate
CO2
NADP
Succinate
4-hydroxybutyrate
NADPH
Succinic semialdehyde
Succinyl-CoA
NADPH
Acetyl-CoA
CO2
4
CO2
Formate
Acetyl-CoA
Malonyl-CoA
CODH/ACS
Acetoacetyl-CoA
ATP
3-hydroxybutyryl-CoA
CO2
3-hydroxypropionate
Malyl-CoA
CO2
H2O
Crotonyl-CoA
H2O
3-hydroxypropionyl-CoA
Methene-H4folate
NAD+
Glyoxylate
Malonate semialdehyde
10-formyl-H4folate
Methenyl-H4folate
H2O
NADH
CoA
CO2
Citrate
Crotonyl-CoA
ATP
3
3-hydroxybutyryl-CoA
CO2
1,3-diphosphoglycerate
Ribulose-5-phosphate
NAD+
Oxaloacetate
Malate
NADH
4-hydroxybutyryl-CoA
ATP
Acrylyl-CoA
Methyl-H4folate
Succinate
Propionyl-CoA
Methyl-CFeSP
CO
CO2
4-hydroxybutyrate
NADPH
Succinic semialdehyde
Methylmalonyl-CoA
Succinyl-CoA
27/01/15
CoA
CODH/ACS
Acetyl-CoA
ATP
CoA
NADPH
Figure 3. Outline of all the six CO2 fixing pathways. 1) Calvin cycle, with the key enzymes
RubisCO and PRK. 2) Outline of two pathway i.e. reductive TCA cycle (shown in black)
and dicarboxylate/4-hydroxybutyrate cycle (shown in purple). 3) Reductive acetyl-CoA
pathway with the key enzyme CODH/ACS. 4) Outline of the two pathway i.e. 3hydroxypropionate pathway (shown in black) and 3-hydroxypropionate/4-hydroxybutyrate
cycle (shown in blue). (Modified from Saini et al., 2011).
1.2.2 Calvin cycle
Calvin or Calvin-Benson-Bassham or Reductive pentose phosphate cycle was first
recognised about 50 years ago. The cycle operates in plants, algae and cyanobacteria,
which all perform oxygenic photosynthesis. In addition Calvin cycle is used by autotrophic
proteobacteria, some of which do not tolerate oxygen (Thauer, 2007). It was also shown for
Sulfobacillus spp., iron and sulphur-oxidizing members of the firmicutes (Caldwell et al.,
2007), some mycobacteria (Lee et al. 2009), and green non-sulphur bacteria of the genus
Oscillochloris (phylum Chloroflexi) (Ivanovsky et al., 1999).
In Calvin cycle CO2 reacts with the five-carbon sugar ribulose 1,5-bisphosphate to yield
two carboxylic acids, 3-phosphoglycerate, from which the sugar is regenerated. There are
two key enzymes, ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO) and
ribulose 5-phosphatate kinase (phosphoribulokinase). In addition, the sedoheptulose
bisphosphatase reaction can also be regarded as specific for the cycle. The cycle requires
19
nine ATP equivalents and six NADPHs for the synthesis of one glyceraldehyde-3phosphate molecule (Berg, 2011).
Four different forms of RubisCOs are know, all varying in structure, catalytic properties
and oxygen sensitivity (Tabita et al., 2008). Form I is the dominant type of RubisCO
occurring in plants as well as in photo- and chemoautotrophic bacteria (Selesi et al., 2007).
Form II RubisCO has poor catalytic characteristics, only functioning well at low oxygen
and high CO2 concentrations. Form III can be found in archaea, but is not known to
participate in autotrophic CO2 fixation via Calvin cycle and its function remains unknown
(Fuchs, 2011). Form IV is termed “RubisCO-like” protein as it does not catalyse the CO2
fixation reaction (Badger and Bek, 2008). RubisCO form I and II have frequently been
used as functional markers to investigate the diversity of autotrophic microorganism
(Watson and Tabita, 1997; Kellermann et al., 2012).
1.2.3 The reductive citric acid cycle
The reductive citric acid cycle (or reductive tricarboxylic acid [rTCA] cycle) was first
proposed for the green sulphur bacterium Chlorobium limicola (Chlorobi) (Evans et al.,
1966). Later it was found in anaerobic or microaerobic members of various other phyla
such as Aquificae, Proteobacteria (especially of the delta and epsilon subdivisions) and
Nitrospirae (e.g., Nitrospira and Leptospirillum) (Berg, 2011). This cycle is mainly found
in anaerobes or in organisms that tolerate oxygen only at levels below those found in air
(microaerophiles) (Thauer, 2007). There are some strains that use the cycle under aerobic
conditions as well. Hydrogenobacter thermophilus (Aquificae) encodes two different
isoforms of 2-oxoglutarate: ferrodoxin oxidoreductase with different sensitivities to
molecular oxygen (Yamamoto et al. 2006). Under oxic conditions, a five sub-unit form of
the enzyme that is O2 tolerant is preferentially synthetized. However, its robustness comes
at the expense of a > 5-times-lower specific activity (Yamamoto et al., 2010).
The reductive citric acid cycle reverses the reactions of the oxidative citric acid cycle
(Krebs cycle) and forms acetyl-CoA from two CO2-molecules. Acetyl-CoA is reductively
carboxylated to pyruvate, from which all other central metabolites can be formed, i.e.
pyruvate/phosphoenolpyruvate (PEP), oxaloacetate, and 2-oxoglutarate. Most of the
enzymes of the two pathways are shared, with the exception of three key enzymes that
allow the cycle to run in reverse: ATP citrate lyase, 2-oxoglutarate: ferredoxin
oxidoreductase, and fumarate reductase (Hügler et al., 2005). The reductive citric acid
cycle is generally considered the most energy-efficient CO2 fixation pathway (Erb, 2011).
The cycle requires (at least in Chlorobium) only two ATP equivalents to form pyruvate
(Berg, 2011).
In addition of different isoforms of 2-oxoglutarate: ferrodoxin oxidoreductase also
fumarate reductase exists in several forms: thiol dependent (in methanogens with an
incomplete reductive citric acid cycle), NADH dependent or electron donor unknown. ATP
citrate lyase may be either one-enzyme or two-enzyme systems comprising a subunit that
synthesizes and cleaves citryl-CoA (Fuchs, 2011).
20
1.2.4 Reductive acetyl-CoA pathway
Reductive acetyl-CoA pathway is the only linear (noncyclic) pathway for CO2 fixation.
The pathway is linear because acetyl-CoA, the primary CO2 fixation product, is formed
from the direct but independent reduction of two CO2 molecules: one to the level of a
carbonyl group, the other to the level of a methyl group. Therefore regeneration of a
primary CO2 acceptor molecule is not required (Alber, 2009). The enzyme bond carbonyl
group is reduced of one molecule of CO2 catalysed by acetyl-CoA synthase. The methyl
group is derived from a three-step reduction of the second molecule of CO2. In prokaryotes
this key enzyme with the core as carbon monoxide dehydrogenase (CODH) coupled with
acetyl-CoA synthase (ACS) with metallocofactor has dual active site (Berg et al., 2010a).
CO dehydrogenase/acetyl-CoA synthase has common roots in all prokaryotes using this
pathway (Berg, 2011). The pathway is found in broad range of phylogenetic classes and
preferred by prokaryotes living close to the thermodynamic limit, like acetogenic bacteria
and methanogenic archaea. Methanogens and acetogens use the reductive acetyl-CoA
pathway, not only for CO2 fixation but also for energy conservation via generation of an
electrochemical gradient (Berg 2011; Ragsdale and Pierce, 2008). Acetogens generate
acetate during autotrophic growth or fermentation, whereas methanogens withdraw
methyltetrahydromethanopterine from the pathway and reduce it to methane. The reductive
acetyl-CoA pathway functions also in anaerobic ammonia-oxidizing planctomycetes,
sulphate-reducing bacteria (Desulfobacterium sp., Deltaproteobacteria) and in autotrophic
Archaeoglobales (Euryarchaeota) growing by means of anaerobic respiration (Berg 2011).
The reductive acetyl-CoA pathway can also be used for the assimilation of a variety of C1
compounds like CO, formaldehyde, methanol, methylamine, methylmercaptane, and
methyl groups of aromatic O-methyl ethers/esters. As the CO2 reduction to acetate through
the pathway does not consume ATP, it can be exploited for steering electrons produced
during fermentations to CO2 (acetogenesis) (Berg, 2011). Acetoclastic methanogenic
archaea disproportionate acetate through the pathway into CH4 plus CO2 (Berg et al.,
2010a), and hydrogenotrophic methanogens use its methyl branch for methane formation
(accompanied with energy production), in addition to autotrophic CO2 fixation (Berg,
2011).
The reductive acetyl-CoA pathway functions in psychrophiles as well as
hyperthermophiles (Berg, 2011; Takai et al., 2008). Productive utilization of the pathway
relies on a set of oxygen sensitive metalloenzymes to synthesize acetyl-CoA (Jeoung et al.,
2014). Part of the organisms applying this pathway has been isolated from environments
with fluctuating oxygen concentrations like soils. There are different ways in
microorganisms to deal with oxidative stress the synthesis of enzymes of oxidative
response (catalase, peroxidase, etc.), switching to other electron acceptors, and symbiotic
relationships with O2-consuming partners (Drake et al., 2008). This, however, does not
render oxygen-tolerant organisms into true aerobes (Berg, 2011). The acetyl-CoA pathway
has a high requirement for metals (Mo or W, Co, Ni, and Fe), which are water soluble
preferentially in the reduced oxidation state (i.e., under anoxic conditions). In addition the
pathway exploits broadly coenzymes such as tetrahydropterin and cobalamin (Berg, 2011).
These requirements for metals, cofactors, anaerobiosis and substrates with low reducing
21
potentials such as H2 or CO restrict the reductive acetyl-CoA pathway to a limited set of
anoxic niches, despite its high energetic effiency (Berg et al., 2010a).
1.2.5 3-Hydroxypropionate bicycle
The 3-hydroxypropionate bicycle or Fuchs-Holo bicycle functions in the green non-sulfur
phototrophs of the Chloroflexaceae family, which grow preferentially under
photoheterotrophic conditions. The only autotrophic representative of this family found so
far is Chloroflexus aurantiacus, in which the bicycle was discovered (Berg, 2011). The
bicycle has a rather limited occurrence (Zarzycki et al., 2009).
Each turn of the first cycle results in the net fixation of two molecules of bicarbonate into
one molecule of glyoxylate. The pathway starts from acetyl-CoA, and conventional ATPand biotin-dependant acetyl-CoA and propionyl-CoA carboxylases act as carboxylating
enzymes (Fuchs, 2011). In the first glyoxylate synthesis cycle, acetyl-CoA is converted
through 3-hydroxypropionate, propionyl-CoA, succinyl-CoA to (S)-malyl-CoA. Finally
(S)-Malyl-CoA cleavage regenerates the starting molecule acetyl-CoA and releases
glyoxylate as a first carbon fixation product (Berg, 2011). Acetyl-CoA is reused as a
starting molecule while glyoxylate combine with propionyl-CoA and results in the
formation of acetyl-CoA and pyruvate (Zarzycki et al., 2009). Acetyl-CoA can serve as
starter for another round, and glyoxylate is assimilated in a second cycle. The whole
bicyclic pathway results in pyruvate formation from three molecules of bicarbonate (Fuchs,
2011).
Even the energy cost of the bicycle is high: it requires seven ATP equivalents for the
synthesis of pyruvate (Zarzycki et al., 2009), its key carboxylase(s), biotin-dependent
acetyl-CoA/propionyl-CoA carboxylase, is virtually irreversible and uses bicarbonate as an
active inorganic carbon species. This may be especially advantageous under neutrophilic
and alkaliphilic conditions (Berg, 2011). 3-hydroxypropionate bicycle allows coassimilating trace amounts of organic compounds like acetate, propionate and succinate,
even under oxic conditions as no enzyme of the cycle is oxygen sensitive, an ability that
may be advantageous in oligotrophic aquatic habitats (Zarzycki et al., 2009) This makes
the pathway especially suitable for mixotrophy.
1.2.6 Hydroxypropionate-hydroxybuturate cycle
The hydroxypropionate-hydroxybuturate cycle occurs in aerobic Crenarchaeota
(Sulfolobales and possibly in marine Crenearchaeota group I) (Berg et al., 2007). The cycle
is also present in facultative anaerobic and even strictly anaerobic Sulfolobales species
(Berg et al. 2010b).
In the hydroxypropionate-hydroxybuturate cycle, one molecule of acetyl-CoA is formed
from two molecules of bicarbonate. The key carboxylating enzyme is the bi-functional
biotin-dependent acetyl-CoA-propionyl-CoA carboxylase (Berg et al., 2010a).
Characteristic enzyme for the cycle is the 4-hydroxybutyryl-CoA dehydratase, which
seems to tolerate oxygen (Berg et al., 2010b). The hydroxypropionate–hydroxybutyrate
cycle can be divided into two parts. The first transforms acetyl-CoA and two bicarbonate
molecules through 3-hydroxypropionate to succinyl-CoA, and the second converts
22
succinyl-CoA through 4-hydroxybutyrate to two acetyl-CoA molecules (Berg et al. 2010a).
The Acetyl CoA carboxylase (ACCase) represents one of the key enzymes responsible for
the CO2 fixation in this metabolic pathway. The ACCase has three subunits: biotin
carboxylase (BC) encoded by the accC gene, biotin carboxyl carrier protein encoded by the
accB gene and carboxytranferase encoded by the pccB gene (Hügler et al., 2003). The
enzyme ACCase is present in a wide range of organisms and is especially known to be
involved in fatty acids biosynthesis (Moss and Lane, 1971). Since archaeal fatty acids are
different from those of bacteria, the presence of ACCase is generally taken as an indication
of carbon fixation through the hydroxypropionate pathway in autotrophic crenarchaeota
(Hügler et al., 2003).
In terms of ATP costs, the cycle is expensive: in the synthesis of one pyruvate nine ATP
equivalents are used and three molecules of pyrophosphate are generated (Berg, 2011).
1.2.7 Dicarboxylate-hydroxybutyrate cycle
Dicarboxylate-hydroxybutyrate cycle occurs in the anaerobic or microaerobic autotrophic
members of the crenarchaeal orders Thermoproteales and Desulfurococcales (Huber et al.,
2008; Berg et al., 2010b). The cycle can be divided into two parts: in the first part, acetylCoA, CO2 and bicarbonate are transformed through C4 dicarboxylic acids to succinyl-CoA,
and in the second part, succinyl-CoA is converted through 4-hydroxybutyrate into two
molecules of acetyl-CoA. One acetyl-CoA can be used for biosynthesis and the second
serves as a CO2 acceptor for the next round of the cycle. 4-Hydroxybutyryl-CoA
dehydratase is the key enzyme in the dicarboxylate–hydroxybutyrate cycle (Berg et al.,
2011).
The active CO2 species in the dicarboxylate–hydroxybutyrate cycle are CO2 as the cosubstrate for pyruvate synthase and bicarbonate (HCO3–) as the co-substrate for
phosphoenolpyruvate carboxylase. Pyruvate formation in this cycle requires five ATP
equivalents, and one energy-rich pyrophosphate is formed. This cycle is less energy
consuming than the Calvin cycle as it consumes seven ATP equivalents per formed
pyruvate (Berg et al., 2011; Huber et al., 2008).
1.2.8 Aerobic methane oxidation
Methanotrophs are a group of bacteria and archaea possessing a highly specialised
metabolism restricted to the utilization methane and methanol (Bowman, 2006).
Methanotrophs are a subset of bacteria and archaea known as methylotrophs.
Methylotrophs are able to utilize as their sole source of carbon and energy reduced carbon
substrates with no carbon-carbon bonds (Lidstrom, 2006). Methanotrophs are obligately
methylotrophic and do not have the ability to grow on organic compounds possessing
carbon-carbon bonds. Few strains however, can utilize methylamine and narrow selection
of other C1 compounds (Bowman, 2006). Their major physiological role is participation in
the methane cycle and supplying C1 intermediates and various metabolites to other
members of microbial communities in extreme ecosystems (Trotsenko and Khmelenina,
2002).
Methanotrophs are ubiquitous in nature and well adapted to high or low temperature, pH
and salinity (Trotsenko and Khmelenina, 2002). Methanotrophs typically occur at the
23
aerobic/anaerobic interface of wet environments (Trotsenko and Khmelenina, 2005).
Habitats in which methanotrophs are common include rice paddies; muddy soils of
swamps and marshes; river, pond and lake surface sediments; meadow and deciduous
forest soils; activated sewage sludge; some peat bogs; coal mine surfaces and drainage
waters (Bowman, 2006).
Under aerobic conditions methanotrophs are divided into three major categories: Type I,
Type II and TypeX (Trotsenko and Khmelenina, 2005). Type I methanotrophs belong to
gamma-subdivision of Proteobacteria and family Methylococcaceae, include e.g. genera
Methylomonas, Methylobacter, Methylomicrobium, Methylosphaera, Methylosarcina,
Methylocaldum, Methylothermus, Methylosoma and Methylohabius. They assimilate
formaldehyde produced from the oxidation of methane or methanol via the ribulose
monophosphate (RuMP) pathway (Trotsenko and Khmelenina, 2005; McDonald et al.,
2008) (Figure 4). Type II methanotrophs belong to alfa-subdivision of the Proteobacteria
and family Methylocystaceae and include four validated genera: Methylosinus,
Methylocystis, Methylocella and Methylocapsa. They assimilate formaldehyde via the
serine pathway. Type X methanotrophs include some intermediate genera: Methylococcus
and Methylocaldum from Gammaproteobacteria, that pose parts of both the RuMP and the
serine pathways (Trotsenko and Khmelenina, 2005).
Two aerobic filamentous methane oxidisers have been described, Crenothrix polyspora
(Stoecker et al., 2006) and Clonothrix fusca (Vigliotta et al., 2007) that are
Gammaproteobacteria and closely related to type I methanotrophs. In addition it has been
found that also aerobic species belonging to phylum Verrucomicrobia contain obligate
methane oxidising species that are thermoacidophilic (den Camp et al., 2009). The new
genera including Methylocella and Methylohalobius do not fit very well in type I, II and X
methanotroph generalisations. The terms type I and type II methanotrophs have become
more as synonyms for Gamma- and Alphaproteobacteria type methanotrophs respectively
(den Camp et al., 2009).
24
Type I methanotrophs
sMMO
O2 H2O
NADH+H+
NAD+
Methane
CytCox
CytCred
FADH
X
FDH
XH2
NAD+
NADH+H+
Formic
acid
Formaldehyde
Methanol
CytCred
RuMP
pathway
MDH
Carbon
dioxide
CytCox
O2 H2O
Serine pathway
pMMO
Type II methanotrophs
CytC
= cytochrome c
FADH = formaldehyde
dehydrogenase
FDH
= formate
dehydrogenase
pMMO
membrane bound methane
= monooxygenase
MDH
= methanol
dehydrogenase
sMMO
= soluble methane
monooxygenase
Figure 4. Pathways for the oxidation of methane and assimilation of formaldehyde
(modified from Hanson and Hanson, 1996).
Methanotrophic bacteria oxidize methane to methanol in the first step of their metabolic
pathway. Two forms of methane monooxygenase (MMO) enzymes catalyse this reaction:
soluble MMO (sMMO) and membrane-bound or particulate MMO (pMMO) (Hakemian
and Rosenzweig, 2007). In dissimilatory methane oxidation, MMO oxidases methane to
methanol and then methanol is further oxidised to formaldehyde, which methanotrophs use
for cellular carbon. Excess formaldehyde is oxidised to CO2 via formate. The pathway also
provides cells with reducing equivalents and drives electron transport for generation of
ATP (Bowman, 2006).
Particulate MMO is expressed in practically all methanotrophs except for genus
Methylocella (Murrel et al., 2000; Theisen et al., 2005). Some methanotrophs can form a
soluble MMO, which is distributed irregularly amongst the methanotrophs and has been
detected in most Methylosinus strains (Bowman et al., 1993). Many methanotrophs contain
both particulate and soluble enzymes (Murrel et al., 2000) sMMO synthesis is repressed in
the presence of copper (50 nM) with concurrent increased synthesis of pMMO (Murrel et
al., 2000). For this reason it is thought that sMMO may have evolved in conditions of
copper limitation (Hanson and Hanson, 1996). Limitation in copper may arise from
chelation, adsorption and complexation processes, especially with various organic
compounds. Copper-limited environments such as groundwater are often dominated by
pMMO-producing methanotrophs (Bowman et al., 1993).
sMMO lacks substrate specificity and more than 250 known compounds of many different
structural types can be oxidised by sMMO (Bowman et al. 1993). pMMO of
methanotrophs is not completely specific for methane, but catalyses the oxidation of NH3
to NH2OH. pMMO also hydroxylates ammonia to NO2- (Hanson and Hanson, 1996). On
the other hand ammonia oxidising autotrophic bacteria can oxidise methane to methanol
with their ammonia monooxygenase (AMO) (Bédard and Knowes, 1998). These bacteria
are very similar in the ways they oxidize methane and ammonia, and may also oxidise
several other substrates. However, the maximum rates by which ammonia oxidising
bacteria oxidise methane to methanol are considerably lower than that of methane oxidisers
25
and vice versa (Bédard and Knowles, 1989). AMO and pMMO share a number of
similarities. Both enzymes contain copper, may oxidise a variety of inorganic and organic
compounds and also have similar inhibition profiles (Bédard and Knowles, 1989).
All known methanotrophs and Gram-negative methylotrophs contain the key enzyme,
methanol dehydrogenase (MDH). MDH is the second enzyme in the methane oxidation
pathway. It oxidises methanol, which is produced from methane by MMO, to
formaldehyde, the intermediate of both assimilative and dissimilative metabolism in
methylotrophs. The functional marker gene mxaF, targeting the gene encoding the large
subunit of MDH, is therefore a suitable target for detecting methylotrophs (Hanson and
Hanson, 1996).
Almost all aerobic methanotrophs contain a pMMO and therefore a functional pmoA,
which encodes the α-subunit of the pMMO (Bourne et al., 2001). The most commonly
used functional gene markers for studying the diversity of methylotroph and methanotroph
communities are mxaF and pmoA (Wang et al. 2004). The combined use of both markers
may enable the detection of a large group of organisms participating in C1 metabolism. As
the phylogenetic tree of pmoA correlates well with 16S rRNA phylogeny, pmoA is a
suitable marker for simultaneous observation of functionality and taxonomy (Kolb et al.,
2003; McDonald et al., 2008).
1.2.9 Anaerobic methane oxidation
Methane is the most abundant hydrocarbon in the atmosphere, and plays important role as
a green house gas. It has been estimated that it has so far contributed an estimated around
20% of postindustrial global warming (Kirschke et al., 2013). The causes and effects of the
variation in global fluxes of methane throughout Earth’s history are studied widely, but the
underlying microbial processes and their key agents remain poorly understood. This is a
disturbing knowledge gap because 85% of the annual global methane production and about
60% of its consumption are based on microbial processes. Only three key functional
groups of microorganisms of limited diversity regulate the fluxes of methane on Earth,
namely the aerobic methanotrophic bacteria, the methanogenic archaea, and their close
relatives, the ANaerobic MEthanotrophic archaea (ANME) (Knittel and Boetius, 2009).
Anaerobic oxidation of methane (AOM) has mystified biogeochemists and
microbiologists for decades despite a strong research effort aimed at understanding its
regulation (Joye, 2012). Anaerobic methanotrophic archeae have been proposed as the
major candidates for AOM reaction. Joye (2012) summarised four suggested mechanisms
of AOM (Figure 5). Two of these involve single microorganisms using previously
undescribed metabolic pathways that produce either an unexpected intermediate (nitric
oxide, by Methoxymirabilis oxyfera with intra-aerobic methane oxidation pathway; Ettwig
et al., 2010) or metabolic products (disulphide production by ANME-2 archaea; Milucka et
al., 2012). It is still presumed that the other two processes — the coupling of AOM to
either sulphate reduction or to metal-oxide reduction — operate through the synergistic
interactions of microbial consortia. However, the exact microbial partners in the case of
AOM coupled to metal reduction, and the metabolic intermediates involved in either suite
of processes, have yet to be determined.
26
Recently Haroon et al. (2013) showed that ANME-2d population performed nitrate-driven
AOM. The ANME-2d was capable of independent AOM through reverse methanogenesis
using nitrate as the terminal electron acceptor. Nitrate produced was reduced to dinitrogen
gas through a syntrophic relationship with an anaerobic ammonium-oxidizing bacterium.
Haroon et al. (2013) proposed the ANME-2d population a name Candidatus
´Methanoperedens nitroreducens´.
New primers for the detection of closely related sequences for M. oxyfera have been
successfully developed (Luesken et al., 2011). The new primer set did not detect aerobic
methane oxidisers and vice versa.
Figure 5. There are four known ways in which microorganisms achieve anaerobic
oxidation of methane (AOM). 1) Anaerobic methanotrophic archaea (ANMEs) oxidize
methane and convert it to carbon dioxide and water together with sulphate-reducing
bacteria, which transform sulphate to hydrogen sulphide (Boetius et al., 2000). The
mechanism of energy exchange between the ANMEs and the sulphate-reducing bacteria is
unknown. 2) The oxidation of methane to CO2 by ANMEs is coupled to the reduction of
metal oxides, whereby metals such as manganese (Mn) or iron (Fe) are reduced to the +2
oxidation state (Beal, et al., 2009). 3) The bacterium Methoxymirabilis oxyfera converts
nitrite (NO2−) to nitric oxide (NO) and then dismutates (splits) NO into nitrogen and
oxygen as diatomic gases. The bacterium then uses the resulting O2 to support methane
oxidation (Ettwig et al., 2010). 4) Some ANMEs oxidize methane but also reduce sulphate
to zero-valent sulphur (S0), which they produce in the form of disulphide (HS2−) (Milucka
et al., 2012). The disulphide can be used by associated bacteria, Deltaproteobacteria, to
yield sulphide (HS−) and sulphate, but this is an association of convenience, rather than
necessity. (Modified from Joye, 2012).
27
1.2.10 Methanogens
Methanogens are members of archaea, which produce large quantities of methane as the
major product of their metabolism and are strict anaerobes (Deppenmeier, 2002). Methane
synthesis is the major source of energy for growth of methanogens. The methanogenic
bacteria are related to each other primarily by their mode of energy metabolism but are
very diverse with respect to their other properties. Methanogens obtain their energy for
growth from the conversion of a limited number of substrates to methane gas. The major
substrates are H2 + CO2, formate, methylated C1 compounds and acetate as energy and
carbon sources for growth. All of these substrates are converted stoichiometrically to
methane (Deppenmeier, 2002; Whitman et al., 2006).
There are three substrate groups for the growth of methanogens (Whitman et al., 2006;
Penger et al., 2012). In the first group the energy substrate (electron donor) is H2
(hydrogenotrophic methanogenesis), formate or certain alcohols and the electron acceptor
is CO2, which is reduced to methane. The capability to utilize H2 as an electron donor for
CO2 reduction is almost universal among methanogens. In the second group, the energy
substrate is one of a variety of methyl-containing C-1 compounds (methylotrophic
methanogenesis). Usually these compounds are dismutated. Some molecules of the
substrate are oxidised to CO2. The electron acceptors are the remaining methyl groups,
which are reduced directly to methane. In the third group, acetate (acetoclastic
methanogenesis) is the major source of methane. Here the methyl carbon of acetate is
reduced to methane and the carboxyl carbon is oxidised to CO2. The reaction involves
several unique coenzymes and cofactors (Whitman et al., 2006). The ability to catabolise
acetate is limited to species of Methnosarcina and Methanosaeta (Boone et al., 1993).
Methanogenesis may be viewed as a form of anaerobic respiration where CO2, the methyl
groups of C-1 compounds or methyl carbon of acetate is the electron acceptor. The free
energy change associated with methanogenesis allows for the synthesis of 1 (acetate) to
less than one ATP can be synthesized under environmental conditions (Deppenmeier and
Müller, 2007). The methanogens are abundant in environments of moderate temperature,
pH and salinity and where electron acceptors such as O2, NO3-, Fe3+ and SO42- are limiting
(Whitman et al., 2006). In anoxic environments, the presence of NO3-, Fe3+ and SO42inhibits methanogenesis and e.g. in anoxic soil, nitrate reducers, ferric iron reducers and
sulphate reducers outcompeted methanogens and the flow of electrons moved from CH4
towards CO2 production (Chidthaisong and Conrad, 1999) Presumably, the ability of the
sulphate reducing bacteria to outcompete the methanogens is a direct consequence of the
more positive reduction potential of SO42- compared to that of CO2 (Whitman et al., 2006).
The methanogenic main groups are the Methanomicrobiales, Methanosarcinales,
Methnobacteriales, Methanococcales and Methanopyrales.
1.2.11 Fermentation
Fermentation is a form of anaerobic digestion that generates ATP by the process of
substrate-level phosphorylation or ion-gradient-driven phosphorylation (Müller, 2001;
Böck, 2009). The energy for generating ATP comes from the oxidation of organic
compounds. Instead of using oxygen as the terminal acceptor for the electrons withdrawn
from the substrate during degradation, anaerobes use alternate acceptors such as nitrate,
nitrite, sulphur, or sulphate in a process designated anaerobic respiration. If such acceptors
28
are not available, fermenting organisms can internally produced electron acceptors (Böck,
2009). The substrate range is wide including polymers such as polysaccharides, proteins,
DNA and lipids that are attacked by extracellular enzymes and broken into smaller units
which are taken up by the initial degrader or other fermenters. Fermentable monomers
include sugars (hexoses, pentoses, tetroses), polyols, organic acids, amino acids, and
purines and pyrimidines. Apart from these classical substrates, others such as acetylene,
citrate, glyoxylate, succinate, oxalate and malonate are also fermented (Müller, 2001).
Fermentation can be an obligatory process, as in the diverse group of Clostridia, or it may
be facultative, as in staphylococci, enterobacteria, or yeast, which have complex regulatory
mechanisms to ensure that fermentative enzymes are produced merely when the cells are
deprived of external electron acceptors (Böck, 2009).
The ATP yield of fermentation is dependent on the pathway used and can range from 0.3
to 4 mol ATP per mol substrate (Müller, 2001). In average the energy yield lies in the
range of 2–2.5 mol of ATP per mole of consumed glucose. Most of the carbon in the
substrates is not incorporated into cell mass due to low-energy gain, but rather appears in
the fermentation end products. Because the reducing equivalents withdrawn from the
substrate must be transferred to an internally generated acceptor, the sum of the oxidation
state of the reduced products and of the oxidized products must equal the oxidation state of
the substrate (Böck, 2009).
Hydrolytic breakdown of carbohydrates into the monomeric or oligomeric constituents
provides important fermentation substrates for many obligate and facultative anaerobes.
Types of carbohydrate fermentation are classified in most cases according to the products
that are formed and released. A characteristic feature of most carbohydrate breakdown
routes during fermentation is that pyruvate plays a central role.
Short-chain organic acids and amino acids are also major substrates for fermentations.
These organic acids are generated from carbohydrates by fermentation or by oxidative
deamination of amino acids arising from proteolysis. Among the short-chain organic acids,
acetate is by far the most abundant substrate (methanogens). Most propionic acid bacteria
are able to ferment lactate, which yields propionate as end product. In addition amino acids
are readily fermented by many bacteria, especially by members of the Gram-positive
anaerobes belonging to the peptolytic clostridia. Amino acid fermentation proceeds
through individual pathways that involve a plethora of chemically unusual and often also
novel reactions. Few specialist organisms can ferment purines and pyrimidines. Organisms
that are able to degrade pyrimidines or derivatives thereof are scarce (Böck, 2009; Müller,
2001).
1.3 Terminal electron acceptors and donors in deep subsurface
In deep subsurface the energy sources are organic matter and other reduced compounds
such as Mn(II), Fe(II), ammonia and sulphide that can be oxidized with the release of
energy. Microorganisms have evolved various strategies for conserving energy to support
growth from these oxidations. The pathways in which microorganisms gain energy from
the oxidation of organic matter involve a complex series of electron transfer reactions
within the microorganisms (Lovley and Chapelle, 1995). The occurrence of anaerobic
terminal electron accepting processes (TEAP) govern the biogeochemistry and
29
microbiology of subsurface environments (Heinmann et al., 2010). The significant electron
transfer is the transfer to an electron acceptor from the environment. The most common
terminal electron acceptors are expected to be O2, nitrate, Mn(IV), Fe(III), sulphate, and
CO2. Metal and metalloid reductions carried out by microorganisms include in addition:
U(VI) to U(IV), Se(VI) to Se(0), Cr(VI) to Cr(III), Mo(VI) to Mo(V) and Au(III) to Au(0).
The reduction of metals other than Fe(III) and Mn(IV) is generally not significant in terms
of organic matter oxidation because of the relatively low concentrations of these electron
acceptors (Lovley and Chapelle, 1995).
1.3.1 Sulphur metabolism
Sulphur is among the most abundant elements on Earth. It is mainly present as pyrite
(FeS2) or gypsum (CaSO4) in rocks and sediments and as sulphate in seawater (Muyzer and
Stams, 2008). The sulphur cycle is complex, because sulphur has a broad range of
oxidation states, from -2 (completely reduced) to +6 (completely oxidized), and can be
transformed both chemically and biologically. In addition, the sulphur cycle is closely
linked to other element cycles, such as the carbon and nitrogen cycles.
In assimilative sulphate reduction sulphate is taken up as a nutrient and reduced to
sulphide, which is then incorporated into sulphur-containing amino acids and enzymes
(Figure 6) (Böttcher, 2011). The formation and the degradation of organic sulphur
compounds (C–SH) are not solely microbial processes, but numerous other organisms also
participate. Particularly, the formation of organic sulphur is accomplished by all
photosynthesizing organisms, like algae or green plants. Dimethylsulphide (DMS) is the
most common product of oceanic green algae sulphur conversions. Green plants and many
microorganisms assimilate sulphate as their sole sulphur source (Lens, 2009).
Oxidation and reduction reactions for the generation of metabolic energy are also
important. In dissimilative sulphur and sulphate reduction hydrogen sulphide is
produced from elemental sulphur or from dissolved sulphate by sulphur- and sulphatereducing microorganisms, respectively (Böttcher, 2011). Energy metabolism based on the
disproportionation of the inorganic sulphur compounds thiosulphate and sulphite into
sulphide and sulphate has been detected in several SRB. In addition disproportionation of
elemental sulphur to hydrogen sulphide and sulphate has been found (Finster et al., 1998).
Members of both bacteria and archaea can use sulphate as a terminal electron acceptor
(Muyzer and Stams, 2008).
30
S°
2
2
Oxic
SH groups of proteins
7
DMSO
6
8
DMS
1
SO42DMSO
7
6
H 2S
DMS
8
SH groups of proteins
Anoxic
5
5
3
S°
4
3
Figure 6. 1) Sulphate-reducing bacteria have a key role in the sulphur cycle. They use
sulphate (SO42-) as a terminal electron acceptor in the degradation of organic matter,
which results in the production of hydrogen sulphide (H2S). 2) The sulphide can be
oxidized aerobically by chemolithotrophic sulphur-oxidizing bacteria or 3) anaerobically
by phototrophic sulphur bacteria to elemental sulphur (S°) and SO42-. Other
transformations, which are carried out by specialized groups of microorganisms: 4)
sulphur reduction and 5) sulphur disproportionation. 6) Organic sulphur compounds, such
as dimethylsulphoxide (DMSO) can be transformed into dimethylsulphide (DMS) and vice
versa by several groups of microorganisms. 7) In assimilative sulphate reduction sulphate
is taken up as a nutrient and incorporated into sulphur-containing amino acids and
enzymes. 8) Desulphurylation of organic sulphur during the decomposition of dead
organisms releases the sulphur again as HS. (Modified from Muyzer and Stams, 2008)
In the microbial sulphur cycle, sulphate is converted into sulphide by SRB via
dissimilatory sulphate reduction. This process of bacterial respiration occurs under strictly
anaerobic conditions and uses sulphate as a terminal electron acceptor. Electron donors are
usually organic compounds, eventually, hydrogen (Lens, 2009). In the presence of
sulphate, SRB are able to use several intermediates of the anaerobic mineralization
process. Besides the direct methanogenic substrates molecular hydrogen (H2), formate,
acetate, and methanol, they can also use propionate, butyrate, higher and branched fatty
acids, lactate, ethanol, and higher alcohols, fumarate, succinate, malate, sugars, carbon
monoxide, amino acids and aromatic compounds. Desulfovibrio strains have been reported
to be able to reduce di-, tri-, and tetrathionate. Typically, polymeric organic compounds,
such as starch, cellulose, proteins, nucleic acids (DNA and RNA) and fats are not direct
substrates for SRB. In addition to the reduction of sulphate, dismutation of sulphur,
sulphite and thiosulphate is also very common among SRB. (Lens, 2009; Muyzer and
Stams, 2008). In the absence of an electron acceptor, SRB are able to grow through a
31
fermentative or acetogenic reaction. Pyruvate, lactate, and ethanol are easily fermented by
many SRB (Lens, 2009).
Sulphate reducing microorganisms are found by detecting their dsr (dissimilatory sulphite
reductase) genes or aps (adenosine 5’-phosphosulphate reductase) genes from
environmental samples. The dsr and aps are highly conserved across the sulphate reducing
bacteria and archaea (Friedrich, 2002).
Sulphur oxidation is performed by many bacteria and some archaea. The oxidation of
reduced sulphur is mediated by a diverse range of bacterial species. They can be divided
into two main groups: the aerobic and microaerobic chemotrophic sulphur oxidisers
(sometimes called the colourless sulphur bacteria) and the anaerobic phototrophic sulphur
oxidisers (sometimes called the purple and green sulphur bacteria) (Lens, 2009).
Various reduced-sulphur compounds, for example, sulphide, elemental sulphur and
thiosulphate can be oxidised by microorganisms. Oxidation of sulphide into elemental
sulphur is performed by autotrophic bacteria. The chemoautotrophic process proceeds
aerobically or microaerobically and anaerobically by photoautotrophic sulphide oxidation.
In anoxic conditions the oxidation of sulphide proceeds in oxygen-free conditions, using
nitrate as an electron acceptor. Oxidation of sulphide in anoxic conditions is traditionally
thought to be carried out by bacteria from the genus Thiobacillus, like T. albertis and T.
neapolitanus (Lens, 2009). In recent years Epsilonproteobacteria have been increasingly
recognized as important members of the microbial communities at deep-sea vents able to
generate energy by oxidizing reduced sulphur compounds. For example Sulfurimonas
paralvinellae found in hydrothermal vent polychaetes was able to generate energy by
oxidising reduced sulphur compounds, elemental sulphur or thiosulphate as the sole energy
source, carbon dioxide as the sole carbon source, ammonium or nitrate as the sole nitrogen
source. Nitrate could serve as the sole electron acceptor to support the growth (Takai et al.,
2006).
In bacteria, there are two different sulfur oxidation pathways: the reverse sulphate
reduction pathway, which uses the Dsr, Apr, or Sat enzymes (Kappler and Dahl, 2001),
and the sulphur oxidation Sox multienzyme system (Friedrich et al., 2001). The Sox
system contains four protein components, SoxYZ, SoxXA, SoxB, and SoxCD, for the
complete oxidation of sulfide and thiosulfate to sulfate (Friedrich et al., 2005). The SoxB
protein, encoded by the soxB gene, has been identified as the sulfate thiol esterase in the
Sox system, and the soxB gene is used as a marker gene to survey sulphur-oxidizing
bacteria (Friedrich et al., 2005). Homologous proteins to SoxB have not been found in the
domain archaea (Friedrich et al., 2001).
1.3.2 Iron metabolism
Iron is the fourth most abundant element in the Earth’s crust, and depending upon the
geochemical conditions, it occurs either in soluble forms or in a variety of minerals
(Schwertmann and Fitzpatrick, 1992). Fe(III) minerals may account for a few percent dry
weight of rocks, soils, and sediments. For this reason, in most anoxic ecosystems Fe(III)
minerals are the dominant electron acceptors for bacteria and archaea (Straub, 2011b).
Fe(III) oxides are poorly soluble at neutral pH in in contrast to other electron acceptors
32
such as oxygen, nitrate and sulphate. The solubility of Fe(III) oxides increases with
increasing acidity and at pH values below 2.5 ferric iron is well soluble (Cornell and
Schwertmann, 2003). Concomitant with the change in pH and Fe(III) oxide solubility, the
redox potential of the transition between ferric and ferrous iron changes significantly. The
redox potential of the redox pair Fe3+/Fe2+ is +770 mV only at acidic pH values. At neutral
pH, with poorly crystalline Fe(III) oxides (ferrihydrite, lepidocrocite) as the oxidant, the
redox potential ranges between +100 and -100 mV, while the redox potential of more
crystalline Fe(III) oxides (goethite, hematite, magnetite) may be as low as -300 mV (Straub
et al., 2001).
Dissimilatory Fe(III) reduction to Fe(II) has an important influence on the geochemistry
(Lovley et al., 2004). The concentration of dissolved Fe(II) is controlled by precipitationdissolution reactions and by adsorption processes: Fe(II) precipitates with carbonates,
phosphates and sulfides and tends to adsorb to soil particles and mineral. Chemical
reoxidation reactions of Fe(II) depend on the pH and on the presence of appropriate
oxidants. In the absence of molecular oxygen, Fe(II) can be chemically oxidized at
appreciable rates only by nitrite or Mn(IV) (Straub, 2011a).
In microbes, iron is necessary for processes such as electron transport, nitrogen fixation,
removal of toxic forms of oxygen, synthesis of DNA precursors, tRNA modifications, and
syntheses of certain amino acids and tricarboxylic acid cycle intermediates (Earhart, 2009).
Although iron is abundant on the surface of the earth, its bioavailability is relatively poor.
Bacterial iron assimilation occurs by a great variety of pathways and many bacteria have
multiple iron uptake systems. This enables them to obtain iron in diverse environments and
from several sources. The large number and types of transport systems and variations
among closely related bacteria make generalisations regarding iron uptake difficult, even
within a species (Earhart, 2009).
Dissimilatory Fe(II)-oxidizing prokaryotes utilize Fe(II) as electron donor in aerobic or
anaerobic respiration, i.e., they gain energy by coupling the oxidation of Fe(II) to the
reduction of molecular oxygen or to the reduction of nitrate. In the oxidation of Fe(II) to
Fe(III), only one electron can be obtained, dissimilatory Fe(II)-oxidizing prokaryotes have
to oxidize large quantities of Fe(II) to gain enough energy for maintenance and growth
(Straub, 2011a). Four physiological groups of prokaryotes are known to grow with Fe(II)
as electron donor: acidophilic aerobic and neutrophilic aerobic iron oxidisers, neutrophilic
anaerobic nitrate-reducing iron oxidising prokaryotes and anaerobic photosynthetic iron
oxidisers (Hedrich et al., 2011). The dissimilative oxidation of Fe(II) leads to the formation
of Fe(III) minerals which are well soluble under acidic conditions but barely soluble at
neutral pH. Hence, neutrophilic Fe(II)-oxidizing prokaryotes have to cope with a virtually
insoluble metabolic end product (Straub, 2011a).
Most acidophilic aerobic Fe(II) oxidising proteobacteria can obtain energy from the
oxidation of ferrous iron alone, when this is coupled to the reduction of molecular oxygen.
However, most described species are in fact facultative anaerobes that can also couple the
oxidation of reduced sulphur compounds to the reduction of ferric iron in anoxic
environments (Hedrich et al., 2011).
33
Currently, all known aerobic neutrophilic, lithotrophic Fe(II) oxidising bacteria are
proteobacteria (Emerson et al., 2010). These appear to be divided into freshwater species,
all of which are Betaproteobacteria, and marine species, which have mostly been affiliated
to the proposed (Candidatus) class ‘Zetaproteobacteria’ (Emerson et al., 2007).
Neutrophilic iron oxidisers often colonize the interface between aerobic and anoxic zones
in sediments and ground waters, and have often been described as ‘gradient’ organisms.
Anaerobic neutrophilic nitrate reducing Fe(II) oxidising bacteria (Alpha- Beta-,
Gamma- and Deltaproteobacteria) and archaea (Archaeoglobales) are capable of oxidising
Fe(II) with nitrate as the terminal electron acceptor. At pH 7, all redox pairs of the nitrate
reduction pathway can accept electrons from Fe(II), because their redox potentials are
more positive than of the iron redox couple (Straub et al., 1996). All known species are
able to utilize alternative electron donors (e.g., hydrogen, organic acids) and alternative
electron acceptors (nitrite, oxygen, ferric iron) (Straub, 2011a). The anaerobic neutrophilic
nitrate reducing iron oxidising bacteria can be divided into those that are autotrophic, and
those that use organic materials as carbon sources and can also grow as heterotrophs
(Hedrich et al., 2011).
The ability to reduce Fe(III), ferric iron, to Fe(II), ferrous iron, by the transfer of electrons
occurs widely within the domains bacteria and archaea. The electrons for Fe(III) reduction
derive mainly from the metabolic oxidation of organic compounds or hydrogen (Straub,
2011b). Most known Fe(III)-reducing prokaryotes that grow by Fe(III) reduction belong to
the phylum Proteobacteria with representatives in each of the five subdivisions Alpha(e.g., Acidiphilium rubrum), Beta- (e.g., Ferribacterium limneticum), Gamma- (e.g.,
Shewanella
oneidensis),
Delta(e.g.,
Geobacter
sulfurreducens),
and
Epsilonproteobacteria (e.g., Sulfurospirillum barnesii). Several Gram-positive bacteria that
belong to the phylum Firmicutes (e.g., Thermoanaerobacter siderophilus) and some
separate bacterial lineages like Geothrix fermentans or Geovibrio ferrireducens also have
the ability to reduce ferric iron (Lovley et al., 2004). In addition archaea of the phyla
Euryarchaeota (e.g., Pyrococcus furiosus) and Crenarchaeota (e.g., Pyrobaculum
islandicum) show reduction of ferric iron (Vargas et al., 1998).
Dissimilatory Fe(III)-reducing prokaryotes utilize Fe(III) as terminal electron acceptor
in anaerobic respiration, they gain energy by coupling the oxidation of an electron donor to
the reduction of Fe(III). Only few species have been so far isolated on Fe(III) as an
electron acceptor for growth, including arhaeon Geoblogus ahangari from a hydrothermal
system at 2000 m deep (Kashefi et al., 2002b) and bacterium Geothermobacterium
ferrireducens from Yellowstone hot sediment sample (Kashefi et al., 2002a). In the
absence of Fe(III), prokaryotes either grow by fermentation or utilize alternative electron
acceptors. Common alternative electron acceptors include oxygen, nitrate, manganese,
reduced sulphur compounds and fumarate. Some species also transfer electrons to heavy
metals (e.g. uranium, chromium), graphite electrodes, humic substances or chlorinated
compounds. Fermentation end products such as acetate, ethanol, and hydrogen as electron
donors are utilized by the majority of Fe(III)-reducing prokaryotes. Only few prokaryotes
are known to couple the dissimilatory reduction of Fe(III) to the oxidation of sugars, amino
acids, long-chain fatty acids, monoaromatic compounds or reduced inorganic sulphur
compounds (Straub, 2011b).
34
Acidophilic Fe(III)-reducing prokaryotes thrive in environments of low pH (<3). All
known acidophiles that use ferric iron as an electron acceptor to support their growth are
facultative reducers. In addition, they can also reduce molecular oxygen (Johnson et al.,
2012). So far no representative of archaea has been described to grow by dissimilatory
Fe(III) reduction at acidic pH values (Straub, 2011b).
Neutrophilic Fe(III)-reducing prokaryotes use Fe(III) as terminal electron acceptor only
under anoxic condition in the absence of more favourable electron acceptors such as nitrate
or manganese (Straub, 2011). As Fe(III) oxides are poorly soluble at neutral pH
prokaryotes have developed different strategies to transfer electrons from the cell to the
surface of Fe(II) minerals. The metabolic strategies to improve reduction of Fe(II) minerals
include i) Direct contact between the bacterial cell and Fe(III) minerals facilitates Fe(III)
reduction over short distances. ii) Bacteria secrete chelating agents or exploit microbial or
environmental redox-active electron shuttles (such as flavins or dissolved and solid-state
humic substances, respectively) to facilitate electron transfer over short (nm) and long
(μm) distances. iii) Electrically conductive pili and multistep electron hopping via redox
cofactors that are present in biofilms have been implicated in long-distance extracellular
electron transfer (Melton et al., 2014). Reduction of Fe(III) by electron shuttling
compounds is of particular importance in natural habitats where prokaryotic cells and
Fe(III) minerals are not evenly distributed and Fe(III) might not be directly physically
accessible for prokaryotic cells (Straub, 2011).
1.3.3 Manganase metabolism
Manganese is about the tenth most abundant element in the Earth´s upper continental crust
with an average abundance of 650 mg/kg (Gilkes and McKenzie, 1988). Oxidised
manganese is found in the environment in minerals like birnessite and pyrolusite. Next to
oxygen, manganese oxides are some of the strongest naturally occurring oxidising agents
in the environment (Tebo et al., 2005). Mn oxides participate in a wide range of redox
reactions with organic and inorganic chemical species and compounds. Many dissimilatory
metal-reducing bacteria can use Mn oxides as the terminal electron acceptor for the
oxidation of organic matter or H2 in the absence of oxygen. Mn oxides also have high
sorptive capacities and therefore adsorb a wide range of ions, controlling the distributions
and bioavailability of many toxic and essential elements (Tebo et al., 2004). Manganese
exists in several oxidation states ranging from 0 to +7; however in nature the +2, +3, and
+4 oxidation states have biological importance. Out of the three naturally occurring
oxidation states, only manganese in the +2 oxidation state can occur as a free ion in
aqueous solution (Figure 7). Manganese in the +3 oxidation state can occur in aqueous
solution only when it is complexed. The free +3 ion tends to disproportionate into the +2
and +4 oxidation states. The +4 oxides of manganese (mainly MnO2) are insoluble in water
(Das et al., 2011).
35
Mn(II)-oxidising bacteria are ubiquitous in nature, isolated from virtually any
environmental sample. They are most conspicuous when there is an adequate supply of
reduced Mn(II), such as in oxic-anoxic transition zones (Tebo et al., 2005).
Microorganisms, especially bacteria but also fungi, are known to catalyze the oxidation of
Mn(II) and the formation of Mn(III, IV) oxide minerals (Tebo et al., 2004). These
organisms are phylogenetically diverse, with representatives in the Firmicutes,
Actinobacteria and the Alpha-, Beta- and Gamma-proteobacteria (Tebo et al., 2005).
Mn (II)
Mn (III)
Low pH,
low redox potential
High pH,
high redox potential
Low Low pH
[Mn (II)]
Mn (IV)
Figure 7. The Mn cycle of oxidation states found in nature. Mn(II) is thermodynamically
stable in the absence of O2 and at low pH, whereas in the presence of O2, Mn(III) and
Mn(IV), which occur primarily as insoluble Mn(oxyhydr)oxides, are favoured (modified
from Tebo et al., 2004).
The physiological function of bacterial Mn(II) oxidation remains unknown. Because
Mn(IV) formation is thermodynamically favourable, the bacteria could derive energy from
the reaction; however, this has not been shown conclusively for any organism (Geszvain et
al., 2012). No correlation between CO2 fixation and Mn(II) oxidation has been
demonstrated, and other proposed biological functions of Mn(II) oxidation: protection
from toxic metals, reactive oxygen species, UV light, predation, or viruses; maintenance of
an electron-acceptor reservoir for use in anaerobic respiration; breakdown of natural
organic matter into metabolisable substrates; and scavenging of micronutrient trace metals,
remain problematic (Spiro et al., 2010).
The Mn oxidase enzymes are either multicopper oxidase enzymes or heme peroxidase
family proteins. Included in the multicopper oxidase family are laccases, identified as the
Mn(II)-to-Mn(III) oxidases of fungi. In some cases, multicopper oxidase enzymes and Mn
peroxidases have been shown to work together in fungi (Geszvain et al., 2013). Several
species of marine Bacillus spores oxidize Mn(II) on their exosporium, the outermost layer
of the spore, encrusting them with Mn(IV) oxides catalyzed by an multicopper oxidasecontaining complex (Butterfield et al., 2013).
36
The microbial reduction of Mn(IV) to Mn(II) is proposed to proceed step-wise via two
successive one-electron transfer reactions with production of soluble Mn(III) as transient
intermediate. The first electron transfer reaction is a reductive solubilisation step that
increases Mn bioavailability and the second reaction is coupled to energy generation (Lin
et al., 2012).
Although a large number of microorganisms capable of manganese reduction are known,
many of these are isolated as iron reducers from environments dominated by iron reduction
and are only subsequently shown to reduce manganese oxides in culture. Most properties
of iron reducers and iron-reducing communities have been assumed also to apply to
manganese reducers (Vandieken et al., 2012). Only few species have been isolated with
manganese oxide including Shewanella putrefaciens MR1 from marine sediment (Myers
and Nealson, 1988) and the thermophile Bacillus infernus isolated from terrestrial
subsurface (Boone et el., 1995) and Bacillus spp. from drinking water system biofilms
(Cerrato et al., 2010). Manganese reduction is rarely tested in the description of new
species, so that this capability might be more widespread in existing isolates than is
currently known. Considering the large difference in redox potential between manganese
and iron oxides, it seems likely that manganese reduction in the environment is actually
carried out by specialized manganese reducers (Vandieken et al., 2012).
Vandieken et al. (2012) found highly similar populations of manganese reducers in three
manganese oxide-rich sediments from three distant locations with different environmental
conditions, a Swedish fjord, the deep Skagerrak strait and a continental basin in the
northwest Pacific. They suggested new function, reduction of manganese oxides, for
Epsilon- and Gammaproteobacteria affiliated to Arcobacter, Colwellia and
Oceanospirillaceae.
1.4 Description of the site
The groundwater in Olkiluoto is stratified due to physicochemical parameters (Posiva,
2011). The surface water to a depth of 30 m is fresh to brackish and of meteoric origin. The
uppermost water strata have high concentrations of dissolved inorganic carbon
(bicarbonate). The salinity gradient (concentration of total dissolved solvents, TDS) and
Cl- grows with depth. At the depth between 100 and 400 meters the salinity is similar to
that of present day Baltic, but below 400 m bsl, the salinity significantly increases. The
highest salinity observed so far is 84 g L-1 (Posiva Report, 2008). Between the depths of
100 – 300 m, the concentration of SO4 is elevated as the water originates from the Littorina
Sea. Below this layer, the methane concentration in the water increases. The deepest water,
below 300 m, is dominated by Cl-, whereas SO4 is almost absent.
The temperature of the groundwater at Olkiluoto rises linearly with depth, and ranges from
around 5–6 ˚C at a depth of 50 m to approximately 20 ˚C at a depth of 1000 m (Ahokas et
al., 2008). The pH in the water throughout the depth is slightly alkaline (Appendix B).
Several of the deeper samples included in this study span through the same
hydrogeological zones, HZ20 or HZ21 (Appendix B).
37
This study focused on 19 fracture zone samples originating from 14 drillholes (Figure 8) in
Olkiluoto and the samples were obtained from vertical depths ranging from -296 m to -798
m. The samples represented brackish sulphate water and saline water.
Figure 8. Map of Olkiluoto Island with 57 drillboreholes for research and monitoring
purposes. The boreholes sampled in this study are: OL-KR1, 2, 3, 5, 6, 9, 13, 20, 23, 25,
29, 44, 46 and 49.
38
39
2 MATERIALS AND METHODS
2.1 Sampling
The deep groundwater samples (Table 1) were collected from Olkiluoto area between
December 14th, 2009 and August 21st, 2013. The samples were collected from multipackered drillholes as well as from open drillholes where the sampling section was
packered-off in order to seal off a specific water-conducting fracture zone from the rest of
the drillhole. This isolated fracture zone is purged by pumping out the water collected
between the packers and allowing water from the isolated fracture zone to run into the
packered off section of the drillhole. The conductivity and pH of the pumped water is
followed, and when the values settle, it is assumed that the water represents the endemic
fracture zone water. In order to standardize these samplings, the packer-sealed fracture
zones have been pumped for several weeks before sampling. Upon sampling, the water
was led into an anaerobic glove box (MBRAUN, Germany) by use of a sterile, gas tight
polyacetate tube (8 mm outer diameter) and collected in an acid washed, sterile glass
bottle. Microbial biomass for nucleic acid analyses was concentrated from 500 mL and
1000 mL samples by filtration on cellulose acetate filters (0.2 μm pore size, Corning) by
vacuum suction in the anaerobic chamber. The filters were immediately cut out from the
filtration funnels with sterile scalpels and frozen on dry ice in sterile 45 mL cone tubes
(Corning). The frozen samples were transported in dry ice to the laboratory where they
were stored at -80 °C until nucleic acid extraction. Samples for microbial density
measurements were collected in acid-washed, anaerobic and sterile 100 mL head-space
vials and transported to laboratory at 4 °C protected from light.
Table 1. Groundwater samples studied.
Sample
OL-KR13/360_10
OL-KR13/360_12
OL-KR3/339_12
OL-KR20/410_13
OL-KR6/422_10
OL-KR6/422_13
OL-KR25/357_11
OL-KR3/381_11
OL-KR23/425_09
OL-KR46/471_13
OL-KR46/493_13
OL-KR5/457_12
OL-KR49/532_09
OL-KR9/468_11
OL-KR9/565_11
OL-KR2/597_10
OL-KR1/609_10
OL-KR44/766_13
OL-KR29/801_10
Vertical
depth (z)
-296 m
-296 m
-303 m
-323 m
-328 m
-330 m
-330 m
-340 m
-347 m
-372 m
-390 m
-405 m
-415 m
-423 m
-510 m
-559 m
-572 m
-693 m
-798 m
Sampling date
Borehole type
Sampling
9.3.2010
21.8.2012
21.8.2012
20-21.8.2013
18.5.2010
20.8.2013
31.10-1.11.2011
29-31.8.2011
15.12.2009
15.1.2013
12.3.2013
16.10.2012
14.12.2009
31.10-1.11.2011
29-31.8.2011
27.1.2010
26.1.2010
15.1.2013
18.5.2010
Multi packered borehole
Multi packered borehole
Multi packered borehole
Multi packered borehole
Open borehole
Open borehole
Multi packered borehole
Multi packered borehole
Multi packered borehole
Open borehole
Open borehole
Multi packered borehole
Open borehole
Multi packered borehole
Multi packered borehole
Multi packered borehole
Multi packered borehole
Multi packered borehole
Multi packered borehole
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
Pumping
40
2.2 Geochemistry
Analysis methods are presented in Appendix A. The data was obtained from Posiva.
2.3 Nucleic acid isolation
Total DNA was isolated directly from the frozen cellulose-acetate filters. The filters were
cut to 2 × 2 mm2 pieces with sterile scalpels in a laminar flow hood, and the DNA was
extracted with the PowerSoil DNA extraction kit (MoBio Laboratories, Inc., Solana Beach,
CA). The isolation was performed according to the manufacturer’s instructions. The
isolated and purified DNA was then stored frozen at -80 °C until use. Total RNA was
isolated directly from the frozen cellulose-acetate filter with the PowerWater RNA
isolation kit (MoBio Laboratories, Inc., Solana Beach, CA). The filters were thawed on ice
and care was taken to minimize the time of thawing. The intact filters were inserted into
the bead tubes with flame-sterilized forceps and the RNA extraction was performed
according to the manufacturer’s instructions. Negative DNA and RNA isolation controls
were also included. DNA contamination of the RNA extracts was checked by PCR uainf
bacteria 16S rRNA gene specific primers. If no PCR product was obtained, no DNA
contamination was assumed and the RNA extract was submitted to cDNA synthesis. If a
PCR product was obtained, the RNA extract was first treated with DNase (Promega)
according to the manufacturer’s instructions. The RNA was subsequently submitted to
cDNA synthesis. Aliquots of 11.5 μL of RNA was incubated together with 250 ng random
hexamers (Promega) and 0.83 mM final concentration dNTP (Finnzymes, Espoo, Finland)
at 65 °C for 5 minutes and cooled on ice for 1 minute. The cDNA was synthesised with the
Superscript III kit (Invitrogen), by adding 4 μL 5x First strand buffer, 40 U DTT and 200 U
Superscript III to the cooled reactions. To protect the RNA template from degradation, 40
u recombinant RNase inhibitor, RNaseOut (Promega), was used. The reactions were
incubated at 25 °C for 5 minutes, 50 °C for 1 h and 70 °C for 15 min. Two or four parallel
reactions per sample as well as no template controls were performed. The parallel reactions
were subsequently pooled. RT-PCR was also performed on the negative RNA extraction
controls as well as negative reagent RT-PCR controls to ensure that these steps have
remained uncontaminated during the process.
2.4 Total Number of Cells (TNC)
Total number of microbial cells was estimated by comparing two methods a)
epifluorescence microscopy based on DAPI staining and b) flow cytometry based on
Syto16 staining. In addition bacterial numbers were quantified by qPCR analysis of the
16S rRNA genes. The method for qPCR is presented in the next chapter. The number of
microbial cells in most of the deep groundwater samples was determined by fluorescent
staining with 4’,6-diamidino-2-phenylindole (DAPI) (Sigma). A 5 mL subsample of each
groundwater sample was stained with 1 μg mL-1 of DAPI for 20 min at room temperature
in dark and under aerobic conditions. The number of cells in the sample was then
calculated from 30 microscopy fields according to the magnification factor (Olympus
BX60, Olympus Optical Ltd., Tokyo, Japan and 100 × magnification). The method and
calculation of results is described in Itävaara et al. (2008) and Bomberg et al. (2010).
The cell numbers in the samples OL-KR3/340-343m and OL-KR9/565-569m were
calculated by Flow cytometry. Two replicate samples (1−10 mL water) were dyed with 10
41
μL mL-1 Syto16 dye (Invitrogen) and fixed with 100 μL mL-1 formamide after a 20−30 min
incubation in the dark. In addition, replicate undyed samples were prepared to be used to
determine background fluorescence for the flow cytometry measurements. The
measurements were performed using a flow cytometer (BD FACaria, Becton Dicinson,
New Jersey, USA). Three subsequent measurements were performed on each replicate
sample, where the fluorescent microbial cells from 5 μL sample suspension were detected.
An average number of cells mL-1 groundwater was thus obtained from 6 measurings.
2.5 Amplification library preparation
Next Generation Sequencing technologies, such as the Roche FLX 454 high throughput
pyro sequencing technique enables study of microbial community composition of
uncultured microbial populations in environmental samples.
The amplification libraries for high throughput sequencing were prepared by PCR from the
DNA and cDNA samples as described in Bomberg et al. (2010) and Bomberg and Itävaara
(2012). Bacterial 16S rRNA gene fragments covering the V1-V3 variable regions were
amplified with primers 8F (Edwards et al., 1989) and P2 (Muyzer et al., 1993). Archaeal
16S rRNA gene fragments were produced by a nested PCR approach. First, a 806 bp long
fragment of the 16S rRNA gene was PCR amplified with the A109f (Großkopf et al. 1998)
and Arch915R (Stahl and Amann 1991) primers. Then, a second PCR with tagged
ARC344f (Bano et al. 2004) and Ar774r primers (modified from Barns et al., 1994) was
used to produce the tagged product for sequencing (covering the V3-V4 variable areas).
DsrB fragments were amplified in a single round PCR with tagged primers 2060F and
dsr4R (Wagner et al. 1998). McrA fragments were obtained by nested PCR. Initially, a 1.2
kb mcrA fragment was amplified with primers mcrA412f and mcr1615r (Nyyssönen et al.,
2012). The product of this PCR was then amplified with tagged primers ME1 and ME3r
(modified from Hales et al, 1996).
Fungal ITS fragments were amplified in a two-step PCR. First, a 420−825 bp long
fragment was amplified with primers ITS1F and ITS4 (Gardes and Bruns, 1993; White et
al., 1990). The product of this PCR was used as template in a secondary PCR with tagged
primers ITS1F and ITS2 (Buée et al., 2009) generating a ca 400 bp product.
2.6 Sequence processing and analysis
2.6.1 Small subunit ribosomal (16S) RNA gene, archaea and bacteria
The composition of microbial communities can be assessed by sequencing all the copies of
the universal small subunit ribosomal RNA gene, which is also known as the 16S rRNA
gene in archaea and bacteria. In theory, all the species present in a sample can be
discovered by sequencing the genomic copies of the 16S rRNA gene (16S rDNA) from the
DNA fraction of a sample. In contrast, to reveal the active species, the expressed copies of
the 16S rRNA gene (16S rRNA), i.e. the ribosomes, are sequenced from the RNA fraction.
In this context, in order to characterize the prokaryote communities, we amplified and
sequenced a ca. 400 bp long portion of the 16S rRNA gene containing both conserved and
variable regions.
42
The sequence reads obtained from sequencing centers were first subjected to quality
control using the Mothur software (Schloss et al., 2009). During this step, adapters,
barcodes and primers were removed from the sequence reads, and the quality of base-calls
was assessed in order to remove erroneous reads from the data set. Subsequently, chimeric
sequence reads, which are a type of sequencing artifact arising from sequences from
separate sources fusing into one, were removed from the data set with the USEARCH
algorithm (Edgar, 2010) by de novo detection and through similarity searches against the
Greengenes reference database (DeSantis et al., 2006).
Operational Taxonomic Units (OTUs), which are groups of similar sequences, were
detected from the chimera-filtered sequence data set following the open-reference OTUpicking protocol of QIIME (Caporaso et al., 2010). First, all reads that failed to hit the
Greengenes reference database (DeSantis et al., 2006) with a minimum of 60% identity
threshold were discarded as sequencing error. Subsequently, closed-reference OTUs were
picked at 97% clustering identity against the Greengenes database, and de novo OTUs
were picked from a randomly subsampled sequence subset that failed the closed-reference
OTU-picking stage. Next, singleton OTUs, i.e. OTUs that were represented by a single
sequence, were filtered from the data set. Finally, taxonomy from the domain-level up to
species-level was assigned to OTUs via representative OTU sequences with the RDP
classifier algorithm at minimum of 80% confidence (Wang et al., 2007).
2.6.2 Fungal ITS
The genes of small subunit (SSU, 18S rRNA, equivalent to bacterial and archaeal 16S
rRNA) and large subunit (LSU, 28S rRNA) in fungi are separated by an internal
transcribed spacer (ITS) region, which is transcribed but spliced away before assembly of
the ribosomes. The ITS region is composed of two highly variable spacers ITS1 and ITS2
and the intercalary 5.8S rRNA gene. The use of ITS region as a molecular target results in
better detection of fungi than if targeting SSU rRNA (Lindahl et. al., 2013). Because
ITS does not code for ribosomal components it is highly variable and even closely related
species differ in sequence. At the same time intraspecific variation is relatively low.
Reference databases have also better representation of fungal ITS sequences compared to
SSU. Because fungal ITS doesn´t code for ribosomal components the RNA fraction of
fungal ITS region is only present in the cell when genomic copy is being actively
transcribed and it differs from prokaryotic 16S rRNA which is incorporated into
ribosomes, and is thus present in 100s of copies.
The raw fungal ITS sequence data was analyzed as above, except that the reference
database was UNITE (Kõljalg et al., 2013), and the taxonomic assignments were made by
the BLAST algorithm with a maximum E-value of 0.001 (Altschul et al., 1990).
2.6.3 dsrB and mcrA genes and transcripts
The dsrB and mcrA genes are specific marker genes for sulphate reducers and
methanogens, respectively, coding key genes for both processes. These genes have been
presented in detail in Bomberg et al. (2010). Sequence data obtained from DNA and RNA
for both genes were first quality screened using Mothur as described above. Chimeras were
removed using the usearch61 algorithm in QIIME (Caporaso et al., 2010) using the dsrB
sequence reads as reference for the dsrB chimera search and the mcrA sequence reads as
43
reference for the mcrA chimera search, after which the chimeric sequeces were removed.
Taxonomy assignments were performed using uclust in QIIME towards custom designed
reference databases for dsrB and mcrA sequences designed in this project, respectively.
Sequences, which did not obtain a taxonomy assignment were checked manually, and if no
sequence identity was obtained by using the blastn or blastx tools on the NCBI service
(www. http://blast.ncbi.nlm.nih.gov/Blast.cgi), these sequences were removed from the
data set.
2.6.4 Metabolic predictions
Based on the archaeal and bacterial 16S-derived taxonomic community profiles, utilizing
the program PICRUSt (Langille et al., 2013), we studied the predicted share of genes
related to carbon fixation and methane, nitrogen, and sulfur metabolism in the samples.
PICRUSt is a computational approach to the prediction of potential functions of microbial
communities, which links taxonomic assignments of marker genes, such as 16S, to
phylogenetically nearest sequenced reference genomes, from which total metagenomes and
their function are extrapolated. PICRUSt has demonstratedly captured key findings of
shotgun sequencing-based studies from 16S data (Langille et al., 2013). However, the
approach becomes less reliable when there are no evolutionary close reference genomes for
the taxonomic assignments, which is a typical case for novel environmental samples.
Likewise, the predictive approach of PICRUSt cannot account for common phenomena
such as horizontal gene transfer.
2.6.5 Heatmap generation
The biom tables, which are part of the output of the open-reference OTU-picking protocol
of QIIME (Caporaso et al., 2010), were summarized by taxonomic ranks from phylum to
species-level. These rank-specific tables were read into the R environment (R
Development Core Team, 2008), and heatmaps were generated utilizing the reshape2 data
reshaping package (Wickham, 2007), the grid graphics package (Murrell, 2005), and
ggplot2 data visualization package (Wickham, 2009).
2.7 Real-time quantitative PCR (qPCR)
The abundances of bacterial 16S rRNA genes and genes and transcripts of the dsrB, amoA,
narG and mcrA (sulphate reducers, ammonia oxidizers, nitrate reducers and methanogens,
respectively) were estimated by real-time quantitative PCR (qPCR). The qPCRs for the
dsrB, amoA, narG and mcrA were performed as described for the dsrB and mcrA genes in
Bomberg et al. (2012).
In order to estimate the quantity of bacteria in the groundwater samples 16S rRNA gene
targeted qPCR was used in parallel to the cell counts by microscopy (see above). For the
bacterial 16S rRNA 1 μl of 10-fold diluted DNA extract (corresponding to 1 mL
groundwater μL-1 DNA) was used as template. The qPCR was performed with KAPA™
SYBR® Fast 2× Master mix for Roche LightCycler 480 (Kapa Biosystems, Inc., Boston,
MA, USA) and triple reactions were performed for each sample. The amplifications were
performed with primers P1 and P2 (Muyzer et al., 1993), which specifically target the
sequences flanking the V3 region of the bacterial 16S rRNA gene. Each reaction contained
44
3 pmol of each primer. The qPCR program was as described for the dsrB, mcrA, amoA
and narG targeted qPCRs above, except that the annealing temperature was 57ºC.
Bacterial genomes generally contain several copies of the ribosomal genes in their
genomes, which renders 16S rRNA gene targeted qPCR biased towards a higher number of
microorganisms than the actual cell number accounts for. In order to bypass this problem
the obtained 16S rRNA gene copy number of each sample was divided by the
corresponding TNC value. The variation ranged between 0.8 to 8.5 times higher values by
qPCR than by microscopy. An average correction coefficient was calculated from 11
Olkiluoto groundwater samples for which both the TNC value obtained by microscopy and
the 16S copy number obtained by qPCR was available. The average correction coefficient
was calculated to be 3.86. Each 16S rRNA gene qPCR value was hence divided by this
factor in order to convert the 16S rRNA gene copy number into an approximate for cell
numbers.
45
3 RESULTS
3.1 Total Number of Cells (TNC)
The number of microbial cells mL-1groundwater as calculated by microscopy varied
between 4.2 × 105 cells mL-1at 296 m depth to 2.3 × 104 mL-1at 798 m depth (Figure 9).
The corrected 16S rRNA qPCR showed the 1.3 × 106 mL-1at 296 m depth to 6.9 × 103 mL1
at 330 m depth (Table 2).
The cell numbers obtained from two samples examined by flow cytometry (OL-KR3/381m
and OL-KR9/564m) were at the same level than the cell numbers examined by microscopy
of samples from similar depths (Figure 9). The cell number estimated by qPCR of the
microbial 16S rRNA gene had only weak similarity to the cell number obtained by
microscopy. The number after correction by the correction coefficient 2.2 (see chapter 2.7)
was either higher or lower than the numbers obtained by microscopy or flow cytometry.
This is due to the fact that different microbial groups contain a different number of copies
for their ribosomal genes in their genomes. A cell in the stage of division (multiplication)
may have several copies of its chromosome. Lastly, the 16S rRNA genes of different
microbial groups are amplified at varying efficiency in the PCR. However, the results of
the qPCR are mostly within the same order of magnitude as those obtained by microscopy
or flow cytometry. The microscopy and flow cytometry results show a tendency that the
microbial numbers decrease with the depth. Above 405 m the microbial numbers are over
105 cells mL-1 whereas below 405 m the numbers are under 105 cells mL-1.
0
TNC and 16S rRNA qPCR
50
100
x 10000
150
200
300
Depth (m)
400
500
600
700
800
900
TNC microscopy
TNC flow cytometry
16S qPCR DNA
Figure 9. Total number of microbial cells mL-1 in the samples from depths between 296 m
and 798 m were determined by epifluorescence microscopy or flow cytometry and also
estimated by 16S rRNA gene qPCR (corrected values) of the DNA extracts. The error bars
represent standard error of mean (SEM).
46
Table 2. Microbial cell numbers in the groundwater samples determined by microscopy or
flow cytometry and for comparison, total bacterial counts analyzed by qPCR for bacterial
16S rRNA gene. (*cell numbers obtained with flow cytometry, na = not analysed)
Sample ID
OL-KR13/360_10
OL-KR13/360_12
OL-KR3/339_12
OL-KR20/410_13
OL-KR6/422_10
OL-KR6/422_13
OL-KR25/357_11
OL-KR3/381_11
OL-KR23/425_09
OL-KR46/471_13
OL-KR46/493_13
OL-KR5/457_12
OL-KR49/532_09
OL-KR9/468_11
OL-KR9/565_11
OL-KR2/597_10
OL-KR1/609_10
OL-KR44/766_13
OL-KR29/801_10
Sampling date
9.3.2010
21.8.2012
21.8.2012
20-21.8.2013
18.5.2010
20.8.2013
31.10-1.11.2011
29-31.8.2011
15.12.2009
15.1.2013
12.3.2013
16.10.2012
14.12.2009
31.10-1.11.2011
29-31.8.2011
27.1.2010
26.1.2010
15.1.2013
18.5.2010
Vertical depth
(m)
Cell number (cells mL-1)
Direct counts of microorganisms (Microscopy or
flow cytometry)
-296
-296
-303
-323
-328
-330
-330
-340
-347
-372
-390
-405
-415
-423
-510
-559
-572
-693
-798
4.2×105
1.1×106
1.6×105
2.0×105
1.0×105
1.0×105
na
2.4×105 *
2.5×105
1.1×105
2.2×105
2.1×105
1.5×104
na
2.9×104 *
5.9×104
8.7×104
5.5×104
2.3×104
Bacterial
counts
(16S
rDNA
qPCR corr.)
1.3×106
2.7×105
7.8×103
1.2×104
6.3×104
6.9×103
2.8×105
1.7×104
6.6×105
5.3×104
6.7×104
2.2×104
2.1×104
1.3×105
2.8×105
2.4×105
3.4×104
1.4×104
2.7×104
3.2 Number of sulphate reducers, ammonium oxidisers, denitrifiers and methanogens
determined by qPCR
The number of sulphate reducing bacteria (SRB), ammonium oxidisers, denitrifying
bacteria and methanogenic archaea were analysed based on quantification of the marker
genes dsrB, amoA, narG and mcrA in the microbial biomass respectively based on DNA
and RNA extractions. DNA is expected to describe the whole community and RNA the
active population of the community.
Sulphate reducing bacteria were detected from all the examined samples, except the
deepest sample OL-KR29/801m (Table 3, Figure 10A). The highest number of SRB
determined by the marker gene dsrB based on DNA analysis was 7.2×104 mL-1 in OLKR25/357m. The higher counts were detected more often in upper depths than in deeper
depths (Figure 10A). Active SRB counts determined by RNA extracted from the filtered
microbial biomass were found in 12/19 samples. The active counts were at the highest
4.6×102 mL-1 in 330 m bsl (OL-KR6/422m). The ratio of active dsrB containing (RNA)
communities to the total dsrB containing (DNA) communities were less than 0.4 except for
OL-KR5/457m where the ratio of RNA and DNA fractions was 0.9 (Figure 10B) in those
samples that were found to contain dsrB gene. The ratio of sulphate reducing bacteria
found compared to total cell number (Figure 10B) was higher than with other marker genes
studied in samples from the OL-KR23/425m, OL-KR13/362m and OL-KR6/422m (0.110.22).
47
Ammonium oxidisers were detected by DNA based amoA gene targeted qPCR. Primers used
targeted generally beta- and gammaproteobacterial amoA genes. Ammonia oxidisers were
found only in 6/19 samples and with low numbers (≤ 70 mL-1). All ammonia oxidisers were
found from the samples above 400 m. However, there were samples above this depth that did
not contain ammonia oxidisers. Active ammonium oxidisers were not found in any of the
samples (Figure 10A, Table 3). The ratio of ammonia oxidisers found compared to total cell
number (Figure 10B) was very low (≤ 0.003).
Table 3. Copy number of bacterial marker genes for sulphate reducing bacteria,
methanogens, ammonium oxidisers and denitrifiers (dsrB, mcrA, amoA and narG marker
genes, respectively) analysed by qPCR, based on DNA and RNA extractions representing
total and active communities in the groundwater samples. (bd = below detection, numbers
in brackets show Standard error of mean)
Vertical
depth (m)
Sample ID
OLKR13/360_10
OLKR13/360_12
OLKR3/339_12
OLKR20/410_13
OLKR6/422_10
OLKR6/422_13
OLKR25/357_11
OLKR3/381_11
OLKR23/425_09
OLKR46/471_13
OLKR46/493_13
OLKR5/457_12
OLKR49/532_09
OLKR9/468_11
OLKR9/565_11
OLKR2/597_10
OLKR1/609_10
OLKR44/766_13
OLKR29/801_10
-296
-296
-303
-323
-328
-330
-330
-340
-347
-372
Sulphate reducers
dsrB
Methanogens
mcrA
DNA
RNA
DNA
6.3×104
(3.0×103)
5.8x103
(1.3x103)
8.6×102
(3.0×102)
7.3x103
(3.1x103)
1.1×104
(1.5×103)
4.1x103
(4.9x103)
7.2x104
(1.6x103)
1.3x104
(9.0x102)
5.5×104
(5.3×103)
1.4x102
(0.2x101)
bd
bd
bd
0.9x101
(0.5x101)
1.5x102
(7.7x101)
2.3x102
(1.5x102)
3.9×102
(4.3×102)
4.6x102
(3.1x102)
1.4x102
(3.8x101)
3.0x102
(2.0x102)
1.5×102
3.3x102
(4.4x102)
1.4×101
(0.8×101)
3.0x103
(1.1x103)
bd
bd
Ammonium
oxidisers
amoA
RNA
DNA
copy number mL-1
bd
3.8x102
(1.6x102)
bd
RNA
DNA
bd
bd
7.0x101
(1.6x101)
0.1x101
(0)
1.1x101
(0.4x101)
bd
bd
bd
5.9x101
(0.4x101)
4.9x102
(2.8x102)
2.2x101
(0.8x101)
1.6x103
(4.5x102)
0.3x101
(0)
1.2x103
(3.3x102)
2.2x101
(0.2x101)
2.2x101
(0.3x101)
6.6x101
(0.4x101)
7.2x101
(2.1x101)
0.2x101
(0)
bd
bd
bd
1.6x102
(6.1x101)
1.1x101
0.3x101
bd
bd
0.7x101
(0.4x101)
bd
bd
bd
bd
bd
bd
bd
bd
4.3x101
(0.7x101)
1.0x101
(0.6x101)
bd
0.1x101
(0)
bd
bd
Denitrifiers
narG
bd
RNA
bd
bd
bd
bd
bd
bd
bd
bd
bd
-390
9.5x102
(7.7x102)
bd
6.5x102
(4.0x102)
bd
1.7x101
(0.8x101)
bd
3.0x103
(1.8x103)
0.2x101
(0.1x101)
-405
2.5x102
(3.2x101)
2.2x102
(3.0x101)
0.6x101
(0.3x101)
0.6x101
(0.3x101)
bd
bd
3.9x102
(9.3x102)
6.8x101
(3.3x101)
-415
1.5×102
(3.6×101)
bd
bd
bd
bd
bd
1.9x102
(1.4x101)
bd
-423
4.1x104
(6.7x102)
6.7x102
(1.2x101)
1.7×101
(1.1×101)
1.9×103
(4.6×102)
0.8x101
(0.3x101)
bd
bd
1.3x101
(0.1x101)
2.2×101
(0.8x100)
bd
bd
bd
bd
bd
bd
bd
bd
bd
bd
bd
bd
bd
bd
bd
0.4x101
(0.3x101)
bd
0.6x101
(0.4x101)
bd
bd
bd
bd
bd
5.6x101
(0.5x101)
7.1x101
(1.0x101
2.0x102
(0.8x101)
4.5x102
(2.3x101)
1.7x102
(1.2x102)
2.5x101
(0.2x101)
-510
-559
-572
-693
-798
1.6x101
(1.6x101)
bd
1.9×101
(1.4x101)
bd
bd
bd
bd
bd
1.9x101
(1.2x101)
bd
48
Copies of dsrB ml-1
A
10
1000
200
300
300
400
400
700
DNA
800
10
100
1000
B
10000
200
200
300
300
400
400
0,01
600
600
700
DNA
800
cDNA
700
800
ratio DNA/Cells
900
900
0
Copies of narG ml-1
10
1000
B
300
300
400
400
Depth (m)
200
200
500
600
Ratios narG
0,0
0,2
0,4
500
600
700
700
900
0,005
500
500
800
Ratios amoA
0
Depth (m)
Depth (m)
1
ratio RNA/DNA
900
Copies of amoA ml-1
0
ratio DNA/cells
800
cDNA
900
A
1,0
600
600
A
0,5
500
500
700
Depth (m)
0,0
100000
200
Depth (m)
Depth (m)
0
Ratios dsrB
B
DNA
cDNA
800
900
ratio DNA/Cells
ratio RNA/DNA
Figure 10. A) The number of sulphate reducers, ammonium oxidisers and denitrifying bacteria
from the dsrB, amoA and narG gene targeted qPCR. The error bars represent standard error of
mean (SEM) B) The ratio of target dsrB, amoA and narG qPCR gene counts (DNA) and total cell
counts and the ratio of targeted qPCR counts from DNA and RNA fractions.
49
A
0
Copies of mcrA ml-1
1
10
100
1000
10000
B
0
200
300
300
400
400
Depth (m)
200
Depth (m)
500
600
700
800
900
Ratios mcrA
0,5
1
1,5
500
600
700
DNA
cDNA
800
900
ratio DNA/cells
ratio RNA/DNA
Figure 1. A) The number of methanogens from the mcrA gene targeted qPCR of the DNA
fractions and from RNA transcripts. The error bars represent standard error of mean
(SEM) B) The ratio of target mcrA qPCR gene counts (DNA) and total cell counts and the
ratio of targeted qPCR counts from DNA and RNA fractions.
Denitrifying bacteria were found from all samples. The number of denitrifiers was mainly
low varying from 3.0×103 (OL-KR46/493m) to 3 mL-1 (OL-KR6/422m). Active denitrifiers
were found in 4/19 of samples with low counts (2 to 68 mL-1) and the active population
was lower than the general DNA based narG gene amount found in these samples. There
was no dependence between the depth and the amount of denitrifying bacteria. The ratio of
denitrifying bacteria found compared to total cell number (Figure 10B) was low (< 0.08).
Microbes have two alternative nitrate reductases (Nar and Nap) for nitrate reduction to
nitrite. However Nap genes have been identified only in some Alfa-, Beta- and
Gammaproteobacteria (Philippot, 2002; Van Spanning et al., 2005).
Methanogens were detected by DNA based mcrA gene targeted qPCR from over half of the
samples (11/19, Table 3). The number of methanogens was between 0 and 3.0×103 mL-1.
There were more often a little higher counts of mcrA-genes found in depths above -400 m
than below it. Based on RNA extractions, only in three samples active methanogens were
detected. However, the ratio of active methanogens (RNA) compared to methanogens
detected based on DNA was higher than with other studied marker genes (ratio 0.1 to 1.4,
Figure 11B). The ratio of methanogens found compared to total cell number (Figure 11B)
was low (< 0.02).
3.3 Bacterial diversity
The diversity of the microbial communities was studied based on RNA extractions
providing information of active microbial communities and DNA fractions showing the
total bacterial groups in the bedrock aquifers in Olkiluoto. Bacterial 16S rRNA gene
sequences were obtained from all the samples. A total of 534,425 sequence reads were
obtained from 19 drillholes. The sequence count median was 8,795 with a range from
2,855 sequences in OL-KR46/471m 16S rRNA sample to 55,594 sequences in OL-
50
KR2/597m 16S rRNA sample (Appendix J). 4,528 different operational taxonomic units
(OTUs) belonging to 47 different phyla or candidate divisions, 127 classes, 218 orders, 343
families, 551 genera and 616 species (Appendix Q) were detected with minimum of two
sequences per OTU at 97% clustering identity. From the 4,528 bacterial OTUs 83.7% were
assigned to bacterial phyla meaning that 16.3% of the OTUs remained as unidentified
bacteria. The number of OTUs in different samples ranged from 86 (OL-KR44/766m
RNA) to 954 (OL-KR2/597m RNA).
In order to compare the bacterial diversity of the samples, data normalisation was applied
by subsampling 2,817 sequences (the lowest sequence count in a sample without sequences
found only once in all sequence data) from each sample (Appendix J). Alpha diversity was
assessed in all data and normalised data by the following metrics: OTU count, Chao1
richness estimate, Shannon index and also Minimum species count in normalised data. In
normalised data the diversity estimate (Chao1) and the OTU counts were congruent in both
DNA and RNA samples (Figure 12). The lowest coverage (46.9%) was obtained from OLKR13/360m (2010) RNA sample and the highest (88.2%) in OL-KR46/471m RNA
sample. In general, between 55.4−85.0% and 46.9−88.2% of the Chao1 estimated OTUs
were detected (Appendix J) in total and active communities, respectively. The Shannon
diversity index takes into account both the number of different OTUs but also their
evenness. When comparing all sequence data and normalised data the sample diversities
(Shannon index) are the same or a little higher in all data analysis. Diversities in
normalised data were mostly congruent in DNA and RNA fractions, but in both OL-KR46
samples (471m and 493m) RNA fraction diversity indexes were around two units higher
than in DNA fractions (Figure 12). The highest diversity was observed in OL-KR29/801m
DNA sample (5.7) and the lowest in OL-KR23/425m RNA sample (0.9). In four samples
the diversity was higher in RNA fraction than in DNA fraction (OL-KR46/471m and
493m, OL-KR/9565m, OL-KR2/597m). The diversity of OL-KR13/360m increased
between two samplings 2010 and 2012 noticeably (DNA fraction from 4.1 to 5.5 and RNA
fraction from 2.9 to 4.7), whereas the diversity of OL-KR6/422m sampled in 2010 and
2013 decreased (DNA fraction from 4.4 to 1.2 and RNA fraction from 3.6 to 1.8)
considerably. The diversities of OL-KR46 and OL-KR9 sampled at two depths at the same
year did not differ considerably between depths (Appendix J).
51
Figure 2. Number of observed operational taxonomic units (OTU), estimated number of
OTUs (Chao1) and Shannon diversity index of the total (DNA, on the left) and active
(RNA, on the right) bacterial communities in the Olkiluoto groundwater samples as
determined by high throughput sequencing. The number of OTUs and Chao1 estimated
number of OTUs are given on the upper X-axis, and the value for the Shannon diversity
index on the lower X-axis.
3.3.1 Overview of the dominant bacterial classes
The analysis includes all the bacterial 16S rRNA sequences. Different types of
proteobacterial lineages were the most prominent bacteria in the total and active
populations according to the detected 16S rRNA sequences in the samples (Figure 13). The
relative abundance of different proteobacterial classes varied with depth (Appendix C).
Alphaproteobacteria were present and active at all depths, but were especially prominent at
OL-KR9 at the both studied depths, where they contributed 40 to 50% of the bacterial
sequence reads from the total and active bacterial communities. Betaproteobacteria were
also present both in the total and active communities at all depths. The highest percentage
of Betaproteobacteria was found in sample OL-KR6/422m from 2013 (>70%). The change
compared to the same sampling point earlier was significant as earlier less than one percent
of the sequences were identified as Betaproteobacteria. Gammaproteobacteria were present
52
in almost all samples. They formed over 60% of the population in deep samples of 572 and
693 m bsl (OL-KR1/609m and OL-KR44/766m). In addition at the depth of 415 m bsl over
70% of the active population and almost half of the total population were
Gammaproteobacteria (OL-KR49/532m). Deltaproteobacteria were abundant and active at
and above depth 423 m bsl. Especially at the depths of 323 m (OL-KR20/410m) and 330 m
(OL-KR25/357m) bsl the Deltaproteobacteria dominated the active population (> 70%).
Epsilonproteobacteria were found regularly at more shallow depths. They contributed with
90 % of the active population and over 70% of the total population at 347 m bsl (OLKR23/425m) and also at 296 m bsl (OLKR13/360m, 2012) over 50% of the both
populations were Epsilonproteobacteria.
In addition to Proteobacteria there were two other phyla that dominated in certain samples
(Figure 13). Actinobacteria formed over 50% of the active population in OL-KR2/597m
and OL-KR29/801m. Another deep sample 693 m bsl (OL-KR44/766m) had also high
portion of Actinobacteria in the active population (30%). Bacteroidetes were the most
abundant total community phylum in OL-KR46 at both depths (372 m and 390 m bsl) (5070%), however their portion was not that high in the active community (6−14%). Other
notable observation was that Nitrospirae were abundant in OL-KR3/381m both in the
active and total population (36% and 27%). Overall Nitrospirae could be found at low
abundances at depths over 415 m bsl but not deeper than that. Firmicutes were found from
all depths and they contributed with a third of the active population in the deepest sample
(OL-KR29/801m). Spirochaetes were found from almost all samples and they were
especially abundant in OL-KR3/381m active (27%) and in total (36%) communities. There
were also sequences grouped into “Bacteria”. These sequences could not be identified into
any particular phylum but were identified as bacterial sequences. This bacterial group was
most abundant in the total community of OL-KR13/360m from the year 2012 and OLKR6/422m from the year 2013, 23% and 27%, respectively.
53
Figure 3. Taxonomic classification heatmap of the bacterial sequence reads obtained by high throughput sequencing of the
total (DNA) and active (RNA) bacterial communities presented at phylum-level. The samples are arranged1 by true vertical
depth from the surface down. The different colours represent relative abundance (%) of sequence reads. Blue 1boxes with the
d-letter describe taxa represented by less than 0.1 % the sequence reads of that sample, black boxes display absence.
54
Figure 14. Taxonomic classification heatmap of the total (DNA) and active (RNA) alpha- and betaproteobacterial communities in the
Olkiluoto groundwater samples as determined by high throughput sequencing of PCR amplified 16S rRNA fragments. The colouring
of the heatmap as in Fig 13.
55
Figure 15. Taxonomic classification heatmap of the total (DNA) and active (RNA) delta-, epsilon and gammaproteobacterial communities in
the Olkiluoto groundwater samples as determined by high through-put sequencing of PCR amplified 16S rRNA 1fragments The colouring of
the heatmap as in Fig 13.
56
Figure 16. Taxonomic classification heatmap of the total (DNA) and active (RNA) communities from predominant families of
different phyla in the Olkiluoto groundwater samples as determined by high throughput sequencing of PCR amplified 16S rRNA
fragments except for proteobacteria. The colouring of the heatmap as in Fig 13.
57
3.3.2 Proteobacteria
The Phyllobacteriaceae was the most dominating alphaproteobacterial family and was most
abundant in the samples from OL-KR9, both depths (468m and 565m) where they
constituted 30% and 36% of the total and 20% and 22% of the active communities in
samples OL-KR9/468m and OL-KR9/565m, respectively (Figure 14). Another
alphaproteobacterial family Caulobacteraceae was found from all active populations except
for OL-KR6/422m (2013). This family was most abundant in OL-KR9/468 and OLKR9/565m (9.4% and 12.4 %, respectively) active communities. Both of these families are
often described as aerobic and chemoorganotrophic. Some species are able to use nitrate as
electron donor instead of oxygen (Garrity et al., 2005a; Mergaert and Swings, 2005). The
third most abundant alphaproteobacterial family, Sphingomonadaceae was found from all
other depths except for OL-KR6/422m (2013). More than 10% of the sequences of the
active community were Sphingomonadaceae in OL-KR46/471m and OL-KR13/360m.
These microbes are described as aerobic chemoheterotrophs able to degrade aromatic
compounds (Yabuuchi and Kosako, 2005).
Betaproteobacteria were present in the total and active population in all samples, but were
especially abundant in OL-KR6/422m (2013) (Figure 14). However in another sample
from the same drillhole in 2010 almost no betaproteobacterial sequences were detected.
The population from the 2013 sample consisted mostly of Hydrogenophaga species (86%
and 73%, total and active community, respectively). Hydrogenophaga spp. belongs to
family Comamonadacea, which includes chemoroganotrophic or chemolithotrophic
aerobic families. Some of the genera are able to use nitrate as an electron donor.
Hydrogenophaga spp. are able to fix N2 and CO2 (Willems and Gillis, 2005). There was
another betaproteobacterial family Rhodocyclaceae found from OL-KR13/360m (2012) in
high amounts (12% and 9%) in active and total community. The Rhodocyclaceae genera
are diverse with fermentative metabolism and ability to fix N2 (Garrity et al., 2005c). In the
OL-KR13/360m (2013) the main genus found was Sulfuritalea which is an anaerobic and
facultatively autotrophic species, able to use sulphur, thiosulphate and hydrogen as
electron donor and nitrate as an electron acceptor (Kojima and Fukui, 2011). There was
also some Betaproteobacteria of the order Methylophilales found in OL-KR3/339m,
especially in the active community (12%). These bacteria oxidise methanol as an energy
source and for growth. They do not use methane (Garrity et al., 2005b).
Gammaproteobacteria were the most abundant bacterial group in the total bacterial
community detected at 415, 572 and 693 m bsl, and were also the most active in these
samples (Figure 15). Most of these sequences belonged to family Pesudomonadaceae,
which includes mostly aerobic and chemoorganotropic microbes that have minimal
requests for the growth media. Some species are able to use nitrate as electron acceptor and
they use simple organic compound as a carbon source (Garrity et al., 2005). Especially in
the OL-KR49/532m, OL-KR44/766m and OL-KR1/609m samples in both active and total
community, a predominant species had similarity to Pseudomonas stutzeri. This genus is
an exception in this family as some of its species can fix N2 (Garrity et al., 2005). Another
gammaproteobacterial order Methylococcales and its family Crenotrichaceae was found
from OL-KR46/471m and 493m. These bacteria are microaerophilic and use methane as
electron donor (Bowman, 2005).
58
Deltaproteobacteria were found in all samples and often at high abundance between 296 m
and 423 m bsl (Figure 15). They were especially abundant in the total and active
community in OL-KR3 at depths (339m and 381m), OL-KR20/410m and OL-KR25/357m
(Figure 15). Deltaproteobacteria formed a bigger part of the active community than of the
total community. The biggest group of Deltaproteobacteria sequencence was similar to
Desulfobacterales order. These bacteria are described as anaerobes having respiratory
metabolism using simple organic compounds as carbon source and electron donors.
Sulphate, sulphite and thiosulphate are used as electron acceptors (SRBs) and some species
use sulphur, polysulphide or nitrate as electron acceptors (Kuever et al., 2005). Two
families of this order, Desulfobacteriaceae and Desulfobulbaceae, were especially
abundant. Family Desulfobacteriaceae genera mostly oxidize organic substrates
completely, whereas genera of the family Desulfobacteriaceae oxidise substrates
incompletely forming acetate (Kuever et al., 2005). In addition to the order
Desufobacterales also Desulfuromonadales order was found especially from OLKR49/471m
and
493m
samples.
Desulfuromonadales
are
anaerobic,
chemolithoheterotrophs or chemoorganotrophs having either respiratory or fermentative
metabolism (Kuever et al., 2005b). Some species may grow by disproportination of
sulphur, thisulphate or sulphite. Family Pelobacteriaceae genera found from OL-KR46
samples are described as fermentative and not being sulphate reducers. They use simple
organic compounds as carbon source (Kuever et al., 2005b).
Epsilonproteobacteria were predominant in OL-KR13/360m (2010) and OL-KR23/425m
and abundant in OL-KR6/422m (2010). In deeper samples (>350 m bsl) the portion of
Epsilonproteobacteria was low (Figure 15). Most of the Epsilonproteobacteria were similar
to family Helicobacteraceae and especially Sulfurimonas and Sulfuricurvum genera.
Sulfuricurvum species have been found to dominate microbial communities in a Canadian
oil sand reservoirs containing severely biodegraded oil (Hubert et al., 2012), groundwater
in Japan (Kodama and Watanabe, 2004), where they utilize sulphide, elemental sulphur,
thiosulphate and hydrogen as the electron donors and nitrate as the electron acceptor under
anaerobic conditions. Another similar epsilonproteobacterium, Sulfurimonas gotlandica,
was found to be dominant sulphur and nitrogen cycling member of the microbial
community in the Central Baltic Sea (Grote et al., 2012). Sulfurimonas were also suggested
to be the main CO2 fixing sulphur oxidising lineages in the redoxcline in the Baltic.
3.3.3 Other bacterial phyla
Firmicutes bacteria were present in the total and active communities at all depths (Figure
16/Appendix D). They were most abundantly present in the total community in OLKR5/457m and OL-KR49/532m and in the active community in OL-KR29/801m. The total
community consisted mostly of family Erysipelotrichaceae for which only little is known.
The members of this family are facultatively anaerobic and chemoorganorophic (Ludwig et
al., 2005). Erysipelotrichi 16S rRNA gene sequences have recently been found in sediment
of sinkholes in Lake Huron, which were filled with hypoxic, sulphate-rich groundwater
(Nold et al., 2010). Family Staphylococcaceae was an abundant (27%) part of the active
population in OL-KR29/801m. These species are faculatively anaerobic,
chemoorganotrophic and fermentative (Schleifer and Bell, 2005). Family Clostridiaceae
sequences were found especially from OL-KR3/339m in the active community (5%) and
are described as chemoorganotrophic or chemolithotrophic, anaerobic and fermentative.
59
Some species fix N2 (Wiegel, 2005). Family Peptococcaceae sequences were found in most
of the samples in low amounts. The most abundant genus Desulfosporosinus is an
anaerobic and sulphate reducing (Ezaki, 2005; Hippe and Stackerbrandt, 2005).
The relative portion of Actinobacteria generally increased at greater depths (Figure
16/Appendix E). The most common Actinomycetales order families were
Microbacteriacea, Micrococcaceae, Nocardioidaceae, and Propionibacteriaceae.
Microbacteria sequences were abundant in OL-KR2/597m in total and active community
(>40%). This family includes species that are facultatively anaerobic and
chemoorganotrophic (Evtushenko, 2009). The genus mostly found from OL-KR29/801m
was Microbacterium. Some Microbacterium species have shown chemolithotrophic
growth coupled with thiosulphate oxidation. Some species have reduced toxic Cr(VI)
under anaerobic condition using acetate as an electron donor (Suzuki and Hamada, 2009).
Some Microbacteria species are reported to survive extreme condition and were found
from 12 000 year old glacial ice (Christner, 2000) or in desert ecosystems including the
Atacama Desert (Osman et al., 2008). In OL-KR44/766m family Nocardioidaceae was
abundant both in total (14%) and active (29%) community. Nocardioidaceae genera are
chemoorganotrohic and have respiratory type of metabolism with flexibility. They use
several carbon and nitrogen sources including toxic environmental pollutants, and possess
a wide spectrum of enzymatic activities. The members of this family can survive
environmental hazards, including desiccation, low and high temperatures, oxygen radicals,
UV damage and toxic compounds (Evtushenko and Ariskina, 2009). The family
Micrococcaceae was abundant in OL-KR29/801m especially in active community (22%).
The genus Micrococcus belonging to Micrococcaceae is aerobic, chemoorganotrophic and
do not have special growth requirements (Busse, 2009). The family Propionibacteriacea
and especially genus Propionibacterium was found from OL-KR29/801m active
community (14%) but not in total community. These bacteria are described as
chemoorganotrophic, having fermentative metabolism that produces propionic and acetic
acids. Most of the species have complex nutritional requirements (Patrick and McDowell,
2009). The family Nocardiacea formed a minor portion of the sequences in OL-KR9/565m
(6%) and OL-KR2/597m (5%) in active community. The genus Rhodococcus was the main
genus and it is described as chemoorganotrophic, aerobic and having oxidative and diverse
metabolism (Jones and Goodfellow, 2009).
Bacteroidetes were dominant in OL-KR46/471m and 493m total communities (51% and
72%). Bacteroidetes were present and active in all samples (Figure 16/Appendix F).
However there was a big difference between active and total communities as the active
community was considerable smaller. Only in three samples (OL-KR46/471m, OLKR46/493m, OL-KR9/565m) more than 5% of the sequences were Bacteroidetes in active
community. Most of the sequences were similar to the order Flavobacteriales and family
Flavobacteriaceae. The family Flavobacteriaceae is described as anaerobic,
chemoorganotrophic and having fermentative metabolism. They use nitrate and nitrite as
electron donors (Bernardet, 2010).
Spirochaetes were found in all total community and all other samples except for OLKR3/339m in the active community. Spirochaetes were more abundant at shallow depths
and especially in OL-KR13/360m (2010) where it formed 32% of the active and 9% of
total community (Figure 5/Appendix G). Spricohaetes are chemoheterotrophic, motile with
60
most having periplasmic flagella (Paster, 2010). High sequence similarity was found to
Spirohaetes sequences from low-temperature oil reservoir in Canada (Grabowski et al.,
2005) and anoxic river sediment in Germany (Kittelman and Friedrich, 2008).
Nitrospirae were found also from shallow depths and no deeper than 415m bsl (Figure
16/Appendix G. In OL-KR3/381m a high abundance of family Thermodesulfovibrionaceae sequences were found both in active (27%) and in total (36%) community. In
other samples the amounts of Nitrospirae were less than 4%. Thermodesulfovibrionaceae
genera have been found from methanogenic thermophilic wastewater sludge (Sekiguchi et
al., 2008) and freshwater hydrothermal sites (Sonne-Hansen and Ahring, 1999). The
species are anaerobic, use organic compounds as a carbon source and are able to use
hydrogen, formate, pyruvate and lactate as electron donors and sulphate, thiosulphate and
Fe(III) as electron acceptor, so being sulphate reducing bacteria (SRB). Some species also
use sulphite as electron donor and nitrate as electron acceptor as well as use
hydrogenotrophic methanogens as electron accepting system (Sekiguchi et al., 2008).
Elusimicrobia were present (Figure 16/Appendix G) mainly in shallow samples at low
abundance (<1%). In OL-KR6/422m (2010) there were 7% and 4% of Elusimicrobia
sequences in active and total communities, respectively. In addition in OL-KR46/471m
there was 3% of Elusimicrobia sequences in the active community. Elusimicrobia were
originally called Termite Group 1 as they were regularly encounterd in termite hindgut but
are present also in many other habitats (Herlemann et al., 2009). Sequences similar to
order Elusimicrobiales sequences found in Olkiluoto have been found in acidic uraniumcontaminated aquifer (Reardon et al., 2004) and Great Artesian Basin in Australia
(Spanevello and Patel, 2004 – genebank submission).
Chloroflexi were found from almost all samples at low abundance (Figure 16/Appendix F)
(≤2%). The representatives of Chloroflexi phylum were divided mostly into two orders,
Anaerolineae and Dehalococoidetes, which both were frequently present in the samples.
The known Anaerolineae species are filamentous strict anaerobes and have been isolated
from different types of anaerobic sludge and digesters, but are also often found in sediment
(Blazejak and Schippers, 2010). Dehalococcoidetes sequences have been detected from
South Pacific deep sediment (Durbin and Teske, 2011) and deep sea ridge flank crustal
fluids (Huber et al., 2006).
Clorobi bacteria were found in low amounts (≤1%) from most of the samples (Figure
16/Appendix F). In OL-KR20/410m active and total community a slightly higher
abundance of Clorobi (2%) were detected. These bacterial sequences had similarity to new
moderately thermophilic, facultatively anaerobic chemoorganotrophic species isolated
from a microbial mat developing on the wooden surface of a chute under the flow of hot
water (46°C) coming out of a 2,775-m-deep oil exploration well. It grows on mono-, di- or
polysaccharides by aerobic respiration, fermentation or by reducing diverse electron
acceptors, such as nitrite, Fe(III) or As(V) (Podosokorskaya et al., 2013).
Bacteria belonging to different Candidate Divisions (CD) were found in most of the
samples, but generally at low abundance and often more in the DNA fractions (Figure
16/Appendix H). The CD group OD1 was present in the total community at all other
depths except for OL-KR46/471m and in less than half of the active community samples.
61
OD1 bacteria have been detected from deep sea continental shelf sediment (Jong and Cho,
2012) and Antarctica lake (depth 367m) water (Karlow et al., 2011). CD group WS3
sequences were found only sporadically in samples but in OL-KR6/422m (2010) there was
10% in active community. WS3 sequences have been detected in oil polluted beach
sediment in Spain (Acosta-Gonzalez et al., 2013) and Baltic Sea redoxcline at depth 119m
(Glaubitz et al., 2013 – genebank submission). CD group OP9 was found in most of the
samples at low abundance (Figure 16/Appendix H). In OL-KR25/357m and OLKR1/609m, 4% of the sequences belonged to OP9 in total community. Similar sequences
have been found from a variety of deep sea environments like Mediterranean mud volcano
(Heijs et al., 2004 – genebank submission) and methane hydrate bearing subseafloor
sediment at the Peru margin (Inagaki et al., 2006 – genebank submission). CD group TM7
was found more often in total community samples than in active community. TM7
sequences tended to be more frequently present in deeper samples. These sequences have
been found from very diverse environments, such as foaming activated sludge (Wagner
and Cloete, 2002), Antarctic lake ice cover (Mosier et al., 2007) and radioactive site
ground water (Nedelkova, 2003).
Planctomycetes were present in many samples at low abundance (<1%) and in the active
community only in OL-KR13/360m (2010) where they contributed with little over 1%
(Figure 16/Appendix G). These sequences resembled sequences previously found from
deep sea hydrothermal region of the East Lau Spreading Centre (Dong and Shao, 2009)
and sediment bacteria from the southern Cretan margin, Eastern Mediterranean Sea
(Polymenakou et al., 2009).
The phylum Thermi sequences were found only in few samples (Figure 16). In the deepest
sample OL-KR29/801m 3% of the sequences belonged to family Deinococcaceae in the
active community. The Deinococcus genera are known to include many species that show
remarkable resistance to radiation. They are also aerobic and chemoorganotrobic, having
respiratory metabolism (Battista and Rainey, 2001).
Main characteristics of most of the above mentioned taxa can be found in Appendix I. In
addition to these 15 phyla or candidate divisions described above representatives of 32
other phyla or candidate divisions were detected in the samples. The amount of these
sequences was minor but they illustrate the potential of the present diversity to adjust if the
conditions change and offer better possibilities for bacteria with alternative features. In
addition sequences were detected that were classified as “Bacteria” but obtained no further
identification as the sequence quality was not good enough or the information on the
reference database is not sufficient to identify them even though the database is
continuously updated.
3.3.4 Metabolic predictions
The potential share of genes dedicated to carbon fixation, methane metabolism, nitrogen
metabolism, and sulphur metabolism was extrapolated from the 16S-assigned taxonomy,
based on the genomic content of the nearest sequenced phylogenetic relatives of the
detected OTUs. The extrapolation did not reveal considerable differences between samples
in relative share of genes dedicated to different energy metabolisms (Figure 17). The share
of the genes associated with carbon fixation pathway was highest in most of the samples.
62
In both OL-KR3 samples (339m and 381m) and OL-KR2/597m in total and active
community the methane metabolism associated predicted gene share was higher than that
associated with carbon fixation. In OL-KR46/471m, the total community had more genes
associated with nitrogen metabolism than with carbon or methane metabolism. In all
samples the share of sulphur metabolism associated genes was lowest. Keeping in mind the
level of incompleteness of the taxonomic classifications, these observations might simply
represent data artefacts.
Figure 8. Relative abundance of functions in the groundwater samples predicted by
PICRUST from known closely related sequenced genomes on the basis of bacterial
taxonomic groups detected in the water sample. The samples are arranged by true vertical
depth from the surface down and are grouped by molecule (DNA, RNA). The colouring of
the heatmap indicate the abundance of the predicted function in each sample according to
the gradient bar on the right.
Sulphate reducing bacteria were detected with high abundance (>15%) from the depth of
296m bsl to 510m bsl in active community. The sulphate concentrations were over 1 mg/l
in these samples. There were three exceptions in SRB occurrence in the active community:
OL-KR13/360m (2010), OL-KR23/425m and OL-KR49/532m, that had only 1% to 5% of
SRB. These three samples had sulphate concentration in similar level as in samples with
high amount of SRB (1.4 to 80 mg L-1). In two of these samples (OL-KR13/360m (2010)
63
and OL-KR23/425m) the sulphur compounds were probably partly metabolised by the
Epsilonproteobacteria families that were predominant bacteria in these samples (52% and
90%) and able to oxidise sulphur compounds. However, the inorganic sulphate source is
likely a bigger source. There is a clear trend that the SRB bacteria are found in high
abundance when the sulphate level is elevated. However, there are exceptions to that and
the amount of SRB may be low even the sulphate concentration is high (OL-KR20/410m).
There was no clear trend between sulphate concentration and the abundance of known
SRB. In addition the qPCR results of dsrB gene abundance did not seem to follow directly
the sulphate concentration. Bacteria oxidising sulphur compounds in Epsilonproteobacteria (Sulfuricurvum and
Sulfurimonas) and the genus Sulfuritalea in Betaproteobacteria were found in high
abundances in samples OL-KR13/360 from years 2010 and 2012, OL-KR6/422m from the
year 2010 and in OL-KR23/425m. In all these four samples the water was mixed. This
water mixing may provide some compound that the sulphur oxidising bacteria exploit
effectively. However, the mixing effect did not affect the bacterial diversity similarly in
other samples. There was no trend between sulphate or total sulphur concentrations and the
abundance of these three known sulphur oxidising bacterial genera. This indicates that
there are other variables that affect the abundance.
The nitrate concentration was below the detection limit in most of the samples. Only in
OL-KR5/457m a low concentration could be measured close to the detection limit. The
concentration of narG transcripts (participating in nitrate reduction) was highest in this
sample, which may be a true connection, indicating active bacterial nitrate reduction at this
depth. On the other hand it may be a coincidence, which is supported by the Figure 17,
where no particularly active nitrogen metabolism was discovered. There was no trend
found between the abundance of known nitrate reducers and the concentration of total
nitrogen or ammonium. However, the abundance of family Flavobacteriacea showed some
trend with the higher concentrations of ammonium. In addition the concentration of narG
genes from total community showed some similarity to the ammonium concentration. The
amoA gene (participating in ammonium oxidation) did not show any trend with the
measured ammonium levels.
3.3.5 The influence of sampling time and sampling depth
The OL-KR6/422m samples taken 2010 and 2013 differed from each other significally in
abundance of different taxonomic groups. In sample OL-KR6/422m from 2010,
approximately 23% of the obtained sequences belonged to both delta- and
epsilonproteobacteria (Appendix C) and in addition high amounts of unclassified bacterial
sequences (27%) were detected in the total community. In the active community the
portion of delta- and epsilonproteobacterial sequences was higher (33%) and the
unclassified bacterial sequences were missing. By 2013 the situation in OL-KR6/422m
had changed and 86% and 75% of the sequences were betaproteobacterial and mainly
belonged to genus, Hydrogenophaga in total and active communities, respectively. The
diversity had decreased significantly between 2010 and 2013 (Figure 12). The same
Hydrogenophaga genus was detected in 2010 at less than 0.1% abundance.
Hydrogenophaga species are known to be facultatively autotrophic, i.e., they oxidise
hydrogen to power carbon fixation only when organic carbon is unavailable (Willems et
64
al., 1989) and they are also able to fix N2 (Willems and Gillis, 2005a). They have been
identified as likely inhabitants of transition zones where hydrogen-enriched subsurface
fluids mix with oxygenated surface water. Hydrogenophaga species are most likely aerobic
or facultatively anaerobic hydrogen-oxidising chemolithoautotrophs (Brazelton et al.,
2013). There was a pumping experiment going on between the samplings in OLKR6/422m, and the water was mixed. This probably has partly induced this shift in
bacterial diversity. However, this change does not show clearly in the chemical data
(Appendix B).
The OL-KR13/360m was sampled in 2010 and 2012. During this time the bacterial
portions changed considerably (Figure 13/Appendix C). The OL-KR13 water was mixed
but has been recovered since 2008 as it has been packered. The amount of
epsilonproteobacterial sequences decreased from 56% to 2% and the relative abundance of
unclassified bacterial sequences, delta- and betaproteobacterial sequences increased. In the
active community the epsilonprotebacterial sequence amounts also decreased (52% to 2%)
and so did also the amount of spirochaete sequences (32% to 0.2%). The increase in
relative abundances 15%, 21%, 27% (percentage point), was seen especially in alpha-,
beta- and gammaproteobacterial portions, respectively. However also the portions of most
other bacterial OTUs detected 2010 increased their portion in 2012. The change that
happened between the samplings did possible not favour the bacteria with increased
portions but more likely weakened the conditions for the Epsilonproteobacteria and
Spirochaetes bacteria. The amount of sulphide decreased from 5.1 to 0.04 mg/l, which may
explain the decrease of sulphur, sulphide and thiosulphate oxidising Epsilonproteobacteria.
The role of Spirochaetes is not well known in deep subsurface environments and for this
reason it is difficult to assess the causes for this decreased abundance. Comparison of the
chemical data show that in addition to the decrease in the concentration of sulphide the
concentration of total nitrogen increased from 0.71 to 4.4 mg/l between the samplings.
Spirochaetes have been shown to fix N2 (Lilburn et la., 2001) but there may be no relation
between the amount of total nitrogen in water and the amount Spirochaetes.
OL-KR46 was sampled from two depths, 471m and 493m, in 2013. OL-KR46 water has
been drawn down earlier as the drillhole has been open. Both depths had abundant
Bacteroides sequences in the total community (52% and 72%) and less in the active
community (6% and 14%). The difference between the sampling depths was seen
especially in the active community as the distribution of Proteobacteria was very different
in these two samples (Appendix C). The deeper sample had abundant
gammaproteobacterial Pseudomonaceae sequences (47%), whereas the upper sample had
17% and 19% (percentage point) more alpha- and deltaproteobacterial sequences,
respectively. The chemical data shows that in both samples the concentrations of especially
sulphate, total sulphur and ammonium were higher compared to all other drillhole samples.
Between these two samples are however several differences in chemical data making it
difficult to assess reasons for the changes of bacterial relative abundances. For example the
concentration of sulphate is high in both samples (498 and 736 mg/l) and the amount of
sulphate reducing bacteria (Deltaproteobacteria) is lower with the higher sulphate
concentration.
OL-KR9 was sampled at depths of 468m and 565m in 2011. The bacterial profiles of the
total community were quite similar with only minor changes in proteobacterial relations
65
(Appendix C). In the active community the portion of deltaproteobacterial sequences
diminished from 32% to 0.1%. This was compensated with increase in the relative
abundances of Gammaprotobacteria (2% to 15%), Alphaproteobacteria (40% to 51%) and
Actinobacteria (4% to 13%) relative abundances, in the total and active communities,
respectively. The chemical data shows that the sulphate concentration is different (13.7 and
0.9 mg L-1), which may explain the difference of sulphate reducing Deltaproteobacteria.
This difference in bacterial and chemical data is natural as the water has not been mixed in
drillhole.
OL-KR3 was sampled at 339m and 381m in 2012 and 2011, respectively. In the upper
sample Delta- and Betaproteobacteria were the most abundant taxa in the total and active
communities. The deeper samples had similar Deltaprotobacteria abundance than the upper
samples but in addition sulphate reducing Nitrospirae sequences (Thermodesulfovibrionaceae) were found. The total relative abundance of SRB in the deeper samples was
71% and 81% in total and active communities, respectively. In the deeper sample the
amount of especially Betaproteobacteria decreased. Again the chemical data shows that the
amount of sulphate increases (from 1.8 to 32 mg/l) together with higher portion of SRBs in
samples. The deeper sample was slightly mixed and the upper sample was not mixed.
Samples OL-KR29/801m, OL-KR1/609m, OL-KR2/597m and OL-KR5/457m belonged to
same fracture zone HZ21. This did not affect the sample taxonomy as the samples differed
a lot on their predominant bacterial phyla (Figure 13).
3.4 Archaeal diversity
Archaeal 16S rRNA gene and transcript sequences were obtained from all samples. In
total, 379,520 high quality sequence reads were assigned into archaeal OTUs. Sequence
count median was 10,405 with a range from 573 sequences in the OL-KR2/597m DNA
sample to 24,002 sequences in the OL-KR6/422m RNA sample from 2010 (Appendix K).
Large variance exceeding an order of magnitude was also observed in OTU counts, from
12 OTUs in the OL-KR46/471m DNA fraction and OL-KR29/801m RNA fraction-derived
communities to 345 OTUs in the OL-KR46/493m DNA fraction-derived community. In
total, 1,700 unique archaeal OTUs with a minimum of two sequences per OTU at 97%
clustering identity were observed in the data set. Taxonomic classification of the OTUs
revealed a minimum of 17 classes, 33 orders, 44 families, and 53 genera. However, a large
fraction of the observed OTUs could only be classified at higher levels of taxonomy.
In order to compare the archaeal diversity of the sampling sites, data normalization was
applied by subsampling 1,500 sequences from each sample (Appendix K). Beta diversity
(Whittaker, 1960) was assessed by the following metrics: OTU count, minimum species
count, Chao1 richness estimate (Chao, 1984), and Shannon index (Shannon, 1948).
Overall, the diversity estimates of the sampling instances were congruent between the 16S
rDNA and 16S rRNA fraction-derived populations, indicating that the active (16S rRNA)
and total (16S rDNA) archaeal communities were comparably diverse (Appendix K).
However, surprisingly in the majority of the sampling instances, the 16S rRNA fractionderived community was more diverse than its DNA counterpart.
66
Based on the analysis, the OL-KR13/360m sample from 2012 and the OL-KR46/493m
sample from 2013 were particularly diverse (Figure 18). The most homogenous
communities were inferred from the DNA fraction of OL-KR3/381m sampled in 2011 (7
OTUs) and the 16S rRNA fraction of the deepest sample, OL-KR29/801m (3 OTUs).
These samples were also the least diverse in the original (non-subsampled) data set with 12
OTUs in both samples (Appendix K). Sampling depth did not seem to predict archaeal
diversity in the samples. Instead, chemical parameters, especially Alk, DIC, and Si
(Appendix B), seemed to correlate with diversity, although the statistical significance of
this putative positive correlation was not assessed.
Figure 18. Number of observed operational taxonomic units (OTU), estimated number of
OTUs (Chao1) and Shannon diversity index of the total (DNA, on the left) and active
(RNA, on the right) archaeal communities in the Olkiluoto groundwater samples as
determined by high throughput sequencing. The number of OTUs and Chao1 estimated
number of OTUs are given on the upper X-axis, and the value for the Shannon diversity
index on the lower X-axis.
In ideal cases, OTUs got taxonomic assignments on each taxonomic level. However, in
practice, only a fraction of the archaeal OTUs could be classified at lower taxonomic levels
such as family, genus and species. Comprehensive species-level classification was possible
for only six out of the 1,700 OTUs. However, almost 62% of the OTUs got species-level hits
against unclassified reference sequences. At the genus-level, 8.7% of the OTUs were
classified into known genera and additional 53.5% got hits against unclassified references.
At the family-level, 33.2% of the OTUs were classified, and other 31.8% got hits against
67
unclassified references. At the order-level, 66.1% of the OTUs were classified, and
additional 2.8% got hits against unclassified references. At the class-level, almost 72% of the
OTUs were classified. However, the remaining 28% of the OTUs could not be classified at
this level. Finally, at the phylum-level, 83% of the OTUs got a classification. However, still
even at the phylum-level, which is just one level below the highest level of taxonomy
(domain), almost one out of five OTUs remained without classification or a hit to an
unclassified reference (Appendix Q). In the context of known archaeal 16S rRNA gene
diversity, these results strikingly demonstrate the uniqueness of the archaea in the OL-KR
samples, and highlight the level of uncertainty at which further conclusions are made.
3.4.1 Overview of the dominant archaeal taxa
Euryarchaeota was the most abundant archaeal phylum in all but one sampling site, OLKR46/471m, where Crenarchaeota were the most numerous (Figure 19). Euryarchaeota
were especially dominant in the total communities (DNA fractions), representing less than
80% of the archaeal community only in the OL-KR/46 samples. Euryarchaeota also
dominated the active communities, although their share was notably smaller especially at
the OL-KR3 and OL-KR9 sites, and the 2012 OL-KR13/360m sample.
From the identified Euryarchaeotal classes; ANME-1 (Anaerobic Methanotrophic Archaea
Group 1), Archaeoglobi, DSEG (Deep Sea Euryarchaeotic Group), Methanobacteria,
Methanococci,
Methanomicrobia,
and
Thermoplasmata,
the
methanogenic
Methanomicrobia, and the generally acidophilic Thermoplasmata were particularly
abundant and present at all sampling depths (Figure 20). DSEG, which are often found, as
their name suggests, from deep sea sediments, were near absent in the total communities,
but very common in the active counterparts, especially in both samples of OL-KR3 (24%
in the upper, 44.4% in the lower), the OL-KR6/422m sample from 2013 (40.1%), and OLKR5/457m (84.2%) and OL-KR9/468m (18.2%) samples. The methanogenic
Methanobacteria, which are common in sediments and other low-oxygen environments,
were almost absent from the upper samples, but abundant in both total and active
communities at depths of 415m and below (Figure 20).
At the order-level, the majority of the dominating Methanomicrobia were classified as
Methanosarcinales (Kendal et al., 2006), ANME-2D family (Figure 21). Recently, a
representative of this family, Methanoperedens nitroreducens, was shown able to perform
nitrate-driven anaerobic oxidation of methane without a partner organism through reverse
methanogenesis with nitrate functioning as the terminal electron acceptor (Haroon et al.,
2013). Another well-represented Methanomicrobial order was likewise CO2-reducing
Methanomicrobiales (Boone et al., 2001), which were very abundant in OL-KR20/410m
(Figure 21). These OTUs were further classified belonging to the Methanoregulaceae
family and the acidophilic Candidatus Methanoregula genus, which has a representative
that uses H2 and CO2 as substrates for methanogenesis (Bräuer et al., 2006).
Methanomicrobia belonging to the F99a103 clade, which was originally isolated from a
nascent hydrothermal chimney, were abundant in the deeper samples, especially in OLKR49/532m and OL-KR9/565m (Figure 21).
The majority of the Thermoplasmata, which were present at all sampling sites, often in
high numbers (Figure 20), belonged to the E2 clade, which is commonly associated with
68
anoxic environments, and includes methanogens (Yashiro et al., 2011). At the family-level,
the vast majority of the Thermoplasmata were classified belonging to TMEG (Terrestrial
Miscellaneous Euryarchaeotal Group), although E2 family Methanomassiliicoccaceae,
which includes a methanol-reducing hydrogenotrophic representative (Iino et al., 2013),
was also present in the majority of the samples, reaching as high abundance as 7.7% of the
total archaeal community of OL-KR3/339m and 5.8% of the active community of OLKR9/468m (Figure 21).
The majority of the DSEG class representatives grouped within ArcA07 clade (Figure 21),
which were first detected from a hypersaline endoevaporitic microbial mat (Sørensen et al.,
2005). Another common Euryarchaeal family was Methanobacteriaceae (Figure 21), which
belongs to the Methanobacteriales order, which are generally hydrogenotrophic and use H2
to reduce CO2 to CH4 (Bonin et al., 2006). The presence of the novel OTUs belonging to this
family seemed to correlate with depth, as they were very abundant in 3 of the 4 deepest
samples, only missing from OL-KR29/801m. They were especially dominant in OLKR2/609m, where they represented 79.4% of the total community and 95.1% of the active
community (Figure 21). Still another Methanobacteriales group that seemed to occur more in
the deeper samples was MSBL1 (Mediterranean Sea Brine Lake group 1), which are thought
to be involved in methanogenesis (van der Wielen et al., 2005). These OTUs further grouped
with SAGMEG1 (South Africa Gold Mine Euryarchaeal Group 1), which were originally
detected in deep South African gold mines (Takai et al., 2001), but were later found from
also deep marine sediments containing methane hydrates (Teske et al., 2008).
Notably, Euryarchaeota that could not be classified into any particular class were present in
almost all samples, representing as much as 89.1% of the total community of the 2010 OLKR13/360m sample and 28% of the corresponding active community (Figure 21). These
OTUs were also common in the total communities of OL-KR6/422m (52.5%), OLKR25/357m (22%), OL-KR23/425m (48.9%), and OL-KR46/493m (9.2%). Additionally,
these OTUs made a significant contribution to the active community of the OLKR25/357m (33.8%) sample.
Crenarchaeota were near universal, being only absent from the deepest OL-KR29/801m
sample. Crenarchaeota were also absent from the total communities of the deep OLKR2/597m and OL-KR44/766m samples. Crenarchaeota were especially abundant in the
samples from OL-KR46, the OL-KR13/360m sample from 2012, the total communities of
OL-KR3, OL-KR49/532m and OL-KR5/457m, samples, and the active communities of
both depths of OL-KR9 (Figure 19). At the class-level, four Crenarchaeota clades; MBGA
(Marine Benthic Group A), MBGB (Marine Benthic Group B), MCG (Miscellaneous
Crenarchaeota Group), and Thaumarchaeota were identified, however, with the exception
of the two latter, the classes were rare, representing no more than 0.2% of the archaeal
sequence reads of any particular sample (Figure 20).
MCG, which are globally distributed and especially common in anoxic low-energy
subsurface sediments (Kubo et al., 2012), were the dominant Crenarchaeotal class, only
absent from the deepest OL-KR29/801m sample. In the context of the active communities,
MCG were particularly abundant in the samples from OL-KR3 and OL-KR46 drillholes, in
the 2012 sample of OL-KR13/360m, and in the OL-KR5/457m and OL-KR49/532m
samples. The highest abundance of MCG was detected in the OL-KR46/471m sample were
69
they represented 77.1% of the total archaeal community. In regard to the active
communities, MCG were more abundant in both samples from OL-KR13, in the OLKR3/339m sample, and particularly, in the samples of OL-KR46 and OL-KR9 sites, both
depths. As with the total communities, the highest abundance of MCG in the active
communities was seen in the OL-KR46/471m sample, were they represented 61.6% of the
active archaeal community (Figure 20). At the order-level, the majority of the MCG
grouped with unclassified environmental samples (Figure 21). The most prevalent
classified MCG order was the nonthermophilic pGrfC26, which was originally recovered
from a temperate marsh environment, and was later detected in rice roots and other various
locations (Groβkopf et al., 1998). The sequences belonging to pGrfC26-like OTUs seemed
to be more abundant closer to the surface.
Thaumarchaeota, which are often regarded to as a phylum instead of a Crenarchaeotal
class, and contain characteristically chemolithoautotrophic ammonia-oxidizing archaea
(Brochier-Armanet et al., 2012; Pester et al., 2011), were present in considerable
abundance in the OL-KR13/360m sample from 2012, and in the total community of the
OL-KR46/471m sample (Figure 20). All detected Thaumarchaeota grouped with the
mesophilic and psychrophilic Cenarchaeales order (Cavalier-Smith, 2002). The
Thaumarchaeota of the OL-KR13/360m sample from 2012 were further classified
belonging into the Cenarchaeaceae family. Also the Thaumarchaeota that were abundant in
the total community of OL-KR46/471m (12.2%), belonged to the Cenarchaeaceae family.
In their case, genus-level classification suggested Nitrosopumilus (Figure 21).
Representatives of this genus are known to occur in oligotrophic seawaters, include some
of the smallest known free-living organisms, and can oxidize ammonia to nitrate at levels
as low as 10 nanomolar (Könneke et al., 2005).
Parvarchaeota is a novel uncultured archaeal group detected mostly in acidic environments
(Juottonen et al., 2008; Baker et al., 2010; Amaral-Zettler et al., 2011), and also in weak
alkaline deep subsurface hot springs (Murakami et al., 2012), and has been assigned a
phylum status in Greengenes taxonomy in congruence with recent research concerning the
diversity of uncultivated microbes (Rinke et al., 2013). With the exception of the deepest
sample, OL-KR29/801m, Parvarchaeota were detected at all the sampling sites. However,
Parvarchaeota were absent from the total communities of the OL-KR44/766m and OLKR9/565m samples. Parvarchaeota were especially numerous in the OL-KR3/339m
sample and at the OL-KR46 sites, and in the total communities of the OL-KR13/360m
samples, and in the active communities of the OL-KR6/422m (2013), OL-KR25/357m,
OL-KR3/381m, and OL-KR9/468m samples (Figure 19). In essence, with the exception of
their distinct phylogeny, nothing is yet known about the physiology of the two detected
Parvarchaeal classes; Micrarchaea and Parvarchaea. From these clades, the latter was fairly
common in the OL-KR13/360m sample from 2012, in both samples from OL-KR3 and
OL-KR46 drillholes, and in the active community of the OL-KR9/565m sample (Figure
21). In the active communities, Parvarchaeal clades seemed to occur together in larger
numbers with DSEG and/or Thermoplasmata. Previously, from 3D cryo-transmission
electron microscopy of biofilms from Richmond Mine, it was observed that unspecified
Thermoplasmata penetrate Parvachaeal cell walls by protuberances (Baker et al., 2010).
However, the nature (symbiosis, parasitism) and purpose of this interspecies interaction
remain unknown (Baker et al., 2010).
70
Sequences that grouped into OTUs that represented archaea, but could not be classified
into phyla due to the lack of highly similar reference sequences, were detected at all sites
except, interestingly, the two deepest locations (Figure 19). These currently unclassifiable
and novel OTUs were especially abundant in the OL-KR46/493m sample, representing ca.
6.5% of both the total and active communities. Overall, it appeared that on the phylumlevel, the two deepest samples were the least diverse. Another observed general trend was
that Crenarchaeota and Parvarchaeota were more likely to occur together at higher relative
abundances. Generally, the archaeal phyla were distributed more evenly in the samples
where also higher alpha diversity was observed (Figure 18; Figure 19). Characteristics of
the main characterized archaeal taxa are summarized in Appendix P.
Figure 19. Taxonomic classification heatmap of the archaeal sequence reads obtained by
high throughput sequencing of the total (DNA) and active (RNA) archaeal communities
presented at the phylum-level. The samples are arranged by true vertical depth from the
surface down. “Other” category is assumed polyphyletic and refers to novel lineages at
the displayed level of taxonomic rank. The colouring of the heatmap as in Fig 13.
71
Figure 20. Taxonomic classification heatmap of the Archaeal OTUs at the class-level. The
samples are arranged by true vertical depth from the surface down and are grouped by
molecule (16S rDNA, 16S rRNA). The letter d signifies the relative share of more than 0%
but less than 0.1%. The black color marks complete absence. “Other” categories are
assumed polyphyletic and refer to novel lineages at the displayed level of taxonomic rank.
Unsuccessful classifications at this level are not shown.
72
Figure 21. Taxonomic classification heatmap of the Archaeal OTUs up to genus-level. The samples are arranged by true vertical depth from the
surface down and are grouped by molecule (16S DNA, 16S rRNA). The letter d signifies the relative share of more than 0% but less than 0.1%. The
black color marks complete absence. “Other” and “Unclassified” categories are assumed polyphyletic and refer to novel lineages and unclassified
environmental OTUs at the displayed level of taxonomic rank, respectively.
73
3.4.2 Metabolic predictions
Extrapolating from the 16S-assigned taxonomy, we studied the potential share of genes
dedicated to carbon fixation, methane metabolism, nitrogen metabolism, and sulphur
metabolism, based on the genomic content of the nearest sequenced phylogenetic relatives
of the detected OTUs. In this context, one clear archaeal trend was observed, i.e., the
relative share of genes dedicated to methane metabolism was clearly larger in the deeper
sampling sites (Figure 22). An exception to this trend was the OL-KR20/410m site.
Keeping in mind the level of incompleteness of the taxonomic classifications, these
observations might simply represent data artefacts.
Figure 22. Metabolic prediction heat map based on the sequenced genomes of the nearest
relatives of the Archaeal OTUs. The samples are arranged by true vertical depth from the
surface down and are grouped by molecule (DNA, RNA).
74
3.5 Fungal diversity
Fungi are mainly decomposers playing a major role in the biodegradation of plant
materials in terrestrial ecosystems but fungal diversity and their role in deep biosphere
environments remains still largely unknown. Fungal diversity has recently been reported
for example in deepsea extreme environments (Nagano and Nagahama, 2012). Possible
roles of fungi in deepsea environments are formation of humic aggregates and carbon
contribution and production of extracellular enzymes involved in the cycling of nutrients
(Raghukumar et. al., 2010).
The diversity of the fungal communities was studied based on DNA fractions presenting
the total fungal community and RNA fractions providing information of the active fungal
communities in the bedrock aquifers in Olkiluoto. Fungal ITS region gene sequences were
obtained from all samples from both DNA and RNA fractions, except for OL-KR44/766m
where a PCR product was successfully amplified and sequenced only from the DNA
fraction.
With high throughput sequencing of fungal ITS gene region with 454 technology
altogether 378,831 fungal ITS sequences belonging to fungal OTUs were obtained. The
number of sequence obtained from each sample ranged from 306 sequences from the RNA
fraction of OL-KR25/357m and 24,616 sequences from the RNA fraction of OLKR9/468m (Appendix L). The mean number of sequences obtained per sample was 10,941
sequences. Fungal ITS sequences belonged to 967 fungal OTUs ranging from 7 OTUs in
the OL-KR3/339m (RNA) to 163 OTUs in the OL-KR9/468m (DNA). These fungal OTUs
were classified to 19 fungal classes, 38 orders, 59 families, 76 genera and 104 species.
From the 967 fungal OTUs 71 % were assigned to fungal phyla meaning that 29% of the
OTUs remained as unidentified fungi. In the fungal class level the 69%, order level 68%,
family level 66%, genera level 63% and species level 64% of the OTUs were assigned to
fungal taxa (Appendix Q).
In order to compare the fungal diversity of the samples, data normalisation was applied by
subsampling 1,581 sequences from each sample (Appendix L, the lowest sequence count in
a sample without singletons in all sequence data). The number of detected fungal OTUs
was lower compared to bacterial diversity and lower number of fungal OTUs was detected
(Appendix L, Figure 23). In general, between 22.4-87.0% and 15.0-100% of the number
of Chao1 estimated fungal OTUs were obtained from the total (DNA) and active (RNA)
communities in the samples. There was no clear connection between sampling depth and
fungal diversity. Instead some difference in fungal diversity was detected between
individual samples. Highest fungal diversity was detected in OL-KR9/468m where the
highest estimated number of OTUs (128) and Shannon diversity index (4.3) was obtained
in the total fungal community (Figure 23). Lowest fungal diversity in the total fungal
community was detected in OL-KR44/766m where the lowest diversity index (1.2) was
obtained. In the active community the highest OTU number was detected in OLKR20/410m (64) and highest diversity index in OL-KR9/565m (3.5). Highest estimated
number of OTUs was in OL-KR23/425m (247) in the active community. Lowest diversity
in the active community was detected in OL-KR3/339m where lowest number of detected
OTUs (7) and estimated OTUs (7) and low diversity index was detected (0.6). In OLKR13/360m and OL-KR25/357m low diversity index (0.5) was detected in the active
75
community. In addition in OL-KR44/766m no fungal PCR product amplified from the
RNA fraction.
The diversity of OL-KR13/360m decreased between two samplings 2010 and 2012
noticeably (total community from 4.2 to 2.9 and active community from 1.5 to 0.5). The
fungal diversity of the active community of OL-KR6/422m sampled in 2010 and 2013 did
not change and only a minor decrease was noticed in the total community between the
sampling times (Appendix L). The diversity of OL-KR46 differed only in the total
community between the two depths but not in the active communities. In OL-KR9 fungal
diversity was higher in the total community at 423 m than at 510 m, but the diversity of
active community was higher in the upper sample than in the lower one but lower in RNA
fraction.
Figure 23. Number of observed operational taxonomic units (OTU), estimated number of
OTUs (Chao1) and Shannon diversity index of the total (DNA, on the left) and active
(RNA, on the right) fungal communities in the Olkiluoto groundwater samples as
determined by high throughput sequencing. The number of OTUs and Chao1 estimated
number of OTUs are given on the upper X-axis, and the value for the Shannon diversity
index on the lower X-axis. * indicates samples with fewer than 1500 sequences
76
Most of the observed fungal sequences (64%) in the bedrock aquifers in Olkiluoto
belonged to the phylum Ascomycota and phylum Basidiomycota was represented by only
9% of all the sequences. This is similar to deepsea environments where fungi mostly
belong to the phylum Ascomycota with a few yeast species belonging to the phylum
Basiodiomycota (Nagano and Nagahama, 2012). Other fungal phyla detected from were
Chytridiomycota, Glomeromycota and Zygomycota, which were only represented as a
minor (0.1−2.1%) part of the fungal community. From all the ITS sequences obtained,
24.8% were classified as fungal sequences but taxonomy remained unidentified.
DNA
OL−KR13/360m_10d
OL−KR13/360m_12d
OL−KR3/339m_12d
OL−KR20/410m_13d
OL−KR6/422m_10d
OL−KR6/422m_13d
OL−KR25/357m_11d
OL−KR3/381m_11d
OL−KR23/425m_09d
OL−KR46/471m_13d
OL−KR46/493m_13d
OL−KR5/457m_12d
OL−KR49/532m_09d
OL−KR9/468m_11d
OL−KR9/565m_11d
OL−KR2/597m_10d
OL−KR1/609m_10d
OL−KR44/766m_13d
OL−KR29/801m_10d
RNA
OL−KR13/360m_10r
OL−KR13/360m_12r
OL−KR3/339m_12r
OL−KR20/410m_13r
OL−KR6/422m_10r
OL−KR6/422m_13r
OL−KR25/425m_11r
OL−KR3/381m_11r
OL−KR23/425m_09r
OL−KR46/471m_13r
OL−KR46/493m_13r
OL−KR5/457m_12r
OL−KR49/532m_09r
OL−KR9/468m_11r
OL−KR9/565m_11r
OL−KR2/597m_10r
OL−KR1/609m_10r
OL−KR44/766m_13r
OL−KR29/801m_10r
33.2
95.2
51.2
96.0
33.6
98.1
91.9
52.9
76.7
8.0
94.2
20.1
59.3
90.1
9.5
11.5
42.5
32.9
62.3
11.5
0.1
0.4
1.4
d
22.2
0.1
0.1
0.2
d
0.7
d
6.3
6.1
57.9
0.2
0.3
d
d
d
2.9
0.1
66.4
10.5
5.5
d
55.2
4.6
48.4
2.6
66.3
1.9
7.8
24.9
23.2
91.9
4.9
79.9
40.7
0.7
84.3
30.5
57.4
0.7
21.4
d
0.2
0.3
d
d
d
0.1
Abundance
100%
75%
50%
99.9
68.8
100.0
60.2
99.5
94.3
99.3
100.0
94.2
4.6
54.5
99.3
63.1
0.2
79.1
74.0
81.1
0.1
38.7
0.5
5.3
0.7
1.1
25%
0.5
d
d
27.2
98.5
15.5
32.6
As
d
31.2
51.7
co
d
5.8
95.3
45.5
0.7
9.8
1.2
20.9
10.5
18.9
0%
d
15.7
No
Fu
Gl
Zy
Ch
Ba
om
go
ng
sid
y
bla
my
my
i:u
er o
iom tridio
st
n
c
co
m
i
hit
my
ota
de
yc
ta
yc
ota
n
co
ot a
tifi
ta
ed
Figure 24. Taxonomic classification heatmap of the fungal sequence reads obtained by
high throughput sequencing of the total (DNA) and active (RNA) fungal communities
presented at phylum-level. The samples are arranged by true vertical depth from the
surface down. The colouring of the heatmap as in Fig 13.
Fungal communities varied between different samples (Figure 24). Ascomycota were the
major identified fungal phylum in most of the samples. Fungal sequences belonging to the
Basidiomycota phylum were abundant mostly in OL-KR2/597m (58%) and OL-KR3/381m
(22%) in the total fungal community and in OL-KR9/468m (98.5%), OL-KR29/800m
(52%), OL-KR13/360m sample from 2012 (31%), OL-KR49/532m (27%), OL-KR2/597m
(15.5%) in the active fungal community. Fungal sequences belonging to the phylum
Chytridiomycota were only found in OL-KR44/766 m (66%) and in OL-KR29/800m
(10.5%). Zygomycota and Glomeromycota were only represented as a minor part of the
fungal communities in Olkiluoto samples.
77
DNA
OL−KR13/360m_10d
OL−KR13/360m_12d
OL−KR3/339m_12d
OL−KR20/410m_13d
OL−KR6/422m_10d
OL−KR6/422m_13d
OL−KR25/357m_11d
OL−KR3/381m_11d
OL−KR23/425m_09d
OL−KR46/471m_13d
OL−KR46/493m_13d
OL−KR5/457m_12d
OL−KR49/532m_09d
OL−KR9/468m_11d
OL−KR9/565m_11d
OL−KR2/597m_10d
OL−KR1/609m_10d
OL−KR44/766m_13d
OL−KR29/801m_10d
RNA
OL−KR13/360m_10r
OL−KR13/360m_12r
OL−KR3/339m_12r
OL−KR20/410m_13r
OL−KR6/422m_10r
OL−KR6/422m_13r
OL−KR25/357m_11r
OL−KR3/381m_11r
OL−KR23/425m_09r
OL−KR46/471m_13r
OL−KR46/493m_13r
OL−KR5/457m_12r
OL−KR49/532m_09r
OL−KR9/468m_11r
OL−KR9/565m_11r
OL−KR2/597m_10r
OL−KR1/609m_10r
OL−KR44/766m_13r
OL−KR29/801m_10r
23.9
0.1
39.2
7.8
10.9
0.1
18.7
26.4
1.2
1.1
10.4
51.2
0.1
0.4
0.9
83.8
5.2
6.2
0.7
d
26.3
1.5
5.1
d
19.1
3.4
94.7
64.7
d
8.0
52.9
d
d
d
54.1
d
22.5
7.7
d
46.6
4.2
19.8
0.2
d
d
0.1
44.3
2.5
0.8
0.1
0.1
0.2
0.1
66.3
d
0.3
d
23.2
d
d
d
91.9
0.7
d
4.9
d
0.1
0.1
0.1
40.7
6.3
2.9
8.2
0.1
6.1
3.4
37.2
d
22.6
12.5
0.1
0.1
0.3
0.1
30.5
Abundance
100%
57.4
66.4
2.9
d
0.7
84.3
d
0.2
59.5
7.8
24.9
79.9
0.5
10.8
0.8
19.8
32.8
2.6
d
0.2
0.1
6.8
0.2
48.4
1.4
22.2
58.8
6.6
d
4.6
1.9
43.5
35.0
55.2
10.5
0.7
5.5
d
75%
21.4
50%
99.9
68.8
26.0
d
0.1
38.7
1.1
5.2
25%
100.0
60.2
29.6
29.4
79.6
12.6
5.2
94.1
40.5
1.1
0%
0.5
0.9
0.5
5.3
0.7
100.0
60.4
20.7
13.1
4.4
d
54.3
0.2
90.7
d
9.0
1.7
0.2
0.8
46.6
27.3
5.8
d
d
95.3
d
45.5
8.6
d
78.3
d
0.2
47.4
5.0
d
d
0.7
27.2
9.8
98.5
1.2
20.9
15.5
10.5
81.1
18.9
32.6
39.7
12.0
d
15.7
h it
d
d
st
f ie
t if ie
bl a
n ti
en
id e
No
nid
un
i: u
ta :
o
ng
c
y
Fu
ro m
is
s
e
t
ed
me
y ce
es
G lo
r ta
ro m
ce
me
:I n
G lo
o ta
c
d
my
te s
t if ie
go
ce
en
Zy
my
n id
id io
a :u
t
y tr
Ch
yco
s
io m
e te
s id
c
Ba
my
dis
n io
se
te s
c ci
ae
ce
y
Pu
er t
ll o m
In c
ta :
me
Tre
yco
io m te s
s id
e
c
s
Ba
my
e te
c
co
d
ar i
my
f ie
o
Ag
n ti
tr y
id e
bo
un
c ro
ta :
Mi
co
te s
my
ce
co
my
As
a ro
c ch
te s
ce
Sa
y
om
te s
ce
b il i
Or
my
o
de
s
th i
e te
Do
c
y
om
te s
o ti
Le
y ce
om
te s
z iz
Pe
yc e
io m e te s
c
my
Eu
So
ro t
rd a
r io
Figure 25. Taxonomic classification heatmap of the fungal sequence reads obtained by high
throughput sequencing of the total (DNA) and active (RNA) fungal communities presented
at class-level. The samples are arranged by true vertical depth from the surface down. The
colouring of the heatmap as in Fig 13.
DNA
OL−KR13/360m_10d
OL−KR13/360m_12d
OL−KR3/339m_12d
OL−KR20/410m_13d
OL−KR6/422m_10d
OL−KR6/422m_13d
OL−KR25/357m_11d
OL−KR3/381m_11d
OL−KR23/425m_09d
OL−KR46/471m_13d
OL−KR46/493m_13d
OL−KR5/457m_12d
OL−KR49/532m_09d
OL−KR9/468m_11d
OL−KR9/565m_11d
OL−KR2/597m_10d
OL−KR1/609m_10d
OL−KR44/766m_13d
OL−KR29/801m_10d
d
0.1
7.8
10.9
0.1
18.7
26.4
1.2
1.1
10.4
0.4
0.9
83.8
5.2
2.8
0.7
d
26.3
1.4
3.4
94.7
64.7
d
8.0
52.9
d
d
d
54.1
d
22.5
7.6
8.9
34.5
d
d
d
46.6
19.8
1.4
5.1
d
19.1
7.8
d
0.1
6.6
0.8
0.1
d
0.2
d
d
d
d
0.7
d
d
91.9
d
4.9
d
79.9
d
d
0.1
0.8
0.3
23.2
0.1
0.5
10.8
0.1
d
3.4
37.2
22.6
59.4
d
d
19.8
32.7
d
24.9
0.2
0.1
2.5
66.3
4.2
44.3
6.8
2.6
d
22.2
d
0.1
48.4
3.4
0.1
d
0.2
1.9
0.2
58.8
55.2
4.6
51.2
34.9
0.1
d
d
40.7
6.3
d
6.0
2.9
d
12.4
0.1
0.1
8.2
0.1
0.1
0.2
0.3
0.1
30.5
57.4
66.4
2.9
d
0.7
84.3
10.5
0.7
5.5
d
Abundance
100%
75%
21.4
50%
99.9
68.8
26.0
d
0.1
38.7
1.1
5.2
100.0
60.2
29.6
29.4
79.6
12.6
5.2
94.1
40.5
1.1
25%
0%
0.5
0.5
0.5
0.5
5.3
0.7
100.0
60.4
20.7
4.3
d
13.1
d
54.3
5.8
0.2
d
d
0.2
90.7
8.9
d
d
d
0.1
47.4
d
78.3
0.8
46.6
27.3
d
0.7
3.8
1.2
27.2
9.8
d
98.5
1.2
20.9
15.5
10.5
81.1
32.6
d
45.5
8.6
1.7
0.2
95.3
18.9
39.7
12.0
d
15.7
Hy So Mi Op So Ch Eu On Pe He Ca Do Do Ple Or Sa Sa As Sp Ag Bo Ca Ph Ru Ma Fil Tre Pu Ba Rh En Mu Ar Div Gl Gl Fu No
po rda cro hio rda ae rot yg ziz lot pn thi thi os bili cc cc comori ari let nth alla ss las oba me cci sid izo tom co cha er om om ng bl
cre rio as st rio tot ial en ale iale od de de po ale har har y dio ca ale ar le ula se si llo nia iom ph o ral eo sis era ero i:un as
les zia dia my les yc yd pht es sp po les m id t hi
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Figure 26. Taxonomic classification heatmap of the fungal sequence reads obtained by high throughput sequencing of the total
(DNA) and active (RNA) fungal communities presented at order-level. The samples are arranged by true vertical depth from the
surface down. The colouring of the heatmap as in Fig 13.
78
RNA
OL−KR13/360m_10r
OL−KR13/360m_12r
OL−KR3/339m_12r
OL−KR20/410m_13r
OL−KR6/422m_10r
OL−KR6/422m_13r
OL−KR25/357m_11r
OL−KR3/381m_11r
OL−KR23/425m_09r
OL−KR46/471m_13r
OL−KR46/493m_13r
OL−KR5/457m_12r
OL−KR49/532m_09r
OL−KR9/468m_11r
OL−KR9/565m_11r
OL−KR2/597m_10r
OL−KR1/609m_10r
OL−KR44/766m_13r
OL−KR29/801m_10r
23.9
39.2
79
3.5.1 Overview of the dominant fungal taxa
In the identified total fungal community the Ascomycota phylum dominated in almost all
samples. Exceptions were OL-KR2/597m where Basidiomycota species dominated and
OL-KR44/766m where Chytridiomycota dominated. In addition, dominating fungal taxa in
OL-KR/360 m, OL-KR6/422m sample from 2010, OL-KR46/471m, OL-KR/457m, OLKR9/565m, and OL-KR1/609m remained unidentified. In the active fungal community
Ascomycota was also the dominating fungal phylum. Only at OL-KR9/468m and OLKR29/801m Basidiomycota was the major phylum. From the Ascomycota phylum
Sordariomycetes was the major active fungal class in most of the samples and also in half
of the samples in total fungal community. In OL-KR20/410m, OL-KR6/422m sample from
2013 and OL-KR46/493m Eurotiomycetes was the dominating Ascomycota class in the
total fungal community and in OL-KR25/357m in the active fungal community. In OLKR23/425m and OL-KR9/468m Pezizomycetes was the dominating class within the
Ascomycota phylum in the total fungal community and in OL-KR6/422m sample from
2010 and OL-KR1/609m dominating class was Leotiomycetes. In the active fungal
community in OL-KR49/532the major class was Leotiomycetes and fungal class
Dothideomycetes in OL-KR6/422m sample from 2010.
Sordariomycetes were present and active at all depths and were the dominating group of
Ascomycota in most of the depths where they contributed with 24−65% of the fungal
sequences of total and 47−100% of the active fungal communities (Figure 25). Exceptions
were OL-KR20/410m, OL-KR6/422m, OL-KR46/471m, OL-KR9/468m, OL-KR9/565m
and OL-KR1/609 m where Sordariomycetes were the minor fungal group in the total
fungal community (0.9−7.7%) and in OL-KR25/357m, OL-KR46/471m and OLKR49/532m Sordardiomycetes were also minor group in the active community
(4.4−9.0%). Members of the Sordariomycetes are ubiquitous and cosmopolitan and
function in virtually all ecosystems as pathogens and endophytes of plants, arthropod and
mammalian pathogens, mycoparasites, and saprobes involved in decomposition and
nutrient cycling (Zhang et. al. 2006, Timonen and Valkonen, 2013). Sordariomycetes are
one of the most frequently detected fungal taxa also in the deepsea environments within the
phylum Ascomycota together with Eurotiomycetes, Saccharomycetes and
Dothiodeomycetes (Nagano and Nagahama, 2012). However, phylotypes within the class
Sordariomycetes are few and unique to the studied deepsea areas and their role in deep-sea
microbial community is still unknown.
Eurotiomycetes was the dominating fungal class in OL-KR20/410m and OL-KR6/422m
sampled in 2013, with 84−95% of the sequences in the total fungal community belonging
to Eurotiomycetes and also in OL-KR25/357m Eurotiomycetes was the dominating fungal
class in the active fungal community (Figure 25). In OL-KR23/425m and OL-KR9/468m
Pezizomycetes was the dominating fungal class in the total fungal community contributing
with 44−54% of the fungal sequence reads. Other detected Ascomycota classes were
Saccharomycetes, which was found in OL-KR25/357m (19%) and in the OL-KR6/422m
sample from 2010 (5%), Dothiodeomycetes, which dominated the active community in the
OL-KR6/422m sample from 2010 (40.5%) and Leotimycetes, which dominated the active
fungal community in OL-KR49/532m (47%), in the OL-KR6/422m sample from 2010
(26%) and in OL-KR1/609m (23%) in the total fungal community. In addition sequences
80
belonging to class Orbilliomycetes were found but they contributed only with a minor
(1.2%) part of the total fungal community in the OL-KR13/360m sample from 2010.
The fungal sequences belonging to the Sordariomycetes were most similar to members of
the Hypocreales order and Nectriaceae family in all samples (Figure 26). Facultative
anaerobic microscopic fungi that use nitrite and/or nitrate as alternative to oxygen have
been identified within the family Nectriacea (Kurakov et. al. 2008). Most of the OTUs
within the family Nectriaceae were closest to Nectria and Fusarium genera (Figure 27).
Fusarium species have been found in deepsea environments especially from oxygendepleted regions and are capable of denitrification (Jebaraj et al, 2010). Species within
genus Nectria have also been found in deep sea sediments but their role in the ecosystem
remains unknown (Singh et. al., 2012).
The majority of the sequences belonging to the Eurotiomycetes were members of the
Penicillium group, which is known to be globally distributed (Figure 27). Penicillium
species dominated in the total community in OL-KR20/410m and in OL-KR6/422m
sample from 2013. Genus Aspergillus-like fungi were found in OL-KR46/493m and OLKR2/597m as a minor group. Penicillium and Aspergillus are generally common in
deepsea (Nagano and Nagahama, 2012, Raghukumar et. al., 2012) and terrestrial
environments, but those species living in the deepsea appear to be physiologically adapted
to the water environment (Damare et al., 2008). In addition, Penicillium/Aspergillus
species have been found in marine oxygen-deficient environments and they tolerate high
salt concentrations (Raghukumar et. al., 2012). Aspergillus is reported to play an important
role in denitrification process in anaerobic marine sediments (Jebaraj et. al., 2010). This
suggests a possible versatile role of fungi in major ecological processes in the extreme
environments in the deep-sea.
Most of the sequences belonging to the class Dothiodeomycetes were closely related to the
Aureobasidium, Cladosporium and Cercospora genera (Figure 27). Aureobasidium and
Cladosporium genera are often reported in deepsea environments (Nagano and Nagahama,
2012). Common characteristics to these fungal groups are resistance or adaptation to high
osmotic pressure and low temperature, which may be essential for survival in extreme
conditions. Aureobasidium species are frequently detected also from saline waters and are
known salt tolerant fungal species (Nagano and Nagahama, 2012). Aureobasidium species
have been found in osmotically stressed environments, such as on rocks. In terrestrial
environments fungal species within the class Dothideomycetes are often endophytes,
epiphytes, saprotrophs and parasites and can change their life style if environmental
conditions change (Timonen and Valkonen, 2013).
99.9
50%
100.0
d
22.6
19.8
94.1
0.8
46.6
27.3
1.2
3.2
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47.4
1.7
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0.2
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81.1
32.6
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Hy
Figure 27. Taxonomic classification heatmap of the fungal sequence reads obtained by high throughput sequencing of the total (DNA) and
active (RNA) Ascomycota communities presented at genus-level. The samples are arranged by true vertical depth from the surface down.
The colouring of the heatmap as in Fig 13.
81
78.3
8.6
d
90.7
0.2
d
d
d
d
4.3
0.2
0.3
0.1
54.0
d
13.1
20.7
60.4
0.5
0.5
12.6
5.2
1.1
79.6
d
29.4
29.6
75%
d
0%
0.1
60.0
2.1
59.4
0.8
d
0.2
32.7
Abundance
100%
3.4
0.8
0.1
0.1
2.5
6.8
0.1 10.8
44.2
0.5
0.1
19.8
d
0.8
6.6
d
0.2
0.1
d
0.2
58.8
d
0.1
34.9
4.2
45.6 1.0
d
34.5
7.2
1.6
0.2
19.1
d
0.1
d
d
d
6.9
0.7
0.1
d
0.1 54.0
22.5
d
d
d
52.9
3.4
5.1
1.4
0.1
1.7
1.0
5.2
26.3
d
0.7
d
83.7
0.9
8.0
d
0.1
64.6
94.7
0.8
2.6
0.1
6.9
2.1
25%
68.8
100.0
RNA
OL−KR13/360m_10r
OL−KR13/360m_12r
OL−KR3/339m_12r
OL−KR20/410m_13r
OL−KR6/422m_10r
OL−KR6/422m_13r
OL−KR25/357m_11r
OL−KR3/381m_11r
OL−KR23/425m_09r
OL−KR46/471m_13r
OL−KR46/493m_13r
OL−KR5/457m_12r
OL−KR49/532m_09r
OL−KR9/468m_11r
OL−KR9/565m_11r
OL−KR2/597m_10r
OL−KR1/609m_10r
OL−KR44/766m_13r
OL−KR29/801m_10r
37.2
13.9
1.2
0.1
7.8
0.1
d
7.2
16.7
26.4
18.7
10.9
39.2
DNA
OL−KR13/360m_10d
OL−KR13/360m_12d
OL−KR3/339m_12d
OL−KR20/410m_13d
OL−KR6/422m_10d
OL−KR6/422m_13d
OL−KR25/357m_11d
OL−KR3/381m_11d
OL−KR23/425m_09d
OL−KR46/471m_13d
OL−KR46/493m_13d
OL−KR5/457m_12d
OL−KR49/532m_09d
OL−KR9/468m_11d
OL−KR9/565m_11d
OL−KR2/597m_10d
OL−KR1/609m_10d
OL−KR44/766m_13d
OL−KR29/801m_10d
82
Fungi belonging to the Basidiomycota phylum were found as a significant group in OLKR2/597m (60%) and OL-KR3/381m (22%) in the total fungal community and in the
active communities of OL-KR49/468m (98.5%), OL-KR29/801m (52%), in OLKR13/360m sample from 2012 (31%), OL-KR49/532m (27%) and OL-KR2/597m
(15.5%). Microbotryomycetes was the dominating basidiomycotal class, and was present
in the total community of OL-KR2/597m (37%) and the active community of OLKR29/801m (40%) (Figure 25). Class Agaricomycetes was present in OL-KR49/532m
(27%) and at OL-KR13/360m (26%) in the active fungal community and in OL-KR9/565m
(6%) in the total fungal community. Other detected fungal classes within the phylum
Basidiomycota were Tremellomycetes, which was found in OL-KR2/597m (12.5%) in the
total fungal community, Basidiomycota Incertia sedis which was found in OL-KR2/597m
(15.5%) in the active fungal community and Puccinomycetes which was found in OLKR46/493m as a minor fungal class (0.2%). Some of the fungal sequences were identified
as Basidiomycota, but otherwise taxonomy remained unidentified. Unidentified
Basidiomycota species were especially present in OL-KR9/468m (98.5%) in the active
fungal community and in OL-KR3/381m (22%) and in the OL-KR13/360m sample from
2010 (10%) in the total fungal community.
The most common classes of the Basidiomycota detected in the Olkiluoto samples were
Microbotryomycetes and Tremellomycetes, and order Malasseziales that has not been
placed in a specific fungal order (Incertia sedis) (Figure 28). These Basidiomycota are the
most common Basidiomycota groups in the deepsea environments (Nagano and
Nagahama, 2012). In Olkiluoto fungal communities, Malasseziales and Cryptococcus-like
yeasts were found in the active community of OL-KR2/597m (Figure 25). Detected
Malassezia were related to cultured Malassezia sp. LCP-2008, uncultured Malassezia from
deepsea sediments (Singh et. al., 2012; Raghukumar et. al., 2012; Edgcomb et al. 2011),
terrestrial soil environments (Nagano and Nagahama, 2012) and methane hydrate-bearing
deep-sea sediments (Lai et. al., 2007) (Figure 27). Cryptococcus is a major component in
deep-sea environments (Nagano and Nagahama, 2012) and have also been isolated from
deep igneous rock aquifers of the Fennoscandian shield (Ekendahl et. al., 2003) and
deepsea methane seeps (Takishita et. al., 2006; Takishita et. al., 2007). Such
methylotrophic yeasts may play a crucial role in converting methane into more accessible
carbon and energy substrates (In OL-KR2/597m CH4 386 mL L-1).
Sporidiobolales and Erytrobasidiales (class Microbotryomycetes and Cystobasidiomycetes)
are typical yeast orders in deepsea environments (Nagano and Nagahama, 2012).
Sporidiobolales was also the dominant order within the class Microbotryomycetes in the
Olkiluoto samples (Figure 28). Within order Sporidiobolales closest fungal genera found in
OL-KR29/801 m and OL-KR2/597 m samples were Rhodotorula and Sporobolomyces
genera. The genus Rhodotorula is composed of anamorphic heterobasidiomycetous yeasts
and comprises both microscopic and macroscopic species. Rhodotorula species have been
identified in deepsea sediments and with culture-depended methods also in deep igneous
rock aquifers of the Fennoscandian shield (Ekendahl et. al., 2003). The Rhodotorula strains
from Fennoscandian rock aquifers were able to grow over a wide range of NaCl (0–70 g
NaCl L-1) and Rhodotorula minuta J3 was even able to grow with salt concentration of 0–
100 g NaCl L-1. Growth was observed at pH interval of 4–10, so salt concentration and pH
are not a limiting factor to yeasts isolated from deep subsurface environment. In addition
yeasts isolated from deepsea environments have been shown to produce siderophores
83
(Connell et al., 2009), a class of molecules used to bind and utilize Fe(III) and Mn(II) from
the environment. These results indicate that fungi may play an active role in
biomineralization process in the deepsea and probably terrestrial deep subsurface
environments.
Agaricomycetes basidiomycetes were found in the active community of OL-KR13/360m
(26%) and OL-KR49/532m (27%) but only as a minority (0.1%) in the total fungal
community (Figure 25). These species generally have well-developed fruiting bodies, like
mushrooms and wood-rotting fungi (Timonen and Valkonen, 2013). Generally
Agaricomycetes species are restricted to terrestrial environments but a few species are
detected from aquatic environments, such as marine oxygen-deprived environments
(Raghukumar et. al., 2012).
The major fungal group (98%) in active community in OL-KR9/468m constituted of
unidentified Basidiomycota species (Figure 28). In total community unidentified
Basiodiomycota were mostly detected in OL-KR3/381m (22%) and OL-KR13/360m
(10%). These were most closely related to uncultultured basidiomycota detected from
below ground grass root (Kivlin et. al., 2011), from indoor dust (Pitkäranta et. al., 2008)
and from grassland soil (Hawkes et. al., 2010).
50%
84
d
12.0
39.7
66.4
d
d
d
d
5.5
10.5
0.3
75%
0.1
0.1
Abundance
0.1
d
25%
5.2
26.0
0.5
0%
27.2
15.5
98.5
RNA
OL−KR13/360m_10r
OL−KR13/360m_12r
OL−KR3/339m_12r
OL−KR20/410m_13r
OL−KR6/422m_10r
OL−KR6/422m_13r
OL−KR25/357m_11r
OL−KR3/381m_11r
OL−KR23/425m_09r
OL−KR46/471m_13r
OL−KR46/493m_13r
OL−KR5/457m_12r
OL−KR49/532m_09r
OL−KR9/468m_11r
OL−KR9/565m_11r
OL−KR2/597m_10r
OL−KR1/609m_10r
OL−KR44/766m_13r
OL−KR29/801m_10r
d
d
d
0.7
0.2
2.9
d
8.2
12.4
0.1
6.3
37.2
6.0
d
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d
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d
d
d
0.1
d
d
d
d
d
0.1
10.4
1.1
d
0.4
1.4
22.2
DNA
OL−KR13/360m_10d
OL−KR13/360m_12d
OL−KR3/339m_12d
OL−KR20/410m_13d
OL−KR6/422m_10d
OL−KR6/422m_13d
OL−KR25/357m_11d
OL−KR3/381m_11d
OL−KR23/425m_09d
OL−KR46/471m_13d
OL−KR46/493m_13d
OL−KR5/457m_12d
OL−KR49/532m_09d
OL−KR9/468m_11d
OL−KR9/565m_11d
OL−KR2/597m_10d
OL−KR1/609m_10d
OL−KR44/766m_13d
OL−KR29/801m_10d
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Figure 28. Taxonomic classification heatmap of the fungal sequence reads obtained by high throughput sequencing of the total (DNA) and
active (RNA) Basidiomycota, Chytridiomycota, Zygomycota and Glomeromycota communities presented at genus-level. The samples are
arranged by true vertical depth from the surface down. The colouring of the heatmap as in Fig 13.
85
Other fungal phyla than Ascomycetes or Basidiomycetes were detected in OL-KR44/766m
where they dominated (66%) in the total community and constituted a minor group
(10.5%) in OL-KR29/801m (Figure 24). These fungi belonged to the class
Chytridiomycetes (Figure 25). Chytridiomycota are the earliest diverging lineage of fungi
and produces zoospores witch indicate adaptation to aquatic environments (Raghukumar
et. al., 2012; Nagano and Nagahama, 2012). Most of the species are free-living forms that
degrade organic material. Chytridiomycota species have only recently been found in
deepsea environments and their genomic sequences differ remarkably from published
species (Nagahama et. al. 2011; Le Calvez et. al., 2009). Olkiluoto Chytridiomycota
sequences were most similar to the order Rhizophydiales, species Rhixophydiales, which
are often found in oxygen-deficient marine environments (Raghukumar et. al., 2012)
(Figure 28). Other fungal phylum’s which contributed with less than 6% in the total and
active communities were Glomeromycota and Zygomycota, which have also been detected
in deepsea sediments (Nagano et. al., 2010).
Dominant fungal class in OL-KR13/360m in both years was Sordariomycetes in the total
and active fungal community but in year 2012 also Leotiomycetes and Dothideomycetes
were present (18.7−26.4%) in the total community. In 2012 no Basidiomycota species
were present in the total fungal community but in 2010 Basidiomycota species contributed
11.5% of the fungal sequences. In active fungal community Basiodiomycota species were
detected only in year 2012 (31.2%). In OL-KR6/422m dominant fungal class in year 2010
was Leotiomycetes and in year 2013 Eurotiomycetes in the total fungal community. In the
active fungal community dominant fungal class in 2010 was Dothiodeomycetes and in year
2013 Sordariomycetes. Dominant fungal class in OL-KR46/471m was Eurotiomycetes and
Sordariomycetes and at OL-KR46/471m fungal taxonomy remained unidentified.
Dominant fungal classes at OL-KR9/468m were Pezizomycetes and Sordariomycetes in
the total community and unidentified Basidiomycota in the active community. At OLKR9/565m dominant fungal class in active community was Sordariomycetes and in the
total community dominant fungi remained unidentified.
3.6 Sulphate reducers - diversity
dsrB gene fragments for high throughput sequencing with 454 technology were
successfully PCR amplified from 18 DNA samples and 16 RNA samples (Appendix M).
No amplification products were obtained from sample OL-KR29/801-867 m or RNA
samples obtained from OL-KR46/493 and OL-KR9/565. Sequencing of the PCR amplified
dsrB fragments resulted in 417,031 good quality sequence reads that were assigned to a
total of 495 OTUs at 97 % sequence similarity (Appendix M). According to estimates of
SRB diversity that were calculated on the basis of the detected OTUs (Chao1, Appendix
M), the obtained sequence reads covered well the estimated SRB diversity in majority of
the analyzed samples. In all but two DNA samples the sequencing captured 65 to 100% of
estimated SRB diversity. In RNA samples at least 71% of the estimated diversity was
captured.
In DNA fractions, the highest number of observed OTUs and taxonomically identified SRB
groups per sample was detected at 296 m depth in sample OL-KR13/360 m collected in 2010
(199 OTUs). In this sample SRB were also most abundant with 15 % of the total microbial
community estimated to be SRB on the basis of qPCR (Figure 10). The least amount of
86
OTUs and taxonomically identified groups was detected at 693 m vertical depth in OLKR44/766 m (7 OTUs) where SRBs were estimated to account for less than 0.1 % of the
total microbial community. In RNA samples the number of different OTUs and
taxonomically identified SRB groups detected per sample was generally lower than in the
DNA samples. Similar to DNA samples, the highest number of OTUs was detected in OLKR13/360 m collected in 2010. In RNA fractions, the least amount of OTUs was detected at
423 m vertical depth in sample OL-KR9/468 m. However, only 199 dsrB sequences were
obtained from this sample likely leading to underestimated SRB diversity in this sample.
Figure 29. Number of observed operational taxonomic units (OTU), estimated number of
OTUs (Chao1) and Shannon diversity index of the total (DNA, on the left) and active
(RNA, on the right) dsrB communities in the Olkiluoto groundwater samples as determined
by high through-put sequencing. The number of OTUs and Chao1 estimated number of
OTUs are given on the upper X-axis, and the value for the Shannon diversity index on the
lower X-axis. Sequences from all samples are subsampled to 1468 sequences to adjust for
sequencing coverage. * indicates samples with less than 1468 sequences. ** indicates
samples from with no dsrB gene fragments or transcripts were successfully PCR amplified.
Based on Shannon diversity index, the highest SRB diversity in DNA fractions occurred in
brackish sulphate-rich water collected above 415 m vertical depth (Figure 29). In saline
water below the depth of 415 m, but also at 347 m the diversity index was consistently
lower. This suggests that in brackish water the different SRB groups are present in even
proportions and no group has selective advantage under the prevailing environmental
87
conditions. This maybe due to the higher diversity and availability of organic carbon and
electron acceptors in the brackish water, whereas in saline water lower amounts of electron
acceptors, i.e. sulphate, or other environmental conditions favor the enrichment of certain
groups of SRBs. In agreement with differences observed in OTU richness, the Shannon
diversity index was also often lower in RNA samples than in DNA samples. However, no
similar trend in relation to sampling depth and type of water was observed. This indicates
that the SRB groups present in the water samples are not equally active under the
prevailing hydrogeochemical conditions.
In brackish SO4-rich water samples, the SRB communities present in the samples were
generally dominated by two groups of Desulfobacterales-like SRB with 82 to 99% of
sequence reads assigned to this order (Figure 30). One group of the Desulfobacterales-like
sequences was most similar to dsrB sequences in the Desulfobacteracea family previously
detected in marine sediments, whereas the other group shared less sequence similarity with
cultured SRB and was most similar to dsrB sequences obtained from Olkiluoto,
Outokumpu, deepsea sediments, polluted aquifers, and denitrifying sulfide removal process
(Nercessian et al. 2005, Bomberg et al. 2010; Zhou et al. 2011; Purkamo et al. 2013).). In
OL-KR46 where drawdown of SO4-rich water in the drillhole has resulted in mixing of the
original brackish water, the Desulfobacterales like sequences accounted for only 8 and
32% of all sequence reads in the upper and lower samples, respectively. In contrast, 36%
of sequence reads in OL-KR46/471m were closely similar to Desulfarculus barsii. D.
barsii is a chemoorganotrophic SRB that can use simple carbon compounds such as
formate and acetate as electron donor and sulphate, sulphite or thiosulphate as electron
acceptor. It has also been suggested to grow autotrophically by using C1-compounds such
as formate (Sun et al. 2010). In OL-KR46/493m, 55 and 13% of sequence reads were
closely similar to Desulfobulbus like sequences previously detected in OL-KR40 at
545−553 m and peptococcaeal dsrB sequences detected in the Outokumpu deep borehole
(Itävaara et al. 2011), respectively. SRB in the genus Desulfobulbus are
chemoorganotrophs and use organic compounds such as propionate and lactate as electron
donor and sulphate, sulphite and thiosulphate as electron acceptor. The organic electron
donors are oxidized incompletely to acetate. In the absence of sulphate Desulfobulbus can
also grow fermentatively (Brenner et al. 2005).
The Desulfobacterales-like SRB also dominated the active SRB communities in the
brackish water samples, but, in contrast to DNA samples, included only sequences similar
to dsrB sequences obtained previously from Olkiluoto, as well as from deepsea sediments,
polluted aquifers, and denitrifying sulfide removal process (Nercessian et al. 2005,
Bomberg et al. 2010; Zhou et al. 2011; Purkamo et al. 2013). The only exception to the
dominance of Desulfobacterales like SRB was sample OL-KR6/422m collected in 2010
that has been mixed by pumping and contains high concentrations of sulphate and sulfide.
This reflects also on the high number of dsrB gene and transcript copies (4.1 x 103 ml-1
dsrB copies and 4.6 x 102 ml-1 dsrB transcripts) indicative of active sulphate reduction. In
this sample over 50% of the sequenced dsrB transcripts were similar to Desulfobacula like
sequences previously detected in several Olkiluoto drillholes (Nyyssönen et. al. 2012).
SRBs in genus Desulfobacula have been reported to utilize a variety of low molecular
weight acids and alcohols as electron donors and carbon source, and sulphate and other
oxidized sulphur compounds as electron acceptors. In OL-KR46/471m Desulfarcula barsii
was consistently detected in both DNA and RNA samples in addition to the
88
Peptococcaceae and Desulfobacula like SRB previously detected in Outokumpu and
Olkiluoto (Itävaara et al. 2011, Nyyssönen et. al. 2012).
Desulfobacterales-like SRB were also present in saline water, but their occurrence was
more sporadic and not dependent on sampling depth or disturbances caused by water
mixing or draw down in drill hole. In OL-KR23/425m (347m bsl) that has been mixed due
to packer failure and in OL-KR5/457m (405 m bsl) that is recovering towards baseline
conditions, Desulfobacterales-like SRB dominated both the total SRB communities present
in the samples (88 and 92%) and the active SRB communities (90 and 93%). Similar
dominance of Desulfobacterales like SRB was also observed in the saline water of OLKR9/468m (423 m bsl) that represents a baseline-like sample with no recent disturbance.
In contrast to brackish water samples, the Desulfobacterales-like dsrB genes in the saline
water samples included only Desulfobacteriaceae-like sequences from marine sediments
(Kaneko et al. 2007; Lazar et al. 2011). In the RNA extracts, Desulfobacteriaceae-like
sequences from marine sediments as well as deep-sea sediments, polluted aquifers, and
denitrifying sulfide removal process were detected. This is also different from the brackish
waters where Desulfobacteriaceae-like sequences from marine sediments were not
detected. In addition, in OL-KR1/609m (572 m) the proportion of the dsrB sequences
similar to Desulfobacterales was 87 %, but the active SRB community was dominated (74
%) by Desulfomicrobium like SRB previously detected in several Olkiluoto drillholes
(Bomberg et al. 2010). SRB in genus Desulfomicrobium grow on simple organic
compounds using sulphate as electron acceptor with the incomplete oxidation of carbon
substrates to acetate and CO2. Autotrophic growth with formate or H2 has also been
reported (Brenner et al. 2005). In the absence of sulphate, they can also grow
fermentatively on simple carbon compounds.
In all of the four saline samples described above (OL-KR23/425m, OL-KR5/457m, OLKR9/468m, and OL-KR1/609m) sulfide concentration was an order of magnitude higher
than in the other saline water samples. Particularly in the slightly mixed water of OLKR23/425m (347m) and OL-KR5/457m (405 m) the detection of 102 dsrB transcripts mL-1
sample water was indicative of active sulphate reduction. In the remaining saline water
samples (OL-KR49/532m, OL-KR9/565m, OL-KR2/597m, OL-KR44/766m, and OLKR29/801m) the abundance of dsrB genes and transcripts decreased from 6.6 x 102 copies
and 1.6 10-1 transcripts mL-1 to below the detection limit. The SRB communities in these
samples were also different from the ones in brackish and saline water samples where
sulphate was available and molecular biological analyses suggested SRB activity. The
dsrB fragments PCR amplified from DNA and RNA samples were similar with the dsrB
gene from Desulfobacterium autotrophicum and Desulfobacula, Desulfobulbus, and
Desulfovibrio, and Peptococcaceae like dsrB genes previously detected in both Olkiluoto
and Outokumpu (Bomberg et al. 2010; Itävaara et al. 2011). Desulfobacterium
autotrophicum can grow on a variety of organic compounds including long-chain fatty
acids, but is also capable of chemolithoautotrophic growth on H2, CO2, and sulphate
(Strittmatter et al. 2009). SRB in genus Desulfovibrio are chemoorganotrophic with
sulphate as electron acceptor, but can also grow fermentatively without sulphate. Oxidation
of organic compounds is incomplete in many species of Desulfovibrio (Brenner et al.
2005). Together with sulfide concentrations close to detection limit, these results suggested
that sulphate reduction was not a significant process in the deep saline water samples (OLKR49/532m, OL-KR9/565m, OL-KR2/597m, OL-KR44/766m, and OL-KR29/801m).
DNA
OL-KR13/360m_10
OL-KR13/360m_12
OL-KR3/339m_12
OL-KR20/410m_13
OL-KR6/422m_10
OL-KR6/422m_13
OL-KR25/357m_11
OL-KR3/381m_11
OL-KR23/425m_09
OL-KR46/471m_13
OL-KR46/493m_13
OL-KR5/457m_12
OL-KR49/532m_09
OL-KR9/468m_11
OL-KR9/565m_11
OL-KR2/597m_10
OL-KR1/609m_10
OL-KR44/766m_13
OL-KR29-801m_10
SRB Genus Abundance Within Samples
0.8
1.2
0.1
0.1
0.3
0.1
0.1
2.4
d
0.2
0.1
0.1
d
d
0.1
0.8
d
0.1
d
0.4
0.1
d
d
d
0.2
d
d
d
1.4
d
0.7
d
d
d
0.2
d
3.0
d
0.1
0.2
d
0.1
d
d
0.4
d
d
0.3
0.1
d
0.1
0.1
0.1
d
13.3
4.2
0.2
d
1.4
0.1
0.1
d
d
0.3
0.1
1.8
d
d
0.4
2.2
d
0.4
d
0.3
0.1
0.2
13.0
0.2
0.7
0.3
d
0.7
1.0
0.4
0.1
d
15.6
0.5
d
11.6
0.2
1.1
0.1
0.2
d
0.1
d
d
0.5
d
3.0
0.2
0.1
0.2
0.5
1.4
0.4
d
d
d
0.3
0.1
3.4
0.3
0.1
0.1
0.9
36.3
1.6
0.1
d
6.1
0.7
0.1
d
d
0.3
0.2
6.2
0.1
4.6
0.1
d
0.1
82.4
d
0.2
0.9
98.6
d
d
85.3
d
d
d
d
82.4
d
d
87.4
0.2
93.2
d
d
96.8
0.2
d
0.4
d
5.4
0.1
95.2
d
0.1
32.4
0.4
6.0
7.8
55.3
0.3
d
0.1
1.3
d
d
0.1
0.2
d
d
0.2
91.5
d
0.2
d
0.1
d
d
5.3
d
0.1
1.5
1.7
d
0.4
2.3
d
d
d
d
2.3
d
d
d
0.1
0.5
d
d
d
d
d
0.4
d
d
0.1
d
3.9
0.4
d
0.5
0.1
0.2
d
0.1
0.1
d
0.4
0.1
0.1
0.2
0.1
0.1
d
d
Figure 19. Taxonomic classification heatmap of the fungal sequence reads obtained by high throughput sequencing of the total (DNA)
1.2
0.2
0.1
1.3
22.2
d
d
0.3
0.1
2.0
0.2
1.7
1.8
19.2
4.4
0.1
3.4
0.1
0.3
0.1
7.6
29.8
4.7
0.1
66.5
0.8
d
62.9
0.1
0.7
4.5
0.6
87.1
7.5
d
0.3
4.1
0.1
11.3
0.4
0.2
0.2
18.7
1.8
0.7
0.7
d
0.5
1.2
26.5
3.3
d
0.2
15.3
0.2
0.5
0.2
0.6
d
2.1
1.9
d
2.0
0.2
0.2
1.5
4.7
0.5 78.9
0.5
86.6
0.1
0.6
0.4
56.7
8.2
0.1
1.4
23.4
1.2
d
0.3
6.2
3.0
Abundance
d
75%
50%
0.2
0.7
0.1
1.8
0.7
0.3
d
0.2
0.4
0.2
4.1
0.1
0.2
0.1
0.1
0.2
1.7
0.3
0.1
d
d
0.2
0.6
0.3
1.0
d
d
d
0.3
0.2
0.1
d
d
0.1
0.8
34.9
0.2
3.3
4.0
d
3.1
1.8
0.3
0.2
d
d
0.1
0.6
0.7
d
0.1
d
0.1
d
d
0.6
d
3.3
0.2
1.1
0.1
0.1
0.5
0.7
d
d
0.8
0.9
1.4
1.6
d
22.3
6.9
0.5
0.1
11.8
22.6
0.1
0.6
8.2
0.3
98.7
0.8
84.5
1.3
89.5
1.0
0.1
0.3
d
d
d
90.4
5.7
50.7
0.6
0.4
d
d
0.1
d
42.2
d
d
77.4
2.1
1.4
0.8
89.5
1.6
84.9
2.2
d
1.3
0.1
d
26.6
0.9
d
0.1
0.2
0.3
1.6
89.9
0.6
9.7
2.3
d
0.3
0.6
d
0.7
d
0.1
0.2
0.1
25%
1.0
0.1
d
d
d
0.2
0.2
0.9
d
1.9
0.1
0.1
0%
0.1
d
0.2
0.1
d
d
d
1.4
92.6
1.4
0.5
2.1
2.5
79.8
5.9
37.8
13.2
0.3
d
0.3
d
0.1
d
0.5
87.1
13.9 28.2
15.6
10.5 19.5
d
10.9
0.4
1.7
d
0.7
20.1
3.2
1.7
0.1
7.4
0.1
9.7
17.2
0.1
8.6
0.1
0.5
0.7
89
RNA
OL-KR13/360m_10
OL-KR13/360m_12
OL-KR3/339m_12
OL-KR20/410m_13
OL-KR6/422m_10
OL-KR6/422m_13
OL-KR25/357m_11
OL-KR3/381m_11
OL-KR23/425m_09
OL-KR46/471m_13
OL-KR46/493m_13
OL-KR5/457m_12
OL-KR49/532m_09
OL-KR9/468m_11
OL-KR9/565m_11
OL-KR2/597m_10
OL-KR1/609m_10
OL-KR44/766m_13
OL-KR29-801m_10
0.4
0.2 74.0
1.7
0.1
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Figure 30. SRB taxonomy. The taxonomic distribution and relative abundance of dsrB sequence reads in the different samples are shown
for the DNA samples in the upper part of the figure and for the RNA samples in the lower part of the figure. The samples are organized in
the order of vertical sampling depth. The taxonomic assignments for the sequences are presented in the bottom on the figure. The colours
of the heatmaps indicate the relative abundance of sequence reads, where the lowest percentage is shown in light blue and the highest
towards red according to the gradient bar on the right of the graph. Black cells indicate 0, and d indicate that a group was detected, but at
relative abundance below 0.1% of the total number of sequence reads from that sample.
90
3.7 Methanogens - diversity
Methanogens were detected based on the amplification of mcrA gene and transcript
fragments for amplicon library sequencing. In general, the abundance of mcrA genes and
transcripts was low, and a two-step PCR was needed for their detection. McrA genes were
detected from 14 of the 19 samples, while RNA transcripts only from 8 samples (Appendix
N).
The total number of mcrA sequences obtained was 201,467 distributed over 22 samples. The number of sequences obtained per sample varied between 798 and 28,694 sequences,
with exception of the DNA samples from OL-KR46/471m (5 sequences) and OLKR46/493m (25 sequences). Due to the low number of mcrA sequences obtained from
these two samples, they will be excluded from the diversity analyses.
The mean number of sequences/sample was 9,158. The mcrA gene and transcript
sequences were grouped into OTUs sharing 99% sequence similarity within each OTU.
The total number of mcrA OTUs obtained was 199. The OTU number varied between 7
and 166 between the different sequenced samples (Appendix N, Figure 31). The number of
OTUs to be found in each sample as estimated by the Chao1 species richness indicator
varied between 13 and 364 mcrA OTUs in the samples (DNA and RNA), while the actual
number of possible species of methanogens determined by the taxonomic identification of
each OTU varied between 1 and 12. According to the Chao1 on average over 51% of the
estimated diversity was obtained from the samples. The lowest coverage (15.2%) was
obtained from OL-KR44/766m DNA sample, despite the high numbers of sequences
obtained from this sample. In addition, the number of different species identified from the
high number of sequence reads was only 4, indicating that the community was still well
covered and no new species would have been likely to be obtained by more sequencing.
The highest diversity coverage (75%) was obtained from the active community (RNA) of
sample OL-KR3/339m. This sample provided only 798 sequences, which fell into 3
different methanogen species. The highest Shannon diversity indices (H’ > 2) were
obtained from the total community of samples OL-KR13/360m collected in 2010, OLKR20/410m and OL-KR25/357m, of which OL-KR13/360m (2010) and OL-KR25/357m
represent mixed water layers, while OL-KR20/410m represents a fracture, which is
recovering toward its natural state.
91
Figure 31. Diversity. The shifts in the number of detected mcrA sequence OTUs, species
richness Chao1 and Shannon diversity index between the different samples are shown for
the A) DNA samples and B) RNA samples separately. The number of OTUs and Chao1
estimated number of OTUs are given on the upper X-axis, and the value for the Shannon
diversity index on the lower X-axis. * indicates samples with only few sequences.
According to their taxonomy, the mcrA OTUs belonged to 27 different taxonomical groups
(”species”) (Figure 32). The most frequently detected mcrA sequences belonged to a group
of Methanosarcinaceae (DeepOlki3, 5, 6, 7, 8, 9) previously detected in Olkiluoto
groundwater (Bomberg et al., 2010), for which cultured representatives are not yet
available. These were present in different samples throughout the studied depth profile. In
the upper parts of the depth profile (296−347m) this Methanosarcinaceae cluster was the
dominant group detected from the active community, while in the deeper parts (572 m and
798 m), the Methanosarcinaceae were more similar to methanogens detected in the deepsea
sediment of the Nankai Through (leg 1173, Newberry et al., 2004). These were also the
dominating methanogens detected in the total community at 423 m (OL-KR9/468m).
Members of the Methanosarcinaceae generally are able to utilize many different small
carbon compounds for methanogenesis, such as H2-CO2, acetate, methanol, methylamines
and methyl sulfides (Kendall and Boon, 2006).
92
Figure 32. Heatmap. The taxonomic distribution and relative abundance of mcrA sequence
reads in the different samples are shown for the DNA fraction in the upper part and for the
RNA samples in the lower part of the figure. The samples on the left are displayed in depth
order according to their actual vertical depth. The taxonomic assignments for the sequences
are presented in the bottom on the figure. The colours of the heatmaps indicate the relative
abundance of sequence reads, where the lowest percentage is shown in light blue and the
highest towards red according to the gradient bar on the right of the graph. Black cells
indicate 0, and d indicate that a group was detected, but at relative abundance below 0.1% of
the total number of sequence reads from that sample.
93
Methanolobus psychrophilus-like methanogens were only detected from DNA fractions,
and they were most abundant in the total methanogen community in OL-KR13/360m from
2010 and in OL-KR9/565m. M. psychrophilus was first isolated from the Zoige wetland of
the Tibetian Plateau where it conducts cold-adapted methanogenesis using methanol as its
preferred substrate, but it is also able to utilize methyl sulfides (Zhang et al., 2006).
Methanospirillaceae methanogens most similar to Methanospirillum hungatei was found as
the sole methanogen in the active population of OL-KR25/357m, and was not detected in
any other sample. This methanogen was first isolated from sewage sludge (Smith, 1966;
Ferry et al., 1974), and was long the sole representative of its genus. Novel Methanospirilli
have been isolated in recent years from soil (Iino et al., 2010), wetland soil (Zhou et al.,
2014) and a propionate-oxidizing methanogenic consortium from a low-temperature,
granulated sludge bed bioreactor (Parshina et al., 2014). The genus contains both cold
adapted and mesophilic species, and they use H2 and CO2 as substrate for methanogenesis.
Methanoregulaceae were a major part of the methanogenic population in samples between
296 −347 m, but constituted also a major part of the methanogenic community in OLKR29/801m. In the active population they were detected sporadically and only in samples
OL-KR20/410m and OL-KR23/425m. Methanoregulaceae methanogens have been
isolated from rice field soil (Sakai et al., 2012), anaerobic sludge digesters (Imachi et al.,
2008) and an acidic bog (Bräuer et al., 2011). Only few pure cultured Methanoregulaceae
archaea yet exist, and their physiology has been shown to be quite diverse. All of the
cultured Methanoregulaceae are hydrogenotrophic methane producers, but some may also
use acetate for methanogenesis, or otherwise require acetate for growth (Imachi et al.,
2008; Sakai et al., 2012), or use formate for methanogenesis (Imachi et al., 2008).
Methanobacterium-like methanogens were only detected in the deeper samples of the
studied depth profile. They were the dominating methanogens in the total community at
572 m in sample OL-KR1/609m, and were also the dominating active methananogens in
this sample. Methanobacteria generally use H2 and CO2 for methanogenesis and have
previously been isolated in pure culture from saline deep groundwater of Äspö, Sweden
(Kotelnikova and Pedersen, 1997).
A small minority of the mcrA transcripts from OL-KR20/410m also belonged to the
Methanobacterium, but they were not seen in the DNA fraction of the sample. ANME1
associated methanogens previously detected from clone libraries of Olkiluoto groundwater
samples (Nyyssönen et al., 2012) were detected as a minority (0.6% of the sequence reads)
in sample OL-KR9/468m (423 m depth), but were not found in the active fraction of the
population.
The substrates used for methanogenesis by the different groups detected in these samples
have been estimated according to what is known for the closest cultured species.
According to this the methanogens in Olkiluoto groundwater are very versatile, and may
hence more easily adapt to changing conditions in their living habitats. It is even possible
that the methanogens here classified according to their mcrA gene sequences to belong to a
group of Methanosarcinaceae may be able to oxidize methane anaerobically. The archaeal
group ANME-2D nests within the Family Methanosarcinaceae, to which the greater part of
the Olkiluoto mcrA also belong. A genome of the ANME-2a from a mud volcano in the
94
Atlantic Ocean has recently been partly resolved (Wang et al., 2013) first of all revealing
the complete set of genes needed for anaerobic methane oxidation. In addition, the authors
found a high level of similarities between the genomes of cultured Methanosarcina and the
partly sequenced ANME-2a. ANME-2 archaea were also one of the dominating groups of
archaea detected in the groundwater samples of Olkiluoto based on the archaeal 16S rRNA
gene.
9
4 CONLUSIONS
In 2009−2013 the total and active microbial communities in Olkiluoto groundwater
fractures from depths between 296 m and 798 m below surface level were analyzed by 16S
rRNA and rDNA targeted Tag pyro sequencing. In addition the number of sulphate
reducing bacteria (SRB), ammonium oxidisers, denitrifying bacteria and methanogenic
archaea were analysed by qPCR.
The following general conclusions can be drawn from the results:






Fungi and bacteria appear to play a versatile role in major ecological processes in
terrestrial deep subsurface environments allowing them to adapt to changing conditions
in their living habitats. The dominating bacteria belonged to groups, which although
preferring a heterotrophic life style have the capacity to live autotrophically (i.e. fix
CO2 as carbon source). The role of fungi in deep biosphere environments is largely
unknown, but especially Ascomycota have been reported to play an important role in
denitrification in deepsea environments, and may play a key role in the N cycling also
in deep groundwater in Olkiluoto.
The total number of microbial cells (TNC), sulphate reducers, ammonia oxidisers and
methanogens decreased with the depth.
Fungal and archaeal communities were less diverse compared to the bacterial
communities.
No connection was found between sampling depth and bacterial, archaeal or fungal
diversities measured by Shannon diversity index. This is probably due to different level
of disturbance and heterogenecity in environmental parameters in drill holes.
Due to the high number of uncultured or unculturable microorganims present in nature,
classification to lower taxonomic levels, such as genus and species is mainly not
possible. This was also seen in the present analysed data set, where the majority of the
microbial taxa were not classified to lower taxonomical levels. Nevertheless, the
sequences matching most closely to the Olkiluoto sequences often belonged to deep
subsurface and deepsea uncultured microorganisms, especially concerning the archaea
and fungi.
The microbial community of OL-KR13/360m and OL-KR6/422m were sampled at two
different time points, respectively. In OL-KR13/360m the community had changed
dramatically between the two sampling points, and minor changes in the groundwater
chemistry were also detected, especially the sulphide concentration decreased. In OLKR6/422m changes in microbial community profiles were also seen, but the
hydrogeochemistry did not noticeably vary between sampling times.
The nitrogen cycle in deep subsurface environments is still not well understood. Common
genes involved in the nitrogen cycle, such as amoA and narG, were detected at lower
concentrations as expected. In addition, previously undetected nitrogen cycling
microorganisms in Olkiluoto groundwater samples were found. The possible involvement
of the ANME-2D archaea in the nitrogen cycle can also be suggested.
 Ammonia oxidation is the first phase in the nitrification process. Ammonia oxidizers
were detected only in the samples from depths above 390 m and active ammonia
oxidisers, as determined by the number of amoA gene transcripts detected, were not
found in any of the samples. The primers used in the assay are mainly targeting beta-

and gammaproteobacteria, leaving possible other ammonia oxidizers out of the
detection. Nevertheless, beta- and gammaproteobacteria were prominent in the
samples, and their amoA genes should have been detected, if they were present. It is
also possible that the fungi present contribute more to the ammonia oxidation and
break down of organic compounds than the bacteria, leaving the bacterial ammonia
oxidation obsolete.
The nitrate concentration was below detection limit in most of the samples. The number
of total and active nitrifiers remained at the same level at all depth and they were found
from all samples. The denitrifying ability has been found in microbes belonging to
numerous groups of bacterial and archaea. The major archaeal groups detected in
Olkiluoto samples, the ANME-2D have been found to oxidize methane anaerobically
using nitrate as electron acceptor. This reaction leads to formation of nitrite, which
promptly ends up as N2, which is unusable as nitrogen source unless for specialized
nitrogen fixing microorganisms. These have not yet been searched for in Olkiluoto but
the general microbial diversity indicates the presence of nitrogen fixing bacteria.
Sulphate reduction is estimated to be one of the most important microbial processes
affecting the safety of long-term storage of nuclear waste.
 SRB were present throughout the groundwater layers in Olkiluoto, with exception of
the deepest water layers (798 m vertical depth). The highest number of both present
and active SRB were detected in SO4-containing brackish water and slightly mixed
saline water above 347 m vertical depth.
 SRB communities were most diverse in brackish sulphate containing water collected
above 415 m vertical depth. This maybe due to the higher diversity and availability of
organic carbon and electron acceptors in the brackish water.
 In general, the SRB communities were dominated by different groups of
Desulfobacterales. In brackish SO4-containing water samples, these bacteria dominated
both the SRB communities present in the samples and the active SRB communities. In
saline water samples, the SRB communities were different and the occurrence of
Desulfobacterales like SRBs was more sporadic and not affected by sampling depth or
disturbances caused by mixing and draw down in drillhole.
 The SRB groups present in the different water samples are not equally active.
Particularly in the mixed samples OL-KR6/422m (330 m), OL-KR23/425m (347m)
and OL-KR5/457m (405 m) higher amount of sulphate and sulphide as well as dsrB
transcripts indicate active sulphate reduction.
 In most of the saline samples without recent mixing, SRB were present in low numbers
and no indications of active sulphate reduction were seen.
Bacterial diversity:
 Proteobacteria was the predominant bacterial phylum in most of the samples.
 The most dominant alphaprotebacterial families were Caulobacteraceae and
Phyllobacteriaceae which are often described as aerobic and chemoorganotrophic.
Some species are able to use nitrate as electron donor instead of oxygen in anaerobic
conditions.
 Betaproteobacteria were present in the total and active population in all samples. In
2013 the microbial population mostly consisted of Hydrogenophaga which includes
chemocaorganotrophic and chemolitohitrophic aerobic families.






Gammaproteobacteria were the most abundant bacterial group in samples taken from
415, 572 and 693 m bsl. Most of these sequences belonged to family
Pesudomonadaceae, which mostly contain aerobic and chemoorganotrophic species.
Deltaproteobacteria were found in all samples and formed a big part of the active
community. The biggest group of Deltaproteobacteria was Desulfobacterales which are
anaerobes and can use sulphate, sulphite and thiosulphate as electron acceptors. The
families of Desulfobacteriaceae and Desulfobulbaceae were detected abundantly.
Desulfobacteriaceae typically oxidase organic substrates completely whereas
Desulfobacteriaceae oxidase substrates incompletely and for acetate.
Epsilonproteobacteria were especially dominating in samples from mixed fracture zones
(OL-KR13/360m, OL-KR6/422m, OL-KR23/425 and OL-KR25/357m) obtained
between 2009 and 2011. The epsilonproteobacteria are especially S oxidizers, a process
which may become activated when the sulphate-rich and methane-rich water layers mix.
The deepest samples were especially dominated by Actinobacteria.
The most abundant genus in the deepest sample OL-KR29/801m was Microbacterium
which may live chemolithotrophically oxidising thiosulphate.
In addition to Proteobacteria and Actinobacteria also Bacteroidetes, Firmicutes,
Nitrospirae and Spirochaetes phyla formed a noticeable part of the bacterial community
in specific samples. The Bacteroidetes were especially common in the mixed water
samples of OL-KR46, while Nitrospira favoured the stable water of OL-KR9.
Archaeal diversity:
 Euryarchaeota was the most abundant archaeal phylum in all but one sampling site.
 ANME-2D belonging to the Methanosarcinales were the most abundant archaea. A
novel cultured representative of the ANME-2D, Methanoperedens nitroreducens, has
been shown to perform nitrate-driven anaerobic oxidation of methane without a partner
organism through reverse methanogenesis with nitrate functioning as the terminal
electron acceptor.
 In addition, autotrophic methanogens using H2 and CO2 as substrates for
methanogenesis are present (Methanoregula, Methanobacteria, Methanospirillum)
especially below 415 m.
 Thermoplasmata were present at all sampling sites and most of them are commonly
associated with anoxic environments. They have been hypothesised to be heterotrophic,
but new findings indicate, that they may include methanogenic organisms. This may
lead to new discoveries in regard to the microbial methane cycle in deep subsurface
environments.
 DSEG (Deep Sea Eyryarchaeotic group) were prominent in the archaeal communities,
and are typical uncultured deep subsurface archaea, which are commonly found also
from deep sea sediments.
 ANME-1, Archaeoglobi, Methanobacteria and Methanococci were present and active
at specific depths, but generally did not dominate the archaeal communities.
 Crenarchaeota phylum has not previously been detected in Olkiluoto groundwater at
greater extent. Now they were found almost in all samples and were the most abundant
phylum in the mixed water of OL-KR46/471. Groups, such as the Marine Benthic
Group, generally found in anoxic low-energy subsurface sediments and
Thaumaarchaeota, which contain chemolithoautotrophic ammonia-oxidizing archaea,
were detected. Of the Thaumarchaeota the recently isolated Nitrosopumilus maritimus
is known to oxidize ammonia in seawater at very low ammonia-concentrations. It is


possible that this archaeon contributes to the nitrogen cycling in the Olkiluoto
groundwater, where ammonia concentrations are low. They were especially common in
the mixed water of OL-KR46/471m.
Parvarchaeota phylum is a novel uncultured archaeal group detected mostly in acidic
environments and also in weak alkaline deep subsurface hot springs. With the
exception of the deepest sample (OL-KR29/801m) Parvarchaeota were detected at all
the sampling sites.
Unclassifiable and novel sequences (OTUs) represented ca. 7% of both total and active
communities.
Fungal diversity:
 From all the ITS sequences obtained, 24.8% were classified as fungal sequences but
taxonomy remained unidentified. In addition dominating fungal taxa remained
unidentified in some of the samples (OL-KR/360m, OL-KR6/422m (2010), OLKR46/471m, OL-KR/457m, OL-KR9/565m and OL-KR1/609m).
 In the identified total and in the active fungal community the Ascomycota phylum
dominated in almost all samples.
 From the Ascomycota phylum Sordariomycetes was the major active fungal class in
most of the samples and also in half of the samples in total fungal community.
Sordariomycetes are one of the most frequently detected fungal taxa also in the deepsea
environments. Members of the Sordariomycetes are ubiquitous and cosmopolitan and
function in virtually all ecosystems.
 Eurotiomycetes was the dominating fungal class in the samples taken from OLKR20/410 m (410 m) and OL-KR6/422 m (422 m) in 2013. The majority of the
sequences belonging to the Eurotiomycetes were members of the Penicillium group,
which is known to be globally distributed. Also Aspergillus was found in two samples.
Some species belonging to these groups have been found in deep marine environment
and have tolerance for high salt concentration.
 Basidiomycota phylum represented 9% of all the sequences. The most dominant
classes were Microbotryomycetes, Tremellomycetes and Malasseziales that cannot be
placed in as specific fungal order.
 Yeasts identified from deep subsurface environments have showed that yeast may be in
key role in dissolving iron and binding manganese.
 Methylotrophic yeasts (Malassezia and Cryptococcus) have been detected in deepsea
methane seeps and these yeasts may play a crucial role in converting methane into
more accessible carbon and energy substrates.
 It should also be noted that the results of the RNA fraction-derived fungal communities
are fundamentally different from the prokaryotic 16S rRNA fraction-derived
communities, as in prokaryotes, the 16S rRNAs are incorporated into ribosomes, and
thus present in 100s of copies, whereas the RNA fraction of fungal ITS is only present
in the cell when the genomic copy is being actively transcribed.
 Yeasts (fungi) may play an active role in the cycling of Fe(III) and Mn(II), because
they have the ability to produce siderophores. Siderophores are generally iron-binding
compounds, but they may also bind e.g. manganese. This binding of metals to
siderophores makes the metals more bioavailable to the microorganisms, which use the
siderophores to 'catch' sparcely-available metals to be used in for example different
enzymes. Manganese oxides are important electron acceptors in a wide range of redox
reactions, but their availability in the studied samples were low. Mn(II)-oxidazing
bacteria are ubiquitous in nature and are phylogenetically diverse, with representatives
in the Firmicutes, Actinobacteria and the alpha-, beta and gammaproteobacteria.
Methanogens:
 The number of methanogens throughout the depth profile was low.
 The most frequently detected mcrA sequences belonged to a Methanosarcinaceae
previously detected in Olkiluoto groundwater. Members of the methanosarcinaceae
generally are able to utilize many different small carbon compounds for
methanogenesis, such as H2 and CO2, acetate, methanol, methylamines and methyl
sulphides.
 Methanogens in Olkiluoto groundwater are very versatile and may adapt to changing
conditions in their living habitats. It is even possible that the methanogens here
classified to belong to a group of Methanosarcinaceae may be able to oxidize methane
anaerobically.
 The archaeal group ANME-2D, which may perform nitrate mediated anaerobic
methane oxidation, nests within the Family Methanosarcinaceae, to which the greater
part of the Olkiluoto mcrA also belong. A genome of the ANME-2a from a mud
volcano in the Atlantic Ocean has recently been partly resolved (Wang et al., 2013)
first of all revealing the complete set of genes needed for anaerobic methane oxidation.
In addition, the authors found a high level of similarities between the genomes of
cultured Methanosarcina and the partly sequenced ANME-2a. ANME-2 archaea were
also one of the dominating groups of archaea detected in the groundwater samples of
Olkiluoto based on the archaeal 16S rRNA gene.
 On the basis of 16S-assigned taxonomy the relative share of genes dedicated to
methane metabolism was clearly larger in the deeper sampling sites.
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APPENDIX A: Geochemical analysis methods.
Parameter
pH
Eh(Pt)
EC
Unit
DIC/DOC/TOC
TDS
Alk
SO42S2PO4 -
(mg L-1)
(mg L-1)
(meq L-1)
(mg L-1)
(mg L-1)
(mg L-1)
NO3 NH4 +
Fe2+
Na2+
(mg L-1)
(mg L-1)
(mg L-1)
(mV)
(mS m-1)
(mg L-1)
K+
Ca
2+
(mg L-1)
(mg L-1)
Mg2+
Mn2+
-
Cl
CH4
(mg L-1)
(µg L-1)
(mg L-1)
(mL L-1 gas)
Method
pH meter, ISO-10532
Platinum electrode
Conductivity analyzer, SFS-EN-27888
Detection limit
SFS-EN 1484
5
TC: 0.6
IC: 0.3 l
TOC: 0.3
Titration with HCl
IC, conductivity detector
Spectrophotometry
IC, conductivity detector
0.05
0.1
0.1
0.1
FIA method, SFS-EN ISO11905-1
Spectrophotometry, SFS 3032
Spectrophotometry
2007: FAAS, SFS3017, 3044
2008: ICP-OES
2007: FAAS, SFS3017, 3044
2008: ICP-OES
2007: FAAS, SFS3017, 3044
2008: ICP-OES
2007: FAAS, SFS3018
2008: ICP-OES
2007: GFAAS,SFS5074, 5502
2008: GFAAS,SFS5074, 5502
Titration
Gas chromatography
0.05
0.01
5
0.5
0.31
0.5
0.02
0.1
0.15
0.02
1
12.5
5
1 µl L-1 gas
APPENDIX B: Geochemical analysis results.
130
1
APPENDIX C: Taxonomic classification heatmap of the Proteobacteria
sequence reads obtained by high throughput sequencing of the total (DNA) and active
(RNA) bacterial communities presented at class-level. The samples are arranged by true
vertical depth from the surface down. The different colours represent relative abundance
(%) of sequence reads. Blue boxes with the d-letter describe taxa represented by less than
0.1 % the sequence reads of that sample, black boxes display absence.
1
APPENDIX D: Taxonomic classification heatmap of the Firmicutes sequence reads obtained by high throughput sequencing of
the total (DNA) and active (RNA) bacterial communities presented at family-level. The colouring of the heatmap as in App. C.
133
134
APPENDIX E: Taxonomic classification heatmap of the Actinobacteria sequence reads obtained by high throughput sequencing of the
total (DNA) and active (RNA) bacterial communities presented at family-level. The colouring of the heatmap as in App. C.
135
136
APPENDIX F: Taxonomic classification heatmap of the Bacteroidetes, Clorobi, Tenericutes, Thermi and Chloroflexi
sequence reads obtained by high throughput sequencing of the total (DNA) and active (RNA) bacterial communities presented at familylevel. The colouring of the heatmap as in App. C.
137
138
APPENDIX G: Taxonomic classification heatmap of the Elusimicrobia, Nitrospirae, Planctomycetes, Spirochatetes, Tenericutes and
Verrucomicrobia sequence reads obtained by high throughput sequencing of the total (DNA) and active (RNA) bacterial communities
presented at family-level. The colouring of the heatmap as in App. C.
139
140
APPENDIX H: Taxonomic classification heatmap of the OD1, OP3, TM6, TM7 and WS3 sequence reads obtained by high
throughput sequencing of the total (DNA) and active (RNA) bacterial communities presented at family-level. The colouring of the heatmap
as in App. C.
141
142
APPENDIX I: Characteristics of the main bacterial taxa found from Olkiluoto groundwater samples.
Taxonomy
Proteobacteria Alphaproteobacteria
Caulobacteriales Caulobacteraceae
Rhizobiales Phyllobacteriaceae
Spingomonadales Sphingomonadaceae
Proteobacteria Betaproteobacteria
Burkholderiales Comamonadaceae
Hydrogenophaga (OL‐KR6/422m, 2013)
Methylophilales
Rhodocyclales Rhodocyclaceae
Sulfuritalea (OL‐KR13/360m 2012)
Proteobacteria Deltaproteobacteria
Desulfobacteriales
Desulfuromonaceae
Geobacteracea
Pelobacteraceae
Carbon source; Electon donor; Electron acceptor
Reference
Chemoorganotrophic, aerobic
Chemoorhanotroph, aerobic
ED: some use nitrate
ED: some use nitrate
Garrity et al., 2005a
Mergaert and Swings, 2005
Chemoheterotrophic, aerobic, degdradation of aromatic
compounds
Chemoorganotrophic, chemolithotrophic, aerobic
N 2 fixing, some heterotrophic and mixotrophic
Aerobic, oxidases methanol but not methane
Yabuuchi and Kosako, 2005
ED: some use nitrate
Carbon: CO 2 ED: some use nitrate, H2
Carbon: methanol
Willems and Gillis, 2005.
Willems and Gillis, 2005
Garrity et al., 2005b
Garrity et al., 2005c
Facultatively autotrophic and anaerobic, fermentative;
Freswater lake in Japan
ED: sulphur, thiosulphate, H 2 EA: nitrate
Kojima and Fukui, 2011
Chemooranotrophs, chemolithoheterotrophs,
chemolithoautotrophs, SRB, strictly anaerobic, respiratory
metabolism, few fermentative, meso‐ or psychrophilic
Carbon: simple organic compounds, some even aliphatic or
aromatic hydrocarbons ED: simple organic compounds, some
even aliphatic or aromatic hydrocarbons EA: sulphate,
sulphite,thiosulphate, some: sulphur, polysulphide, nitrate
Carbon: some use CO 2 ED: some use H 2
Kuever et al., 2005a
Diverse group, fermentative, N2 fixing
Mostly complete oxidation of organic substrates
Mostly incomplete oxidizers that form acetate
Chemolithoheterotrophs, chemoorganotrophs, anaerobic,
respiratory or fermentative, mesophiles
Complete oxidation of organic substrates
Fermentative
Some may grow by disproportionation of sulphur,
thiosulphate, and sulphite
Carbon: simple organic compounds ED: simple organic
compounds acetate, carboxylic acids EA: ferric iron, sulphur,
some: Mn(IV), humic substances
Carbon: simple organic compounds
Kuever et al., 2005a
Kuever et al., 2005a
Kuever et al., 2005b
Kuever et al., 2005b
Kuever et al., 2005b
Proteobacteria Epsilonproteobacteria
Campylobacterales Helicobacteraceae
Microaerophilic, anaerobic
Sulfurimonas
Chemolithoautotrophic, some aerotolerant;
Sulphidic Baltic sea water
Sulfuricurvum
Facultatively anaerobic, chemolithoaturotrophic;
Underground crude‐oil storage cavity, Japan
Proteobacteria Gammaproteobaceria
Methylococcales Crenotrichaceae
Pseudomonadales Pseudomonadaceae
Methanotrophic, microaerophilic
Chemoorganotrophic, aerobic, minimal media,
Garrity et al., 2005d
Carbon: CO 2 , some acetate pyruvate, peptone, yeast extract
ED: sulphide, sulphur, thiosulphate EA: nitrate, nitrite, O 2
Carbon: CO 2 ED: sulphide, sulphur, thiosulphate, H 2
EA: nitrate, O 2
Labrenz et al., 2013
ED: methane
Carbon: simple organic compounds ED: some use nitrate,
ammonium
Bowman, 2005
Garrity et al., 2005
Kodama and Watanabe, 2004
143
Desulfobacteriaceae
Desulfobulbaceae
Desulfuromonadales
Description; Isolation
Taxonomy
Firmicutes Erysipelotrichi
Erysipelotrichales Erysipelotrichaceae
Bacilli
Bacillales Staphylococcaceae
Clostridia
Clostridiales Clostridiaceae
Clostridiales Peptococcaceae
Desulfosporosinus
Actinobacteria Actinobacteria
Actinomycetales Microbacteriaceae
Microbacterium
Micrococcaceae Micrococcus
Nocardiaceae Rhodococcus
Nocardioidaceae
Bacteroidetes Flavobacteriia
Flavobacteriales Flavobacteriaceae
Nitrospirae Nitrospira
Nitrospirales Thermodesulfovibrionaceae
Spirochaetes MVP15
PL‐11B10
WS3
OD1
Carbon source/Electon donor/Electron acceptor
Reference
Facultatively anaerobic, chemoorganotrphic
Ludwig et al., 2005
Facultatively anaerobic, chemooraganotrophic, fermentative
Schleifer and Bell, 2005
Chemoorganotrophic, chemolithotrophic, anaerobic,
fermentative, some fix N2
Chemolithohetero/autotrophic, chemoorganoheterotrophic,
anaerobic, fermentative or respiration, SRB
SRB
Wiegel, 2005
Chemoorganotrophic, aerobic, facultatively anaerobic
Chemolithotrophic
Chemoorganotrophic, aerobic, minimal media
Chemoorganotrophic, aerobic, oxidative diverse metabolism
Ezaki, 2005
EA: sulphate, thiosulphate
ED: thiosulphates, some Cr(VI) EA: acetate
Chemoorganotrophic, degradation of toxic pollutants, many Carbon: many sources
enzyme activities, chemolithotrophic growth with CO and H 2
Chemoroganotrophic, anaerobic, aerotolerant, fermentative, Carbon: sugars
produce propionic and acetic acids, nutritional requirements
Hippe and Stackebrandt, 2005
Evtushenko, 2009
Suzuki and Hamada, 2009
Busse, 2009
Goodfellow, 2009;
Jones and Goodfellow, 2009
Evtushenko and Ariskina, 2009
Patrick and McDowell, 2009
Chemoorganotroph, anaerobic, fermentative
ED: nitrate, nitrite
Anaerobic, SRB, thermophilic
Carbon: organic compounds ED: H2, formate, pyruvate, lactate Sekiguchi et al., 2008
EA: Sulphate
Paster, 2010
Grabowski et al., 2005;
Nagaosa et al., 2008
Acosta‐Gonzalez et al., 2013
Glaubitz et al., 2013
Jong and Cho, 2012
Karlov et al., 2011
Chemoheterotrophic
Low‐temperature oil reservoir, Canada;
Groundwater, Japan
Oil polluted beach sediment, Spain;
Baltic Sea redoxcline, 119m depth
Yellow Sea continental shelf sediment;
Lake Radok Antarctica, depth 367m
Bernardet, 2010
144
Propionibacteriaceae Propionibacterium
Description; Isolation
APPENDIX J: General statistics of bacterial 16S rDNA and 16S rRNA sequences in the samples, and sample alpha
diversity estimates. OTUs reported at 97% clustering identity.
145
146
APPENDIX K: General statistics of archaeal 16S rDNA and 16S rRNA sequences in the samples, and sample alpha
diversity estimates. OTUs reported at 97% clustering identity.
147
148
APPENDIX L: General sequence statistics of fungal ITS sequences in the samples, and sample alpha diversity
estimates. OTUs reported at 97% clustering identity.
149
150
APPENDIX M: General statistics of dsrB sequences in the samples, and sample alpha diversity estimates. OTUs
reported at 97% clustering identity.
151
152
APPENDIX N: General statistics of mcrA sequences in the samples, and sample alpha diversity estimates. OTUs
reported at 97% clustering identity. 153
154
155
APPDENDIX O. Pyrosequencing fact sheet.
Method/concept
Operational
taxonomic units,
or OTUs
diversity
diversity indexes
species richness
species
richness
estimators
species evenness
observed number
of species
Description
Group of e.g. sequences sharing a
certain amount of similarity. Most
of the microbial species in the
environment are not culturable in
laboratory conditions and have thus
not
been
scientifically
characterized and described. The
presence of these species has only
been detected as gene fragments
obtained from the total microbial
DNA or RNA pool in different
environmental samples. As they
have no cultured close relatives,
these uncultured microorganisms
are often referred to belong to
certain operational taxonomic
units, or OTUs.
Schematic description of the division of units into
OTUs depending on pre-defined characteristics. A) The
total community of species (red, green and blue
squares and circles) is identified and determined to
belong to certain groups according to similarity, i.e.
into operational taxonomic units according to their
similarity. The division is determined by the user into
B) a rough or C) more fine division.
The biological diversity of a certain environment describes the total number of different
biological species in that environment, without regard to which species are present. Microbial
diversity describes the total number of microbial species or groups in a given environment.
The biological diversity of a sample or environment may be characterized by the actual
number of species or OTUs observed in a sample. For instance, if 20 different species or
OTUs are detected in a sampling, the observed number of species (or OTUs) is 20.
Environments are given so called diversity indexes, which take into account both the number
of observed species or OTUs as well as the amount of individuals (or sequence reads)
belonging to each of these species or OTUs and uses this combined information to estimate
diversity in terms of species abundance and evenness. The Shannon diversity index is high
in samples that have a large number of species and/or in samples where the species are evenly
distributed.
The species richness in a certain environment describes the total number of different species
in that environment. The higher the number of species the higher is the species richness. The
species richness between samples of different sizes may be compared by the use of
rarefaction curves.
Species richness estimators approximate the total number of species or OTUs that an
environment ultimately could contain based on the amount of units observed in the analysis.
There are different estimators available. The CHAO diversity estimate approximates the total
number of different species based on the relationship of OTUs that contain only one or two
sequences to the total number of OTUs.
Describes the distribution of individuals belonging to each species in a certain environment.
An environment with 10 mice and 10 squirrels has an even species distribution. An
environment with 2 mice and 18 squirrels has a very uneven species distribution in regards to
these two species.
The biological diversity of a sample or environment may be characterized by the actual
number of species or OTUs observed in a sample. For instance, if 20 different species or
OTUs are detected in a sampling, the observed number of species (or OTUs) is 20.
156
In a rarefaction calculation, the
species richness of a sample is
plotted as a function of the
number of individuals sampled,
or sequence reads obtained. If
the slope of the plotted curve is
steep, it indicates that a higher
number of species is likely to
be obtained with more intensive
sequencing (or sampling). If the
slope of the curve is low more
intensive sampling is likely to
yield only few additional
species.
rarefaction
A total community of individuals A), is randomly sampled
and the result is plotted as a function B) of the detected
sequences. The curve goes from steep to horizontal and
shows how many sequences/individuals must be sampled
so that the total number of different species have been
detected.
APPENDIX P: Characteristics of the main characterized archaeal taxa found from Olkiluoto groundwater samples.
Taxonomy
Crenarchaeota
MCG (Miscellaneous Crenarchaeota Group)
Description; Isolation
Carbon source; Electon donor; Electron acceptor Reference
Highly diverse uncultured group, thought to include heterotrophs; Most common in anoxic low‐
energy environments such as sediments where sulfate penetrates at least 10 cm, rare in oxic
habitats, lower abundance with high rates of AOM and sulfate reduction, likely anaerobes that
do not gain energy from AOM and sulfate is likely not an electron acceptor, possibly fermentors
of organic matter
Unknown; Unknown; Unknown
Kubo et al., 2012
Oxidizes ammonia to nitrite
Könneke et al., 2005
Cs: CH4 ; Ed: Various; Ea: Various
Knittel et al., 2005;
Jagersma et al., 2012
May belong to Methanomicrobia (Lyimo et al., 2008); putative methylotroph
Unknown; Unknown; Unknown
Lyimo et al., 2008;
Sørensen et al., 2005
Originally detected in deep South African gold mines, thought to be ubiquitous in subsurface
habitats
Unknown; Unknown; Unknown
Thaumarchaeota (often classified as their own
phylum)
Cenarchaeales (Nitrosopumilus)
Chemolithoautotrophic nitrifier; Mesophilic, psychrophilic
Euryarchaeota
ANME‐1
Uncultured AOM species from methane‐rich habitats, typically co‐occur with ANME‐2 and
abundance increases with depth, maybe more sensitive to oxygen than ANME‐2
DSEG (Deep Sea Euryarchaeotic Group)
ArcA07
Methanobacteria
Metanobacteriales ‐ MSBL1 ‐ SAGMEG‐1
Methanomicrobia
Methanosarcinales
Methanosarcinales ‐ ANME‐2D
Methanosarcinales ‐ Methanolobus sp.
Thermoplasmata
E2 ‐ TMEG (Terrestrial Miscellaneous
Euryarchaeotal Group)
E2 ‐ Methanomassiliicoccaceae
Parvarchaeota
pMC2A384
Anaerobic wide substrate range methanogens, can split acetate to CH4 and CO 2 , and catabolize
methyl compounds
Member is known that can perform nitrate‐driven AOM through reverse methanogenesis
Methylotrophic mesophile methanogen
Ed: H2 ; Ea: CO2 , methanol, methyl amines/sulfides Bonin & Boone, 2006
Ea: Nitrate
Methanol, methyl amines, methly sulfides
Haroon et al., 2013
Bonin & Boone, 2006
acidophiles, aerobes, anaerobes, mesophiles, thermophiles, sulfate‐reducers.
Unknown; Unknown; Unknown
Yashiro et al., 2011
Family includes a chemoheterotrophic methane‐producing methanol‐reducing mesophilic
anaerobic hydrogenotrophic member
Cs: not CO2 ; Ea: Methanol; Ed: H2
Iino et al., 2013
pMC2A384 is auxotrophic and can reduce sulfur to hydrogen sulfide (NADPH dependent)
Rinke et al., 2013; Baker
et al., 2010
157
Methanobacteriales ‐ Methanobacteriaceae Anaerobic mesophilic H2 oxidizing autotrophic methanogens
Takai et al., 2001; Teske
et al., 2008; Sørensen &
Teske, 2006
Ed: H2 , formate(?), secondary alcohols(?), CO(?); Bonin & Boone, 2006
Ea: CO2
158
Appendix Q. Bacterial, Archaeal and Fungal taxa with classification
Phyla
Number
of OTUs
Number of
Bacteria*
4,528
Archaea*
1,700
967
Taxa
Fungi**
Classes
% of
sequences
assigned to
Number of
47
83.7
3
83.0
5
70.9
Orders
% of
sequences
assigned to
Number of
127
79.4
17
71.8
19
68.9
Families
% of
sequences
assigned to
Number of
218
73.9
33
66.1
38
68.1
Genera
Species
% of
Number of sequences
assigned to
% of unidentified
domain specific
sequences
% of
sequences
assigned to
Number of
% of
sequences
assigned to
343
68.2
551
51.4
616
44.9
16.3
44
33.2
53
8.7
6
0.4
17.0
59
65.8
76
62.5
104
64.4
29.0
*Greengenes taxonomy
**Unite taxonomy
159
160