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Microbial ecology in the deep terrestrial biosphere: a geochemical, metagenomic and culture-based approach
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1
Microbial ecology in the deep terrestrial biosphere: a geochemical, metagenomic and
culture-based approach
Lily M. Momper
A Dissertation Submitted to the Faculty of the
USC Graduate School
University of Southern California
In Partial Fulfillment of the Requirements for the
Degree Doctor of Philosophy
(BIOLOGICAL SCIENCES)
Department of Biological Sciences
Marine Biology and Biological Oceanography
2016
Approved by
Advisory Committee
Chair
John F. Heidelberg Jan P. Amend Moh Y. El-Naggar
2
We certify that we have read this dissertation and that it satisfies our requirements and
expectations for a dissertation for the degree of Doctor of Philosophy in Biology.
DISSERTATION COMMITTEE
____________________________________________
Jan Amend, Chairperson
____________________________________________
John Heidelberg
____________________________________________
Moh El-Naggar, outside member
3
© Copyright 2016 by Lily M. Momper
All rights reserved
4
ACKNOWLEDGEMENTS
I dedicate this dissertation to my husband, William McClure, who had unfathomable faith
in my ability when I did not have it myself. Your support made this journey manageable,
enjoyable and sometimes even ludicrous.
I give great thanks to my primary mentor, Jan Amend, who adopted me into his lab when
I came knocking less than four years ago. I will be forever grateful that you gave me the
opportunity to complete my doctoral work with the NASA Astrobiology Institute Life
Underground team.
Thanks to the members of the Amend lab at USC, my companions over the last four
years. I am especially grateful to Guang Sin Lu who, when I first arrived in the Amend lab,
taught me the completely foreign skills necessary to culture anaerobes. Thank you to all of my
office mates who have made the past few years a very special time in my life. I hope it has been
in yours as well. Very special recognition to my office and lab mate Laura Zinke, who shared
many extremely late work nights with me and helped to make them productive and fun.
To Amanda Semler, the talented and dedicated undergraduate student I had the pleasure
to mentor: thank you so very much for working with me over the past year. I honestly do not
know how I could have completed so much laboratory work without your help. You will do great
things, and I am forever grateful I was able to be there at the beginning of your career.
Many thanks to the Sanford Underground Research Facility (SURF) personnel and staff
who made it possible for our team to safely access Earth’s deep, dark, and potentially dangerous
subsurface. Special thanks to Tom Ream, our guide while exploring the mine (SURF). He
facilitated and accommodated our sometimes inconvenient requests in the name of science.
Kathy Hart of the SURF team provided us with geologic maps of all the mine workings from the
Vulcan database. We thank her for those maps that were incorporated into this document.
And of course I must give thanks to my parents and siblings, who are always there,
cheering for me in the background. To my parents, thank you for instilling in me perseverance,
tenacity and a perverse enjoyment of challenge. Thanks especially to my sister Karen, who
endured my questions about Illustrator and admittedly made my graphics until I finally mastered
that program for myself.
Lastly, thank you to my committee members who have guided me toward the culmination
of the doctoral degree. The time you spent mentoring me and guiding me was and is greatly
appreciated.
5
ABSTRACT
Earth’s deep subsurface biosphere (DSB) supports an enormous store of microbial
life. Although difficult to access and sample, both marine and terrestrial subsurface
environments have recently been probed through ocean drilling programs and mine
exploration. The advent of high throughput sequencing has enabled molecular (DNA)
analyses of the microbial communities inhabiting the DSB that has resulted in a huge
expansion of the publicly available sequences from those global sites. In an effort to
understand better the microbial ecology of deep terrestrial habitats, we examined
bacterial and archaeal diversity in the Sanford Underground Research Facility (SURF) in
the former Homestake Gold Mine, South Dakota, USA. We extracted whole genomic
DNA from both deeply (~1.5 milometers below surface) circulating groundwater and
host rock. Pyrotag DNA sequencing of the 16S rRNA gene reveals diverse communities
of putative chemolithoautotrophs, aerobic and anaerobic heterotrophs, numerous
candidate phyla, and a unique rock-associated microbial assemblage. We then gathered
15 similarly sequenced global terrestrial and marine subsurface samples and compared
them against the SURF sequences and each other. Major biogeographic trends were
discovered. Among all terrestrial samples, the phylum Firmicutes is dominant (average
~33% of total sequences). We also performed metagenomic shotgun sequencing on the
same SURF fluids (1.5 kmbs). We reconstructed over 90 genomic bins from
metagenomic sequences, enabling investigation of common metabolic pathways in
terrestrial subsurface microbes. Sulfate and nitrate/nitrite reduction were the most
commonly recovered energy metabolisms among genomes. The most abundant
autotrophic carbon fixation pathway across all genomic bins was the reductive acetyl-
CoA pathway, presumably due to the anoxic conditions in the fluids and the relative
energetic efficiency of this pathway. More than a quarter (24) of our genomes belong to
bacterial phyla without any cultivated members; two of these genomes constitute the
most complete genomes of candidate phyla Omnitrophica (formerly OP3) and
Hydrogenedentes (formerly NKB19) to date. Within these near-complete candidate phyla
genomes we found the complete reductive acetyl-CoA carbon fixation pathway, and
genes for dissimilatory nitrate reduction. This is the first report of these metabolic
capabilities in the cosmopolitan subsurface phylum NKB19. This is one of only 4 studies
to date investigating microbial ecology in the deep terrestrial subsurface using next
generation (Illumina) sequencing technology. The activities of microorganisms in the
deep continental subsurface impact global carbon and nitrogen cycling. In this study we
6
show the first insights into the potential roles of candidate phyla OP3 and NKB19 in
these biogeochemical cycles. We were successful in isolating and characterizing a novel
genus from SURF fluids (Spirosphaera subterraneum gen. nov, sp. nov). It demonstrates
pleomorphy under high vs. low nutrient stress regimes, likely an adaptation to the
nutrient-poor, energy-limited deep subsurface. To our knowledge, this is the first report of an
isolate within the phylum Spirochaetes from the deep terrestrial subsurface. This is a pioneer
study investigating microbial diversity, metabolic capability and nutrient cycling in the
deep terrestrial subsurface. Specifically in this study we have found carbon fixation,
nitrate reduction and sulfate reduction pathways in candidate phyla found in global
habitats that had never before been attributed to those phyla.
7
TABLE OF CONTENTS
Acknowledgements 4
Abstract 5-6
Table of Contents 7-9
Guide to Abbreviations 10-12
Guide to Tables 13
Guide to Figures 14-15
1. Introduction: Investigating microbial ecology in the terrestrial deep biosphere
through the portal Sanford Underground Research Facility (SURF)
Coauthors: Magdalena Osburn, Douglas LaRowe, Jan Amend
Portions of this section are reprinted with permission from the journal Frontiers in
Microbiology: Extreme Microbiology.
1.1 Abstract 18
1.2 Background 19-23
1.3 Rationale 24
1.4 Goals of this study 25-29
2. Global analysis reveals distinct phylum-level differences between marine and
terrestrial subsurface microbial communities
8
Coauthors: Laura Zinke, Brandi Kiel-Reese, Gregory Wanger, Magdalena Osburn, Duane
Moser, Jan Amend
2.1 Abstract 32
2.2 Introduction 33-37
2.3 Methods 37-41
2.4 Results 41-50
2.5 Discussion 50-64
3. Metagenome analysis of fluids 1.5km below surface reveals new energy and carbon
metabolisms in microbial dark matter
Coauthors: Sean Jungbluth, Michael Lee, Jan Amend
3.1 Abstract 67
3.2 Introduction 68-70
3.3 Methods 70-75
3.4 Results 76-89
3.5 Discussion 89-112
9
4. Physiological Characterization of Low-Energy Adapted, Deep-Subsurface
Anaerobes: Spirosphaera subterraneum gen. nov., sp. nov. isolated from fluids 1.5
km in the terrestrial subsurface
Coauthors: Amanda Semler, Guang Sin Lu, Hiroyuki Imachi, Jan Amend
4.1 Abstract 115-116
4.2 Background and specialized culturing methods 116-119
4.3 Introduction 119-121
4.4 Methods 121-125
4.5 Results 125-130
4.6 Discussion 131-141
5. Concluding remarks
5.1 Concluding remarks 143-146
10
Guide to Abbreviations
AAI Average amino acid identity
ANI Average nucleotide identity
BLAST Basic Local Alignment Search Tool
bp base pairs (nucleic acid)
DCO Deep Carbon Observatory
DMA Dimethyl Acetal
DNA Deoxyribonucleic acid
DOE Department of Energy
DSB Deep subsurface biosphere
DUSEL Deep Underground Science & Engineering Laboratory
Etoliko Etoliko Lagoon, Western Greece
FAME Fatty Acid Methyl Ester
FCB Fibrobacteres–Chlorobi–Bacteroidetes superphylum
Ga Billion years ago
GEBA Genomic Encyclopedia of Bacteria and Archaea
gen. Genus
HPLC High performance liquid chromatography
11
IMG Integrated Microbial Genomes
IMG/ER Integrated Microbial Genomes Expert Review
JGI Joint Genome Institute
km kilometers
kmbs kilometers below surface
MDM Microbial dark matter
MUSCLE Multiple Sequence Comparison by Log-Expectation
NCBI National Center for Biotechnology Information
NKB-19 Nankai Trough bacterium clone 19 (aka Hydrogenedentes)
nov. Novel
OP3 Obsidian Pool bacterium clone 3 (aka Omnitrophica)
OP10 Obsidian Pool bacterium clone 10 (aka Armatimonadetes)
OP11 Obsidian Pool bacterium clone 11 (aka Microgenomates)
ORP Oxidation reduction potential
OTU Operational taxonomic unit
PBS Phosphate buffered saline
PhyML Phylogeny maximum likelihood
12
PVC Planctomycetes-Verrucomicrobia-Chlamydiae
superphylum
QIIME Quantitative Insights Into Microbial Ecology
RDP Ribosomal Database Project
rRNA ribosomal ribonucleic acid
SAG Single-cell amplified genome
SAK Lake Sakinaw
sp. Species
SSU small subunit
SURF Sanford Underground Research Facility
UPGMA Unweighted Pair Group Method with Arithmetic Mean
VAMPS Visualization & Analysis of Microbial Population Structure
WS3 Wurtsmith aquifer Sequences-3 (aka Latescibacteria)
13
Guide to Tables
Table
2.1 Physical and geochemical parameters for borehole fluid and industrial water 43
2.2 Complete list of sites and metadata for samples analyzed in Chapter 2 52
3.1 Sample metadata and shotgun sequencing results 78
3.2 List of KEGG enzymes used for carbon fixation analyses in genome bins 81
3.3 Genomic bin information for four candidate phyla compared in this study 85
4.1 Fatty acid composition of strain SURF-ANA1 128
4.2 Differential phenotypic characteristics of strain SURF-ANA1, S. caldaria,
S. psychropila and E. thermophila 132
14
Guide to Figures
Figure
1.1 Cross section of the former Homestake mine 22
1.2 Diagram of Sanford Underground Research Facility (SURF) as proposed by the
National Science Foundation (NSF) 23
1.3 Photographs of exemplary borehole sample sites within SURF 26
2.1 Cross section and plan view of Sanford Underground Research Facility 36
2.2 Manifold at borehole DUSEL-B and rock cores collected immediately after drilling 37
2.3 Comparison between ultra-deep fluid and rock associated sequences at the
OTU and order levels 46
2.4 Global map of sample sites analyzed in Chapter 2. Terrestrial sites in red,
marine sites in blue. 49
2.5 Taxonomic breakdown of all marine and terrestrial sites at the phylum
and class levels. Marine sites in blue scale, terrestrial sites in red scale. 54
3.1 Shot gun sequencing results and quality scores across all base pairs for
Illumina short reads 77
15
3.2 Overview of genomic bin size, differential coverage, and metabolic
gene presence/absence. 80
3.3 Completeness and contamination values for all genome bins 83
3.4 Phylogeny based on 16S rRNA sequence analysis. Genomes indicated by
colored circles to right of taxonomic branch. 86
3.5 Carbon fixation pathways found in genomic bins. Dark red indicates that all
genes coding for irreversible enzymes involved in prokaryotic carbon
fixation are present, white indicates no genes belonging to that pathway are
present. 88
3.6 Average nucleotide (ANI) and amino acid (AA) comparisons for candidate
phyla a) Hydrogenedentes b) Woesarchaeota c) Omnitrophica and d)
Latescibacteria 92
3.7 Anabolic and catabolic pathways in Bin_25 (NKB-19). A) Cytosolic
nitrate reduction to nitrite. B) Carbon fixation via the reductive acetyl CoA
pathway C) Sulfate reduction 96
4.1 Schematic of a low flow, low energy bioreactor used to cultivate deep subsurface
microbes 118
4.2 Strain SURF-ANA1
T
under phase contrast and fluorescence microscopy 126
4.3 Scanning electron micrographs of strain SURF-ANA1
T
under different nutrient
regimes and stressors 127
4.4 Phylogenetic tree of the 16S rRNA gene sequence for SURF-ANA1
T
130
16
CHAPTER 1
Introduction: Investigating microbial ecology in the terrestrial deep biosphere through
the portal Sanford Underground Research Facility (SURF)
By Lily Momper
Coauthors: Magdalena Osburn, Douglas LaRowe and Jan Amend
Portions of this section are reprinted with permission from the journal Frontiers in
Microbiology: Extreme Microbiology.
In part published in: Chemolithotrophy in the continental deep subsurface: Sanford Underground
Research Facility (SURF), USA.
17
‘Is
the
Master
out
of
his
mind?'
she
asked
me.
I
nodded.
'And
he's
taking
you
with
him?'
I
nodded
again.
'Where?'
she
asked.
I
pointed
towards
the
centre
of
the
earth.
'Into
the
cellar?'
exclaimed
the
old
servant.
'No,'
I
said,
'farther
down
than
that.’
―
Jules
Verne,
Journey
to
the
Center
of
the
Earth
18
1.
1
Abstract
Earth’s
deep
subsurface
harbors
an
enormous
reservoir
of
microbial
life.
However,
the
metabolic
capabilities
of
these
microorganisms
and
the
degree
to
which
they
are
connected
to
surface
environments
are
largely
unknown.
Due
to
the
logistical
difficulty
of
sampling
and
its
inherent
heterogeneity,
the
microbial
ecology
of
the
terrestrial
subsurface
is
particularly
poorly
characterized.
In
an
effort
to
understand
better
the
biogeochemistry
of
deep
terrestrial
habitats,
a
holistic
evaluation
of
chemolithoautotrophic
microbial
metabolisms
in
the
Sanford
Underground
Research
Facility
(SURF)
in
the
former
Homestake
Gold
Mine,
SD,
USA
has
been
undertaken.
Geochemical
data,
energetic
modeling,
and
DNA
sequencing
of
constituent
microbial
populations
have
been
combined
to
describe
this
deep,
terrestrial
environment.
SURF
provides
access
into
a
deep
(down
to
8100
ft
below
surface),
iron-‐rich
Paleoproterozoic
sedimentary
deposit
that
is
plumbed
by
numerous
sources
of
deeply
circulating
groundwater.
Geochemical
analyses
of
subsurface
fluids
reveal
enormous
geochemical
diversity
ranging
widely
in
salinity,
oxidation
state
(ORP
330
to
-‐328
mV),
and
concentrations
of
redox
sensitive
species
(e.g.,
Fe
2+
from
bdl
to
6.2
mg/L
and
ΣS
2-‐
from
7
to
2778
μg/L).
Pyrotag
DNA
sequencing
reveals
diverse
communities
of
chemolithoautotrophs,
thermophiles,
both
aerobic
and
anaerobic
heterotrophs,
and
numerous
uncultivated
clades.
Total
metagenomic
sequencing
of
fluids
4850
feet
below
surface
allowed
reconstruction
of
90
individual
genomes,
26
of
which
belong
to
the
‘microbial
dark
matter,’
bacterial
and
archaeal
phyla
for
which
there
are
no
cultured
representatives.
19
1.
2
Background
Earth’s
deep
subsurface
harbors
an
enormous
reservoir
of
microbial
life.
Earlier
estimates
for
the
number
of
microbes
and
the
mass
of
their
constituent
carbon
far
outweighed
those
in
surficial
environments
(Whitman
et
al.,
1998).
More
recently,
size
estimates
of
the
marine
subsurface
have
been
revised
downward
after
more
representative
sampling
revealed
that
older
estimates
were
profoundly
biased
towards
the
organic-‐rich,
coastal
sediments
that
contain
high
numbers
of
microorganisms
(Kallmeyer
et
al.,
2012;
Hinrichs
and
Inagaki,
2012).
A
recent
review
by
McMahon
and
Parnell
(2013)
has
reevaluated
the
size
of
the
terrestrial
deep
subsurface
biosphere
(DSB),
incorporating
new
study
locations,
cell
density
estimates,
porosity
data,
and
revised
understanding
of
the
amount
of
carbon
in
cells.
They
estimate
the
mass
of
the
terrestrial
DSB
to
be
14
–
135
Pg
C,
roughly
two
thirds
that
given
by
Whitman
(22
–
215
Pg
C)
(McMahon
and
Parnell,
2013).
Notably,
these
estimates
remain
larger
than
or
on
par
with
the
1.5
–
22
Pg
C
thought
to
be
in
marine
sediments
(Kallmeyer
et
al.,
2012),
26
Pg
C
attributed
to
terrestrial
soil,
and
2.2
Pg
C
estimated
for
aquatic
environments
(Whitman
et
al.,
1998).
Recent
efforts
focused
on
the
study
of
terrestrial
subsurface
sites
are
providing
data
that
can
be
used
to
determine
better
the
total
mass
of
the
terrestrial
subsurface
biosphere
and
its
connection
to
the
surface
world.
For
example,
billion
year
old
water
and
associated
ecosystems
in
the
Canadian
Shield
have
been
described
(Holland
et
al.,
2013),
a
monophyletic
community
in
deep
South
African
gold
mines
has
been
reported
(Chivian
et
al.,
2008),
and
life
in
seemingly
impossible
ultrabasic
conditions
now
seems
likely
(Brazelton
et
al.,
2012).
20
The
former
Homestake
gold
mine
in
Lead,
South
Dakota
(USA)
is
an
excellent
platform
for
sustained
study
of
the
continental
DSB.
Now
the
Sanford
Underground
Research
Facility
(SURF),
this
location
offers
repeated
and
reliable
access
down
to
4850
feet
below
surface
(fbs).
The
full
footprint
of
the
mine
spans
over
300
miles
of
underground
tunnels
and
shafts
reaching
to
8100
fbs
(Caddey
et
al.,
1991).
Exploratory
boreholes
reach
hundreds
of
meters
into
environments
otherwise
unaffected
by
human
activities.
Environments
within
SURF
are
highly
variable
with
respect
to
host
geology
(Caddey
et
al.,
1991)
and
hydrogeology
(Rahn
and
Roggenthen,
2002).
The
mine
infrastructure
intersects
three
major
Paleoproterozoic
sedimentary
formations:
the
Poorman,
Homestake
and
Ellison
formations,
interrupted
by
younger
rhyolitic
intrusives
(Caddey
et
al.,
1991).
This
highly
variable
geology
in
turn
yields
the
highly
variable
geochemical
microenvironments
we
have
accessed
for
this
study.
Hydrological
modeling
separates
the
mine
into
three
major
recharge
capture
zones
(Murdoch
et
al.,
2011),
which
are
separated
based
on
average
time
of
recharge
from
meteoric
sourced
water:
shallow
(~1
year),
mid
(10-‐100
years)
and
deep
(>1000
years)
(Figure
1).
The
storage
capture
waters
underlie
the
recharge
capture
zones
and
have
flowed
in
the
subsurface
for
hundreds
to
thousands
of
years.
These
waters
follow
an
upward
path
where
a
portion
percolates
into
the
deeper
mine
workings
(Murdoch
et
al.,
2011).
Although
the
host
geology
at
SURF
is
well
characterized
(Bachman
and
Cadey,
1990;
Caddey
et
al.,
1991;
Davis
et
al.,
2009;
Rahn
and
Roggenthen,
2002),
its
biology
has
been
minimally
investigated.
Previous
studies
at
SURF
have
used
16S
rRNA
clone
libraries
and
21
microarrays
to
characterize
the
resident
microbial
communities
(Rastogi
et
al.,
2009;
Rastogi
et
al.,
2010;
Waddell
et
al.,
2010).
Rastogi
et
al.
identified
57
bacterial
taxonomic
units
that
could
not
be
classified
on
even
the
phylum
level;
greater
than
95%
of
the
16S
clone
sequences
were
most
closely
related
to
uncultivated
species.
However,
these
studies
were
comprised
of
only
two
samples,
both
from
the
same
depth
(1.34
kbs
or
4850
fbs).
Vast
improvements
in
sequencing
technology
and
affordability
have
enabled
us
to
expound
upon
these
earlier
studies,
both
with
regard
to
the
number
of
sequences
analyzed
and
the
number
of
sites
sampled.
In
the
current
study,
microbiological
and
geochemical
data
from
a
new
portal
into
the
deep
terrestrial
biosphere,
the
Sanford
Underground
Research
Laboratory
(SURF)
in
the
former
Homestake
Gold
Mine,
South
Dakota
USA,
are
presented.
The
Homestake
Gold
Mine,
active
from
1876
-‐2001,
produced
1,101
tons
of
gold
from
tunnels
as
deep
as
8100
ft
(Caddey,
1991).
A
detailed
diagram
of
the
evolution
of
the
former
Homestake
mine
and
the
lodes
of
gold
that
were
accessed
during
its
125
year
operation
can
be
found
in
Figure
1.1.
Since
mining
activity
ceased,
the
site
was
adapted
to
a
state-‐run
science
facility
primarily
focused
on
particle
physics
(Figure
1.2),
and
which
has
recently
been
explored
by
our
scientific
team,
members
of
the
NASA
Astrobiology
Institute,
Life
Underground.
22
Figure
1.1
Cross
section
view
of
ledges
mined
at
the
former
Homestake
gold
mine,
Lead,
South
Dakota.
Ledges
were
mined
progressively
to
access
deeper
gold
deposits
(http://homestake.sdsmt.edu/).
23
Figure
1.2
Sanford
Underground
Research
Facility
(SURF)
as
a
scientific
laboratory
as
proposed
by
the
National
Science
Foundation
(adapted
from
figure
by
Zina
Deretsky,
National
Science
Foundation)
Mining
tunnels
intersect
three
Paleoproterozoic
metasedimentary
units,
the
Poorman,
Homestake,
and
Ellison
formations,
with
exploratory
boreholes
extending
well
beyond
the
primary
mining
footprint
(Caddey,
1991).
Hydrological
modeling
indicates
relatively
shallow
meteoric
input
in
upper
mining
levels
and
much
older
(>10,000
yrs)
fluids
reaching
the
deeper
levels,
especially
on
the
northern
ledges
(Murdoch
et
al.,
2011).
SURF
Sanford U nderground
R esearch F acility
24
Previous
microbiological
studies
at
SURF
have
focused
on
the
mine
tunnel
environment,
primarily
for
the
identification
of
industrially
relevant
cellulose
degrading
bacteria
(Rastogi
et
al.,
2013;
2009;
2010).
Here,
the
mine
is
used
to
access
the
in
situ
subsurface
biosphere,
i.e.,
habitats
that
have
been
least
affected
by
mining
activity.
1.3
Rationale
The
hydrological
and
geological
variability
present
in
the
continental
subsurface
can
be
much
greater
than
in
marine
environments.
Consequently,
a
multifaceted
approach
is
necessary
to
characterize
the
environmental
complexities
of
deep
terrestrial
ecosystems,
including
understanding
the
feeding
aquifer
and
the
host
lithologies
encountered
by
subsurface
fluids.
One
benefit
of
the
mine
environment
is
that
this
information
is
often
available
as
it
is
critical
to
both
the
economics
of
mining
and
structural
safety.
Because
the
fluids
circulating
in
the
subsurface
near
SURF
have
interacted
with
different
lithologies
for
varying
periods
of
time,
the
geochemistry
of
the
liquids
that
appear
in
the
mine
differ
depending
on
where
they
are
sampled.
The
variable
salinities,
pHs,
temperatures,
and
oxidation
states
will
in
turn
support
different
microbial
communities
that
can
be
probed
using
modern
biomolecular
techniques,
such
as
pyrosequencing.
Although
these
techniques
have
limited
utility
in
connecting
sequence
identity
to
function,
they
can
be
used
to
rapidly
assess
the
microbial
diversity
of
an
environmental
and
to
infer
putative
catabolic
strategies.
We
also
performed
next
generation
metagenomic
sequencing
on
the
deepest
fluids
from
SURF
and
identified
almost
100
Bacterial
and
Archaeal
genomes,
some
of
which
belong
to
novel
phyla
never
previously
sequenced.
Here,
we
combined
these
techniques
and
present
a
characterization
of
the
geochemical
habitat,
microbiome,
and
energetic
framework
for
chemoautotrophic
processes
of
the
subsurface
biosphere
at
SURF.
25
1.4
Goals
of
This
Study
Due
to
the
logistical
difficulty
of
sampling
and
its
inherent
heterogeneity,
the
microbial
ecology
of
the
terrestrial
subsurface
is
particularly
poorly
characterized.
In
an
effort
to
better
understand
the
biogeochemistry
of
deep
terrestrial
habitats,
a
holistic
evaluation
of
chemolithoautotrophic
microbial
metabolisms
in
the
Sanford
Underground
Research
Facility
(SURF)
in
the
former
Homestake
Gold
Mine,
SD,
USA
has
been
undertaken
here,
detailed
in
the
following
chapters.
A
few
examples
of
the
sorts
of
boreholes
and
subsurface
fluids
that
were
sampled
for
this
study
are
found
in
Figure
1.3.
Geochemical
data,
energetic
modeling,
and
targeted
gene
and
metagenomic
DNA
sequencing
of
constituent
microbial
populations
have
been
combined
to
describe
this
deep,
terrestrial
environment.
SURF
provides
access
into
a
deep
(down
to
8100
ft
below
surface),
iron-‐rich
Paleoproterozoic
sedimentary
deposit
that
is
plumbed
by
numerous
sources
of
deeply
circulating
groundwater.
Geochemical
analyses
of
subsurface
fluids
reveal
enormous
geochemical
diversity
ranging
widely
in
salinity,
oxidation
state
(ORP
330
to
-‐328
mV),
and
concentrations
of
redox
sensitive
species
(e.g.,
Fe
2+
from
bdl
to
6.2
mg/L
and
ΣS
2-‐
from
7
to
2778
μg/L).
Pyrotag
DNA
sequencing
reveals
diverse
communities
of
chemolithoautotrophs,
thermophiles,
both
aerobic
and
anaerobic
heterotrophs,
and
numerous
uncultivated
clades
(Chapter
2).
Metagenomic
sequencing
analysis
of
in
situ
microbial
communities
collected
from
fluids
4850
below
surface
has
revealed
abundant
genes
involved
in
nitrogen,
sulfur
and
carbon
cycling
(specifically
via
autotrophic
carbon
fixation).
Additionally,
over
two
dozen
genomes,
reconstructed
from
the
metagenomes,
have
been
identified
as
members
of
the
‘microbial
dark
matter,’
prokaryotic
phyla
that
have
26
Figure
1.3
Photographs
of
HMC-‐10-‐1
and
biofilm
(top
left),
HMC-‐19223
and
biofilm
(top
right),
HMC-‐11938
(bottom
left)
and
SURF-‐B
(bottom
right).
1 2
3 4
27
no
cultured
representatives
and
for
which
we
have
only
culture-‐independent
information
(Chapter
3).
In
an
effort
to
understand
better
the
metabolic
capabilities
of
deep
subsurface
dwellers,
we
have
attempted
to
isolate
and
characterize
novel
Bacteria
and
Archaea
collected
4850
below
surface
at
SURF.
We
successfully
isolated
a
multiple
new
species,
most
notably
a
new
genus
and
species
of
Bacteria,
proposed
Spirospharea
subterraneum,
gen.
nov.,
sp.
nov.
that
can
grow
chemoautotrophically
using
hydrogen
as
an
electron
donor
and
fixing
carbon
dioxide
for
a
carbon
source
(Chapter
4).
In
the
following
chapters
we
investigate
the
deep
subsurface
biosphere
using
next
generation
sequencing
technology,
and
previously
published
hydrology
(Murdoch
et
al.,
2011)
and
thermodynamic
modeling
(Osburn
et
al.,
2014)
to
understand
what
factors
control
SURF’s
subsurface
microbial
community
diversity.
References
Brazelton,
W.
J.,
Nelson,
B.,
and
Schrenk,
M.
O.
(2012).
Metagenomic
evidence
for
H2
oxidation
and
H2
production
by
serpentinite-‐hosted
subsurface
microbial
communities.
Frontiers
in
Microbiology
2,
1–16.
doi:10.3389/fmicb.2011.00268/abstract.
Caddey,
S.
W.
(1991).
The
Homestake
Gold
Mine,
an
Early
Proterozoic
iron-‐formation-‐
hosted
gold
deposit,
Lawrence
County,
South
Dakota.
Chivian,
D.,
Brodie,
E.L.,
Alm,
E.J.,
and
Culley,
D.E.
(2008)
Environmental
genomics
reveals
a
single-‐species
ecosystem
deep
within
Earth.
Science
322:
275-‐278.
28
Hinrichs,
K.-‐U.,
and
Inagaki,
F.
(2012).
Biogeochemistry.
Downsizing
the
deep
biosphere.
Science
338,
204–205.
doi:10.1126/science.1229296.
Kallmeyer,
J.,
Pockalny,
R.,
Adhikari,
R.
R.,
Smith,
D.
C.,
and
D'Hondt,
S.
(2012).
Global
distribution
of
microbial
abundance
and
biomass
in
subseafloor
sediment.
Proceedings
of
the
National
Academy
of
Sciences
of
the
United
States
of
America
109,
16213–16216.
doi:10.1073/pnas.1203849109.
Murdoch,
L.
C.,
Germanovich,
L.
N.,
Wang,
H.,
Onstott,
T.
C.,
Elsworth,
D.,
Stetler,
L.,
and
Boutt,
D.
(2011).
Hydrogeology
of
the
vicinity
of
Homestake
mine,
South
Dakota,
USA.
Hydrogeology
Journal
20,
27–43.
doi:10.1007/s10040-‐011-‐0773-‐7.
Rastogi,
G.,
Gurram,
R.
N.,
Bhalla,
A.,
Gonzalez,
R.,
Bischoff,
K.
M.,
Hughes,
S.
R.,
Kumar,
S.,
and
Sani,
R.
K.
(2013).
Presence
of
glucose,
xylose,
and
glycerol
fermenting
bacteria
in
the
deep
biosphere
of
the
former
Homestake
gold
mine,
South
Dakota.
Frontiers
in
Microbiology
4,
18–18.
doi:10.3389/fmicb.2013.00018.
Rastogi,
G.,
Osman,
S.,
Kukkadapu,
R.,
Engelhard,
M.,
Vaishampayan,
P.
A.,
Andersen,
G.
L.,
and
Sani,
R.
K.
(2010).
Microbial
and
Mineralogical
Characterizations
of
Soils
Collected
from
the
Deep
Biosphere
of
the
Former
Homestake
Gold
Mine,
South
Dakota.
Microbial
Ecology
60,
539–550.
doi:10.1007/s00248-‐010-‐9657-‐y.
Rastogi,
G.,
Stetler,
L.
D.,
Peyton,
B.
M.,
and
Sani,
R.
K.
(2009).
Molecular
analysis
of
prokaryotic
diversity
in
the
deep
subsurface
of
the
former
Homestake
gold
mine,
South
Dakota,
USA.
J
Microbiol.
47,
371–384.
doi:10.1007/s12275-‐008-‐0249-‐1.
29
Whitman,
W.
B.,
Coleman,
D.
C.,
and
Wiebe,
W.
J.
(1998).
Prokaryotes:
The
unseen
majority.
Proceedings
of
the
National
Academy
of
Sciences
95,
6578–6583.
30
CHAPTER
2
Global
analysis
reveals
distinct
phylum-‐level
differences
between
marine
and
terrestrial
subsurface
microbial
communities
By
Lily
Momper
Coauthors:
Laura
Zinke,
Brandi
Kiel
Reese,
Gregory
P.
Wanger,
Magdalena
Osburn
and
Jan
Amend
In
preparation
for
Environmental
Microbiology
31
Science, my boy, is made up of mistakes, but they are mistakes which it is useful to make,
because they lead little by little to the truth.
― Jules Verne, Journey to the Center of the Earth
32
2.1
Abstract
Earth’s
deep
subsurface
biosphere
(DSB)
supports
an
enormous
store
of
microbial
life.
Although
difficult
to
access
and
sample,
both
marine
and
terrestrial
subsurface
environments
have
recently
been
probed
through
drilling
programs,
exploration
of
mines,
and
surface
expressions
of
deeply-‐sourced
fluids.
The
advent
of
high-‐throughput
sequencing
has
enabled
molecular
(DNA)
surveys
of
the
microbial
community
inhabiting
the
DSB
and
with
those
surveys
came
a
huge
expansion
of
the
publicly-‐available
sequences
from
those
global
sites.
In
an
effort
to
better
understand
the
microbial
ecology
of
deep
terrestrial
habitats,
I
examined
bacterial
diversity
in
the
Sanford
Underground
Research
Facility
(SURF),
the
former
Homestake
Gold
Mine
in
South
Dakota,
USA.
I
extracted
whole
genomic
DNA
from
deeply-‐circulating
groundwater
and
others
on
our
team
extracted
from
corresponding
host
rock
(~1.5
kilometers
below
surface).
Pyrotag
DNA
sequencing
of
the
16S
rRNA
gene
reveals
diverse
communities
of
putative
chemolithoautotrophs,
aerobic
and
anaerobic
heterotrophs,
numerous
candidate
phyla,
and
a
unique
rock-‐associated
microbial
assemblage.
Sequencing
data
from
SURF
compared
against
more
than
a
dozen
similarly-‐sequenced
global
terrestrial
and
marine
subsurface
samples
revealed
clear
biogeographic
trends.
In
all
terrestrial
samples,
the
phylum
Firmicutes
is
dominant
(average
~33%
of
total
sequences).
In
marine
sites,
however,
Firmicutes
were
almost
completely
absent
(average
<5%
of
total
sequences),
and
the
phyla
Actinobacteria
and
Chloroflexi
dominated.
As
more
DSB
samples
become
available
we
will
further
elucidate
this
global
biogeographic
partitioning
between
marine
and
terrestrial
bacterial
communities.
33
2.2
Introduction
The
largest
habitable
environments
on
earth
are
below
the
surface,
devoid
of
light
and
difficult
to
access
(Whitman
et
al.,
1998;
Amend
and
Teske,
2005;
Kallmeyer
et
al.,
2012;
Edwards
et
al,
2012;
Colwell
and
D'Hondt,
2013;
Parkes
et
al.,
2014).
It
is
estimated
that
the
marine
subseafloor
sediment
and
terrestrial
subsurface
account
for
6-‐30
×
10
29
microbial
cells
(Whitman
et
al.,
1998;
Kallmeyer
et
al.,
2012).
Although
the
marine
and
terrestrial
subsurface
biospheres
likely
account
for
the
majority
of
the
organic
carbon
reservoir
on
Earth,
geochemical
and
molecular
datasets
are
isolated.
Comprehensive
surveys
of
microbial
diversity
and
geographic
distribution
are
sparse
relative
to
the
immense
volume
of
the
deep
subsurface
biosphere
(DSB)
(Colwell
and
D’Hondt,
2013).
However,
community-‐driven
sequencing
efforts
through
the
Deep
Carbon
Observatory
(DCO)
and
Joint
Genome
Institute
(JGI),
for
example,
have
enabled
global
surveys
of
both
targeted
locus
(e.g.,
16S
rRNA)
and
metagenomic
sequencing.
Publicly
accessible
servers
such
as
Visualization
and
Analysis
of
Microbial
Population
Structures
(VAMPS)
and
the
Ribosomal
Database
Project
(RDP)
have
facilitated
analysis
and
integration
of
these
data
on
a
scale
larger
than
ever
before
possible.
The
‘deep’
biosphere
has
been
defined
often
and
sometimes
arbitrarily.
In
marine
environments,
distinct
physical
boundaries
have
led
some
investigators
to
declare
the
shallow-‐to-‐deep
transition
at
10
cm
to
10
m
below
the
sediment
water
interface
(Whitman
and
Coleman,
1998;
Jørgensen
and
Boetius,
2007;
Edwards,
Becker,
and
Colwell,
2012).
In
terrestrial
environments,
a
more
functional
definition
mandates
that
the
DSB
be
independent
from
photosynthetically
derived
organic
matter
and
reliant
on
endogenous
34
sources
of
energy
(Fredrickson
and
Onstott,
1996;
Stevens,
1997).
However,
it
is
nearly
impossible
to
know
definitively
that
a
subsurface
environment
is
truly
independent
from
surface-‐derived
products.
For
the
purposes
of
this
study,
‘deep’
is
a
functional
definition
rather
than
a
physical
one.
Similar
to
Orcutt
et
al.,
(2011)
and
Loveley
and
Chapelle
(1995)
we
define
the
DSB
as
an
absence
of
photosynthesis
and
a
temporal
hydrologic
separation
from
surface
waters.
Because
of
practical
difficulties
in
sampling,
and
the
relatively
recent
discovery
that
life
persists
kilometers
below
the
surface
(refs),
this
biosphere
remains
ill
explored.
In
the
terrestrial
realm,
active
and
legacy
mines
have
proven
to
be
useful
locations
for
accessing
the
DSB
and
the
microbial
communities
therein
because
of
their
pre-‐existing
infrastructure
and
depths
(Moser
et
al.,
2005;
Onstott
et
al.,
2003;
Sahl
et
al.,
2008;
Shimizu
et
al.,
2007;
Toner
et
al.,
2012).
The
former
Homestake
gold
mine
in
Lead,
South
Dakota
(USA)
is
an
excellent
platform
for
sustained
study
of
the
continental
DSB.
Now
the
Sanford
Underground
Research
Facility
(SURF),
this
mine
contains
over
500
km
of
tunnels
and
shafts
that
reach
~2.5
km
below
surface
(kmbs)
(Figure
2.1a).
(Caddey
et
al.,
1991).
However,
owing
to
flooding
from
below,
access
is
currently
available
from
the
surface
to
~1.5
kmbs.
Legacy
exploratory
boreholes
reach
hundreds
of
meters
horizontally
into
host
rock
and
environments
otherwise
unaffected
by
human
activities.
Since
2013,
members
of
the
Life
Underground
NASA
Astrobiology
Institute
have
repeatedly
accessed
sites
within
SURF
down
to
the
deepest
accessible
levels
at
~1.5
kmbs.
In
this
study,
we
examine
four
samples
from
SURF
at
that
level:
two
borehole
fluids
and
two
rock
core
segments
within
an
exploratory
borehole.
The
SURF-‐designated
names
given
35
to
these
boreholes
at
the
time
of
drilling
are
DUSEL-‐B,
DUSEL-‐D
and
LBNE-‐3.
These
are
the
official
names
in
SURF
archives.
However,
for
the
sake
of
simplicity
we
will
hereafter
refer
to
the
fluid
samples
as
FL-‐SURF-‐B
(DUSEL-‐B)
and
FL-‐SURF-‐D
(DUSEL-‐D)
that
were
drilled,
horizontally
in
different
directions,
600
ft
(180
m)
and
900
ft
(270
m),
respectively,
into
host
rock.
Both
of
these
fluids
intersect
the
Poorman
Formation
(the
Yates
Unit
is
an
informal
designation
given
to
the
amphibolite-‐rich
lowest
unit
of
the
Poorman
formation)
(Figure
2.1a).
Rock
core
was
collected
from
LBNE-‐3,
a
borehole
that
also
primarily
intersects
the
Poorman
Formation
(Figure
2.1b).
Genomic
DNA
was
extracted
from
two
depths
within
LBNE:
418
and
613
horizontal
feet
(125
m
and
184
m,
respectively)
into
the
host
rock.
These
rock
samples
will
hereafter
be
referred
to
as
RX-‐SURF-‐418
and
RX-‐SURF-‐
613.
Photographs
of
both
legacy
boreholes
and
freshly
drilled
core
examined
in
this
study
are
shown
in
Figure
2.2.
Here,
we
collected,
extracted
and
analyzed
sequences
of
the
16S
rRNA
gene
from
borehole
fluid
and
freshly
drilled
rock
core
from
1.45
km
below
surface
at
SURF.
We
then
compared
our
results
to
publicly
available,
similarly-‐sequenced
terrestrial
and
marine
deep
subsurface
16S
rRNA
datasets
in
order
to
gain
a
biogeographic
understanding
of
DSB
microbial
communities.
We
address
the
differences
and
similarities
between
fluid
and
rock
sequences
obtained
from
the
same
geographic
location
(SURF).
36
Figure
2.1.
Sanford
Underground
Research
Facility
(SURF)
cross-‐section
and
plan
view
map.
A.
cross
section
of
mine
workings
at
SURF.
Blue
concentric
circles
indicate
hydrologic
recharge
zones,
adapted
from
Murdock
et
al.,
2012.
B.
plan
view
of
4850
foot
level
(1.45
km).
Yates
shaft
included
for
orientation.
FL-‐SURF-‐B
and
–D
boreholes
indicated
by
red
circle
in
mine.
RX-‐SURF-‐418
and
-‐613
were
retrieved
from
LBNE-‐3.
LEAD
1km
2km
3km
4km
5km
TAILINGS
POND
4500
8500
CUT
OPEN
ROSS SHAFT
YATES SHAFT
DUSEL&B(
DUSEL&D(
0.1(km(
B(
A(
1(km(
B(
37
2.3.
Methods
2.3.1
Sample
site
description
All
samples
were
collected
during
expeditions
to
SURF
in
Lead,
South
Dakota,
USA
between
September,
2013
and
March,
2014.
Two
legacy
boreholes
sampled
in
this
study
were
sites
DUSEL-‐B
and
DUSEL-‐D,
four-‐inch
(10
cm)
diameter
boreholes
that
reach
horizontally
400
ft
(120
m)
and
900
ft
(270
m)
into
the
host
rock,
respectively
(Figure
2.1).
Rock
cores
were
collected
from
freshly
drilled
boreholes
during
a
drilling
expedition
in
April
2014
(Figure
2.2).
Cores
were
collected
on
site,
immediately
vacuum-‐sealed
and
Figure 2.2 A. Manifold at DUSEL-B with filter line attached. B and
C. Rock cores from RX-SURF-418 and -613 immediately after drilling.
B"
A
C"
38
transferred
to
Mylar
bags
to
preserve
anoxic
conditions,
and
stored
on
dry
ice
for
transport
back
to
the
laboratory.
There,
they
were
stored
at
-‐80
o
C
until
analysis.
2.2.2
Geochemical
field
and
laboratory
measurements
Geochemical
measurements
including
oxidation-‐reduction
potential
(ORP),
conductivity,
pH,
temperature,
and
total
dissolved
solids
were
measured
in
situ
with
an
Ultrameter
II
6PFC
E
(Myron
L
Company,
Carlsbad,
CA,
USA).
Samples
for
redox
sensitive
species
(DO,
∑S
2-‐
,
Fe
++
,
Mn,
NO3
-‐
,
NO2
-‐
,
NH4
+
,
SiO2,
ΣPO4
3-‐
),
major
anions,
major
cations
and
dissolved
gases
were
collected
concomitantly
and
preserved
for
laboratory
analysis
as
described
in
Osburn
et
al.
(2014).
Dissolved
gases
were
collected
using
the
bubble
stripping
method,
as
described
in
Alter
and
Steiof
(2005),
and
measured
on
a
Shimadzu
GC-‐
2014ATF
gas
chromatograph
equipped
with
TCD
and
FID
detectors.
The
same
suite
of
geochemical
and
molecular
samples
was
also
collected
from
industrial
water.
Industrial
water
is
sourced
from
a
local
shallow
aquifer
and
has
been
used
during
drilling
of
exploratory
boreholes.
The
industrial
drill
water
sample
included
in
this
study
was
collected
during
the
drilling
of
an
800-‐meter
horizontal
borehole
on
the
4850-‐
foot
level
in
March
of
2014.
2.3.3
DNA
collection,
extraction
and
sequencing
For
borehole
fluid
samples,
total
microbial
cells
were
collected
at
each
station
on
47
mm,
0.2µm
Supor
filters
(Pall
Corporation,
Port
Washington,
NY,
USA).
Whole
genomic
DNA
was
extracted
using
a
modified
phenol-‐chloroform
extraction
with
ethanol
precipitation.
In
brief,
this
included
three
rounds
of
physical
(freeze,
thaw,
vortex)
and
chemical
(lysozyme)
disruption
of
the
cell
wall
prior
to
phenol-‐chloroform
extraction
according
to
Momper
et
al.,
2015.
Controls
during
DNA
extraction
and
polymerase
chain
39
reaction
(PCR)
confirmed
there
were
no
contaminating
nucleic
acids
during
the
extraction
process
(data
not
shown).
Rock
cores
from
freshly
drilled
boreholes
were
collected
during
an
expedition
of
opportunity
in
April
2014
(Figure
2.2).
Rocks
with
visual
fractures
were
targeted
as
having
the
highest
chance
for
having
microbial
biomass.
The
outer
layer
of
the
rock
cores
was
sterilized
by
flaming
with
molecular
biology
grade
100%
ethanol.
Approximately
100
g
of
rock
was
crushed
within
a
sterilized
drum
using
a
ShatterBox
8530
(SPEX
SamplePrep;
Stanmore,
UK)
available
at
the
NASA
Jet
Propulsion
Lab
in
a
clean
lab.
DNA
was
extracted
from
rock
cores
according
to
a
modified
method
previously
described
(Reese,
et
al.,
2013)
in
which
sample
volume
for
each
extraction
was
increased
to
10
g
and
multiple
extractions
were
sequentially
combined.
The
fluid
used
during
the
drilling
process
was
filtered
and
preserved
for
extraction
alongside
the
rock
cores.
No-‐template
controls
were
carried
through
the
extraction
process
as
well.
Whole
genomic
DNA
from
the
borehole
fluid,
rock
cores,
drilling
fluid,
and
extraction
controls
was
sent
to
Molecular
Research
DNA
(Shallowater,
TX,
USA).
Primers
515
forward
and
806
reverse
(Caporaso
et
al.,
2011)
were
used
to
amplify
the
hypervariable
4
region
of
the
16S
rDNA
gene
according
to
previously
described
methods
(Dowd
et
al.,
2008)
.
Samples
were
sequenced
on
Roche
454
FLX
titanium
instruments
using
recommended
reagents
and
following
manufacturer’s
guidelines.
40
2.3.4
Processing
of
pyrosequencing
data
Sequence
data
were
analyzed
using
the
software
programs
QIIME
(Caporaso
et
al.,
2010)
and
Mothur
(Schloss
et
al.,
2009).
Sequences
were
quality
filtered
by
removing
any
sequence
that
had
less
than
200
or
greater
than
350
nucleotide
bases,
had
more
than
one
ambiguous
base
(Ns),
did
not
have
an
exact
match
to
the
proximal
primer
or
had
a
quality
score
less
than
25.
Chimeras
were
removed
using
ChimeraSlayer
(Haas
et
al.,
2011).
Sequences
were
clustered
into
operational
taxonomic
units
(OTUs)
using
the
USEARCH
algorithm
(Edgar,
2010)
at
a
97%
identity
cutoff.
Representative
sequences
were
compared
against
the
SILVA
119
database
(www.arb-‐silva.de).
Taxonomy
was
assigned
to
the
lowest
possible
level
as
the
taxonomy
corresponding
to
the
reference
sequence
defining
that
OTU.
Principal
Coordinates
Analysis
(PCoA)
was
applied
to
summarize
UniFrac
distance
matrices
and
generate
biplots
including
taxonomic
assignments
(Vázquez-‐Baeza
et
al.,
2013).
2.3.5
Data
deposit
Sequences
collected
from
SURF
and
analyzed
in
this
study
were
deposited
in
the
GenBank
database.
All
sequences
are
available
under
BioProject
PRJNA262938.
Core
sequences
can
be
found
under
accession
numbers
SRR3061421
and
SRX149322.
Fluid
sequences
can
be
found
under
accession
numbers
SRR3234039
and
SRX734711.
3.3.6
Selection
of
other
global
sites
Eighteen
community
sequencing
datasets
were
collected
from
the
Visualization
and
Analysis
of
Microbial
Population
Structures
database
(http://vampsarchive.mbl.edu/).
41
Global
marine
and
terrestrial
locations
were
included.
Datasets
were
selected
based
on
the
following
criteria:
1)
overlapping
hyper
variable
region
within
the
16S
gene
sequence,
which
enabled
alignment
of
all
sequences
for
downstream
clustering
and
analysis.
2)
When
multiple
depths
were
sampled
at
the
same
geographic
location,
the
deepest
sample
was
chosen.
Publicly
available
data
are
part
of
a
community-‐sequencing
project
sponsored
by
the
Deep
Carbon
Observatory
(DCO).
These
sequences
were
downloaded
and
processes
using
the
same
methods
described
above
at
the
same
time
that
we
analyzed
sequences
from
SURF.
Geographic
locations
of
the
global
sites
included
in
this
study
are
in
Figure
2.4.
DCO
and
VAMPS
sequence
identifiers,
host
rock
and
sample
type
(terrestrial,
marine)
are
listed
in
Table
2.2.
2.4
Results
2.4.1
Geochemical
measurements
at
SURF
Geochemical
and
physical
data
for
SURF
borehole
fluids
are
given
in
Table
1.
For
comparison,
these
results
include
geochemical
data
from
borehole
fluids
300
feet
deep
(HMC10-‐1
and
-‐19223),
1700
feet
deep
(HMC-‐27-‐1)
and
4850
feet
deep
(HMC-‐13398,
FL-‐
SURF-‐B
and
FL-‐SURF-‐D).
I
have
also
included
mine
service
water,
typically
used
as
lubricant
during
borehole
drilling
operations.
Concentrations
of
dissolved
ions
and
gases
were
higher
in
FL-‐SURF-‐B
and
-‐D
fluids
than
in
fluids
sampled
on
shallower
levels
(see
Osburn
et
al.,
2014
and
Chapter
1,
this
document).
Dissolved
sulfate
and
methane
levels
were
highest
in
FL-‐SURF-‐B,
reaching
approximately
46
mM
and
450
nM,
respectively.
Bicarbonate
(HCO3
-‐
)
was
relatively
high
in
both
SURF
fluids
but
were
especially
high
in
FL-‐
SURF-‐D
(12.45
vs.
2.63
mM).
Total
manganese
was
much
higher
in
FL-‐SURF-‐B
than
–D.
This
42
was
also
noted
at
the
time
of
sample
filtration:
manganese
oxide
accumulation
on
the
filtration
devices
where
filtrate
exited
the
oxygen-‐impermeable
Masterflex
tubing.
43
Table 2.1. Physical and geochemical parameters for borehole fluid and industrial water.
Depth Mn
SO
4
2-
S
2-
NO
3
-
H
2
O
2
N
2
CO
2
CH
4
C
2
H
6
CO
(FBS) (µM) (mM) (ug/L) (µM) (nM) (nM) (µM) (nM) (nM) (nM) (nM)
Industrial
water
surface 15.5 24 6.09 NM BDL BDL NM 0.133 130 70.1 0 1899 56.6 728.29 1.9 0 1.39
HMC-10-1 300 10 0 4.83 NM 44.8 3.62 4.4 3.266 7 22.6 0 898 27 655.23 0.4 0 0.29
HMC-19223 300 12.4 -127 6.16 36.8 5.5 1.8 1.7 1.003 32 35.5 0 1408 26.4 517.82 0 0 0.33
HMC-27-1 1700 16.3 -14 9.98 NM 0.01 3.6 NM 1.118 120 BDL 0 1705 65.8 517.06 4.4 0 0.79
HMC-13398 4850 32.8 -264 12.4 12 0.01 BDL 26.7 1.873 382 40.3 1.23 1996 40.8 975.92 37.9 1.4 2.37
FL-SURF-D 4850 17.6 -235 12.45 9.4 40.3 1.8 0.56 10.94 130 10.3 0.1 32 0.79 187.29 4.9 0.08 0.07
FL-SURF-B 4850 23 -276 2.63 29.45 54.1 10.9 5.9 45.7 83 23.7 0.54 18 0.73 64.48 436.2 2.45 0
NM= not measured
BDL= below detection limit
NH
3
(µM) Site T (°C)
ORP
(mV)
HCO
3
-
(mM)
TDN (µM
[N])
Fe
2+
(µM)
44
2.4.2
SURF
fluid
and
rock
sequences
Sequences
from
borehole
fluid
and
rock
core
segments
were
trimmed
and
quality
filtered
(to
an
average
length
of
254
bases.
A
total
of
19,130
and
30,654
partial
16S
rRNA
gene
sequences
were
recovered
from
FL-‐SURF-‐B
and
-‐D,
respectively.
Core
sections
RX-‐
SURF-‐418
and
-‐613
yielded
2,373
and
10,952
sequences,
respectively.
To
ensure
the
biomass
extracted
from
fluids
and
rock
represents
the
in
situ
communities,
we
sequenced
industrial
water
used
in
the
mine
and
the
drilling
water
used
for
rock
retrieval.
Operational
taxonomic
units
(OTUs)
found
in
industrial
and
drill
water
were
removed
from
the
fluid
and
rock
sample
sequences
using
SourceTracker
(Knight
et
al.,
2011).
Further
analyses
were
only
carried
out
on
these
modified
sequences
from
fluid
and
rock
samples.
Sequences
from
FL-‐SURF-‐B
and
-‐D
showed
no
contamination
from
the
shallower
fluids
or
from
industrial
drilling
water
(data
not
shown).
As
seen
in
Figure
2.1,
our
analyses
suggest
that
these
deeper
fluids
are
likely
recharged
by
the
deepest
storage
recapture
area
(Murdoch
et
al.,
2012).
We
identified
2,064
OTUs
from
FL-‐SURF-‐B
and
3,072
OTUs
from
FL-‐
SURF-‐D
(Figure
2.3a).
In
contrast,
much
lower
diversity
was
found
in
the
rock
core
communities,
with
61
and
248
OTUs
for
RX-‐SURF-‐418
and
-‐613,
respectively.
A
Venn
diagram
of
all
four
SURF
samples
(Figure
2.3a)
revealed
that
740
OTUs
(16.8%)
were
shared
between
the
two
fluids,
and
16
OTUs
(5%)
were
shared
between
the
two
rock
core
samples.
Interestingly,
although
the
fluid
and
core
samples
are
from
similar
host
rock
and
from
the
same
depth
(1.4
km/4850
feet)
(Figure
2.1),
only
10
OTUs
were
shared
between
core
sample
-‐613
and
one
or
both
of
the
fluid
samples;
furthermore,
only
2
OTUs
were
45
shared
between
core
sample
-‐418
and
either
fluid
sample
(Figure
2.3).
This
separation
can
be
interrogated
further
at
the
taxonomic
level
(Figure
2.3b)
and
in
the
following
discussion.
Specifically
we
found
a
dichotomy
between
fluid
and
rock
communities:
putative
sulfate
reducers
dominated
the
fluid
samples
while
putative
hydrogen
oxidizers
dominated
the
rock-‐associated
sequences.
Deltaproteobacteria
are
relatively
abundant
members
of
the
fluid
communities
(~17%
of
total)
but
are
absent
in
the
rock
core
sequences.
Family-‐
and
genus-‐level
interrogation
revealed
multiple
lineages
of
putative
SRBs
in
the
fluids.
SRBs
were
not
identified
in
the
rock
cores
where
sulfate-‐rich
fluids
would
be
effectively
absent
or
inaccessible.
Conversely,
the
Alpha,
Beta
and
Gamma
subclasses
of
the
Proteobacteria
comprise
25,
15
and
31%
of
rock-‐associated
sequences,
respectively.
Within
the
Beta-‐
and
Gammaproteobacteria,
the
genera
Hydrogenophaga
and
Pseudomonas,
respectively,
are
most
dominant.
Within
the
core,
these
two
lineages
include
exclusively
facultative
autotrophs
that
can
use
H2
as
an
electron
donor
(Willems
et
al.,
1989).
46
Figure 2.3. Comparisons between fluid and rock associated microbial communities. A) Venn
diagram showing number of operational taxonomic units (OTUs) in each sample, correlated
to circle size, and the number of overlapping OTUs between samples. B) Taxonomic
breakdown to the class and order levels for rock and fluid sequences. Taxonomic groups that
overlap between rock and fluid are denoted with a star.
Unassigned
Firmicutes_Clostridia_Clostridiales
Firmicutes_Clostridia_Thermoanaerobacterales
Firmicutes_Bacilli_Lactobacillales
Firmicutes_Bacilli_Bacillales
Proteobacteria_Alphaproteobacteria_Rhizobiales
Proteobacteria_Alphaproteobacteria_Rhodobacterales
Proteobacteria_Betaproteobacteria_Gallionellales
Proteobacteria_Betaproteobacteria_Burkholderiales
Proteobacteria_Betaproteobacteria_Rhodocyclales
Proteobacteria_Deltaproteobacteria_Desulfarculales
Proteobacteria_Deltaproteobacteria_Desulfobacterales
Proteobacteria_Deltaproteobacteria
Proteobacteria_Deltaproteobacteria_Syntrophobacterales
Proteobacteria_Deltaproteobacteria_Desulvovibrionales
Proteobacteria_Gammaproteobacteria_Xanthomonadales
Proteobacteria_Gammaproteobacteria_Pseudomonadales
Proteobacteria_Gammaproteobacteria_Pasteurellales
Chloroflexi_Anaerolineae_Anaerolineales
Chloroflexi_TK17
Chloroflexi_S085
Chlorobi_Ignavibacteria_Ignavibacteriales
Nitrospirae_Nitrospira_Nitrospirales
Actinobacteria_OPB41
BRC1
OP3 (Omnitrophica)
OD1_ABY1 (Parcubacteria)
GN04
Cyanobacteria
*
*
*
*
*
*
738
2325
1321
2
3
2
5
238
16
43
FL-SURF-B
FL-SURF-D
RX-SURF-613
RX-SURF-418
738
2325
1321
2
3
2
5
238
16
43
FL-SURF-B
FL-SURF-D
RX-SURF-613
RX-SURF-418
A B#
#RX#######FL#
47
The
presence
of
the
phylum
Cyanobacteria
in
both
borehole
fluids
and
core
rock
samples
was
unexpected,
and
led
to
the
assumption
that
subsurface
fluids
at
SURF
were
connected
to
surface
streams
or
shallow
aquifers.
However,
when
classified
at
the
family
level,
the
cyanobacterial
sequences
did
not
correspond
to
freshwater
lineages.
In
fact,
they
are
most
closely
related
to
a
newly
recognized
aphotic
sister
phylum
to
(or
class
within)
the
Cyanobacteria
(Di
Rienzi
et
al.,
2013;
Soo
et
al.,
2014).
These
aphotic
cyanobacteria
produce
energy
through
fermentation,
releasing
hydrogen,
which
in
turn,
may
serve
as
an
energy-‐rich
electron
donor
for
abundant
Hydrogenophaga
and
other
organisms
in
the
subsurface.
2.4.3
Trends
in
terrestrial
subsurface
communities
Among
all
terrestrial
sites
analyzed
here,
a
unifying
characteristic
was
the
dominance
of
the
phylum
Firmicutes.
Firmicutes
have
been
observed
previously
as
the
dominant
phylum
in
pristine
terrestrial
deep
subsurface
fluids
(Brazelton
et
al.,
2013;
Chivian
et
al.,
2008;
Magnabosco
et
al.,
2015).
Between
marine
and
terrestrial
datasets,
a
striking
biogeographic
partition
was
discovered:
while
phylum
Firmicutes
was
most
abundant
in
terrestrial
ecosystems
the
phyla
Actinobacteria
and
Chloroflexi
were
abundant
in
marine
systems
but
rare
or
not
detected
in
terrestrial.
2.4.4
Comparison
of
SURF
and
global
subsurface
communities
The
community
structure,
based
on
partial
16S
rRNA
gene
sequences,
in
SURF
fluids
and
rock
cores
were
compared
with
those
of
publically-‐available
datasets
from
13
globally
distributed
subsurface
sites
(see
Figure
2.4).
Specifically,
taxonomic
assignments
eographic
measures
were
considered.
For
the
purposes
of
this
global
biogeographic
48
analysis,
SURF
borehole
fluid
sequences
were
concatenated
and
analyzed
as
a
single
location.
Similarly,
sequences
from
the
two
SURF
rock
samples
were
concatenated
and
analyzed
as
a
single
location,
bringing
the
total
of
global
subsurface
datasets
in
this
comparison
to
15—9
marine
and
6
terrestrial.
Partial
metadata,
including
environment
type,
host
rock,
and
location;
latitude
and
longitude;
and
depth,
temperature,
and
pH
for
these
13
sites
and
the
SURF
samples
are
given
in
Table
2.
All
sites
had
circumneutral
pH
(7.1-‐8.3)
at
time
of
measurement
with
the
exception
of
10.LAV
(pH
9.2)
and
an
ultrabasic
serpentinizing
system,
11.BRZ
(pH
12.2).
Temperatures
ranged
from
4-‐10
o
C
for
most
marine
sites
(two
were
warmer
at
19
and
64
o
C)
and
16-‐44
o
C
for
terrestrial
sites.
The
phylum-‐level
taxonomic
assignments
for
all
15
samples
can
be
found
in
Figure
2.6.
Phyla
are
listed
in
order
of
average
abundance
across
all
samples
(Column
1
in
Figure
2.6).
It
can
be
seen
that
the
Proteobacteria
were
abundant
and
ubiquitous
across
all
marine
and
terrestrial
environments
(~41%
and
~36%
of
the
total
in
marine
and
terrestrial
samples,
respectively).
Closer
inspection
shows
that
the
Alpha-‐,
Beta-‐,
Delta-‐,
49
Figure
2.4.
Map
of
sample
sites
analyzed
in
this
study.
Sites
are
numbered
according
to
order
listed
in
Table
2
and
Figure
6:
1.BKR,
2.BRG,
3.HIN,
4.LAZ,
5.ORC,
6.WAL,
7.RAM,
8.SYL,
9.PPA,
10.LAV,
11.BRZ,
12.SURF-‐FL,
13.SURF-‐RX,
14.MOS,
15.PED.
and
Gamma-‐subclasses
of
Proteobacteria
were
present
in
some
or
most
marine
and
terrestrial
subsurface
sites,
but
with
substantial
variability
from
site
to
site;
the
Epsilon-‐
and
Zetaproteobacteria
were
rare
or
absent
from
all
sites
investigated.
Among
the
Proteobacteria,
the
Alpha-‐subclass
was,
on
average,
the
most
prevalent
in
the
marine
ecosystems
(~12%),
and
the
Beta-‐subclass
was,
on
average,
the
most
prevalent
in
the
terrestrial
ecosystems
(~18%).
The
Deltaproteobacteria,
which
contain
many
lineages
of
2
3
4
5,6
7
9
8
1
10
14
15
11 12,13
50
SRBs
and
are
commonly
found
in
sulfate-‐rich
environments,
were
present,
on
average,
at
only
~7%
and
~4%,
respectively,
in
marine
or
terrestrial
systems.
Similar
to
the
Proteobacteria,
the
Bacteroidetes
were
variable
across
all
15
sites
and
showed
no
relative
dominance
in
marine
or
terrestrial
settings.
2.5
Discussion
2.5.1
Comparison
of
SURF
fluid
and
rock
indicative
of
longer
water/rock
interactions
at
greater
depths
within
SURF.
We
found
a
dichotomy
between
fluid
and
rock
communities:
putative
sulfate
reducers
dominated
the
fluid
samples
while
putative
hydrogen
oxidizers
dominated
the
rock-‐
associated
sequences.
This
minimal
overlap
within
this
subsurface
ecosystem
indicates
a
unique,
rock-‐associated
microbial
community
on
the
one
hand
and
a
fluid-‐associated
on
the
other.
Deltaproteobacteria
are
relatively
abundant
members
of
the
fluid
communities
(~17%
of
total)
but
are
absent
in
the
rock
core
sequences.
Family-‐
and
genus-‐level
interrogation
revealed
multiple
lineages
of
putative
SRBs
in
the
fluids,
presumably
taking
advantage
of
the
millimolar
levels
of
sulfate
there.
SRBs
were
not
identified
in
the
rock
cores
where
sulfate-‐rich
fluids
would
be
effectively
absent
or
inaccessible.
Conversely,
the
Alpha,
Beta
and
Gamma
subclasses
of
the
Proteobacteria
comprise
25,
15
and
31%
of
rock-‐
associated
sequences,
respectively.
Within
the
Beta-‐
and
Gammaproteobacteria,
the
genera
Hydrogenophaga
and
Pseudomonas,
respectively,
are
most
dominant.
Within
the
core,
these
two
lineages
include
exclusively
facultative
autotrophs
that
can
use
H2
as
an
electron
donor
(Willems
et
al.,
1989).
Interestingly,
Hydrogenophaga
has
been
identified
globally
in
subsurface
serpentinizing
systems
(Brazelton
et
al.,
2013;
Schrenk
et
al.,
2013),
and
Pseudomonas
is
a
putative
carbon
monoxide
oxidizer
(Kiessling
and
Meyer,
1982;
Krüger
51
and
Meyer,
1987).
Carbon
monoxide
is
a
common
byproduct
of
mining
procedures,
and
that
at
SURF
even
now,
~15
years
after
mine
closure,
carbon
monoxide
is
detectable
in
borehole
fluids
and
the
industrial
water
that
flows
through
the
mine
workings
(Table
1).
Pseudomonas
may
be
taking
advantage
of
a
metabolic
niche
provided
by
this
often
ignored
electron
donor
and
subsequent
carbon
source
(as
CO2
byproduct).
2.5.2
Comparison
of
SURF
to
other
terrestrial
sites
The
unifying
characteristic
among
all
terrestrial
sites
investigated
here
was
the
ubiquitous
dominance
of
the
phylum
Firmicutes,
ranging
from
22
to
51%
of
total
sequences,
with
an
average
of
~33%.
As
discussed
below,
only
one
marine
site
had
abundant
Firmicutes,
with
the
average
in
marine
systems
only
~5%.
Firmicutes
typically
lead
an
anaerobic
lifestyle
as
fermenters
or
sulfate
reducers.
Many
lineages
are
spore-‐forming
autotrophs
.
These
characteristics
are
ideal
for
adaptation
to
the
typically
anoxic
terrestrial
subsurface
ecosystems
that
are
often
low
in
reduced
carbon
compounds
(Lin
et
al.,
2006).
In
this
study,
across
terrestrial
sites
the
family
Peptococcaceae
within
the
Firmicutes
are
often
highly
abundant
(up
to
40%
in
14.MOS_DCO_Bv6v4,
a
terrestrial
site
~300
m
deep
in
Death
Valley,
California,
USA).
These
sequences
are
most
closely
related
to
candidatus
Desulforudis
audaxviator,
an
anaerobic,
chemoautotrophic,
spore-‐forming
sulfate
reducer
that
is
also
capable
of
nitrogen
fixation
(Chivian
et
al.,
2008).
This
lineage
was
first
identified
in
fracture
water
2.8
km
below
52
Table 2.2 Available metadata for the 16 datasets compared in this study. For sites other than SURF, data were gathered from the Visualization
and Analysis of Microbial Population Structures (V AMPS) database.
Sample ID
DCO sample
name Environment Lat/Long Geographic location Host rock Depth (m) pH Temp, C
1.BKR DCO_BKR_Bv4v5 marine 58.56°N 18.25°S Baltic Sea Basin silty clay 47.6 7.7 4
2.BRG DCO_BRG_Bv6v4 marine 11.28°N 93.1°E Andaman Sea siliceous sediment 684 7.1 19
3.HIN DCO_HIN_Bv6v4 marine 33.02°N 32.63°E Mediterranean Sea marine sediment 4.5 7.6 4
4.LAZ DCO_LAZ_Bv4v5 marine 9.5°N 78.57°W Dorado outcrop marine sediment 0.04 8.2 10
5.ORC DCO_ORC_Bv6v4 marine 47.29°N 128.03°W Juan de Fuca Ridge marine sediment 53.6 7.1 4
6.WAL DCO_WAL_Bv6v4 marine 58.7°N 178.56°W Bering Sea marine sediment NA 8.2 4
7.RAM DCO_RAM_Bv6v4 marine 72.2°N 14.43°W Barents Sea mud volcano NA NA NA
8.SYL DCO_SYL_Bv6v4 marine 28.59°S 173.38°W South Pacific Gyre basalt breccia 491 NA NA
9.PPA DCO_PPA_Bv6v4 marine 47.75°N 127.76°W West Pacific basalt 275-287 7.5 64
10.LA V DCO_LA V_Bv6v4 terrestrial 46.1°S 118.92°W Wallula Washington, USA basalt 1100 9.2 38-44
11.BRZ DCO_BRZ_Bv6v4 terrestrial 44.42°N 8.66°E Ligurian Springs, Italy ultramafic NA 12.2 16
12.FL-SURF SURF_fluid terrestrial 44.21°N 103.45°W South Dakota, USA amphibolite/rhyolite 1400 7.10 17-23
13.RX-SURF SURF_rock terrestrial 44.21°N 103.45°W South Dakota, USA amphibolite 1400 NA 38
14.MOS DCO_MOS_Bv6v4 terrestrial 36.41°N 116.52°W Death Valley, California, USA dolomitic 300 7.4 43
15.PED DCO_PED_Bv6v4 terrestrial 61.23°N 21.44°E Finland granitic 366 8.3 NA
DCO=Deep Carbon Observatory
NA= not available
53
surface
in
the
Kalahari
Shield
of
South
Africa
(Gihring
et
al.,
2006;
Chivian
et
al.,
2008),
and
has
since
been
found
globally
in
terrestrial
deep
biosphere
samples
((Gihring
et
al.,
2006;
Chivian
et
al.,
2008;
Jungbluth
et
al.,
2012;
Tiago
and
Veríssimo,
2012;
Aüllo
et
al.,
2013;
Osburn
et
al.,
2014);
and
this
study).
In
the
terrestrial
deep
biosphere
datasets
analyzed
here,
Firmicutes,
Deltaproteobacteria,
and
other
putative
sulfate
reducers
are
ubiquitous.
This
implicates
sulfate
reduction
as
a
potentially
dominant
metabolic
strategy
in
subsurface
continental
ecosystems,
and
highlights
the
need
for
further
investigation
in
these
dark
environments.
54
TOT MAR 12.FL+ 13.RX+ TERR TOT
AVE AVE 1.BKR 2.BRG 3.HIN 4.LAZ 5.ORC 6.WAL7.RAM 8.SYL 9.PPA 10.LAV 11.BRZ SURF SURF 14.MOS 15.PED AVE AVE MAR TERR
Proteobacteria 80%
Alphaproteobacteria
Betaproteobacteria 40%
Deltaproteobacteria
Epsilonproteobacteria 20%
Gammaproteobacteria
Zetaproteobacteria 10%
Firmicutes
Actinobacteria 5%
Chloroflexi
Unassigned;Other 0%
Bacteroidetes
CD12
Cyanobacteria
Planctomycetes
Nitrospirae
SAR406
OP1
OP9
Verrucomicrobia
Archaea_Crenarchaeota
Acidobacteria
OP8
Archaea_Euryarchaeota
OD1
Gemmatimonadetes
Tenericutes
BRC1
Spirochaetes
OP3
Chlorobi
Armatimonadetes
WS3
PAUC34f
GN04
BHI80+139
AC1
Elusimicrobia
GN02
TM7
SBR1093
NC10
WWE1
TM6
WPS+2
Archaea__[Parvarchaeota]
AncK6
Lentisphaerae
Fusobacteria
NKB19
SC4
WS1
LD1
WS4
WS5
Chlamydiae
Fibrobacteres
H+178
LCP+89
NPL+UPA2
SR1
TOT MAR 12.FL+ 13.RX+ TERR TOT
AVE AVE 1.BKR 2.BRG 3.HIN 4.LAZ 5.ORC 6.WAL7.RAM 8.SYL 9.PPA 10.LAV 11.BRZ SURF SURF 14.MOS 15.PED AVE AVE MAR TERR
Proteobacteria 80%
Alphaproteobacteria
Betaproteobacteria 40%
Deltaproteobacteria
Epsilonproteobacteria 20%
Gammaproteobacteria
Zetaproteobacteria 10%
Firmicutes
Actinobacteria 5%
Chloroflexi
Unassigned;Other 0%
Bacteroidetes
CD12
Cyanobacteria
Planctomycetes
Nitrospirae
SAR406
OP1
OP9
Verrucomicrobia
Archaea_Crenarchaeota
Acidobacteria
OP8
Archaea_Euryarchaeota
OD1
Gemmatimonadetes
Tenericutes
BRC1
Spirochaetes
OP3
Chlorobi
Armatimonadetes
WS3
PAUC34f
GN04
BHI80+139
AC1
Elusimicrobia
GN02
TM7
SBR1093
NC10
WWE1
TM6
WPS+2
Archaea__[Parvarchaeota]
AncK6
Lentisphaerae
Fusobacteria
NKB19
SC4
WS1
LD1
WS4
WS5
Chlamydiae
Fibrobacteres
H+178
LCP+89
NPL+UPA2
SR1
Figure
2.5.
Taxonomic
breakdown
of
all
samples
analyzed
in
this
study.
Phyla
are
listed
in
order
of
rank
abundance
and
only
phyla
>2%
abundant
in
at
least
one
sample
are
included.
Overall
average
of
relative
abundance
shown
in
column
on
the
far
left
(purple
scale).
Marine
and
terrestrial
samples
are
shown
in
blue
and
red,
respectively.
Marine
and
terrestrial
average
relative
abundances
are
shown
on
the
left
and
right,
respectively.
55
2.5.2
Comparison
of
terrestrial
and
marine
sites
Proteobacteria
are
common,
even
dominant,
in
most
marine
and
terrestrial
subsurface
ecosystems
investigated
(Figure
2.6).
Firmicutes,
however,
are
only
prominent
in
all
terrestrial
sites,
and
rare
in
most
marine
sites.
Actinobacteria
and
Chloroflexi,
on
the
other
hand,
are
common
in
a
majority
of
marine
sites
(Figure
2.6).
Actinobacteria
are
a
cosmopolitan
and
diverse
Gram-‐positive
phylum
often
associated
with
heterotrophic
organic
matter
degradation
and
secondary
metabolites
production,
including
production
of
antibacterials
and
antifungals
(Ward
and
Bora,
2006).
Actinobacteria
existence
in
environments
including
soils,
microbial
mats,
and
marine
sediments
has
been
noted
(Babalola
et
al.,
2009;
Ruvindy
et
al.,
2015;
Claverías
et
al.,
2015),
and
these
bacteria
can
play
crucial
roles
in
organic
turnover
(Větrovský
et
al.,
2014).
In
samples
presented
in
this
study,
many
marine
bacterial
communities
included
significant
portions
of
Actinobacteria,
(as
much
as
46.3%
in
the
Andaman
Sea
sediment)
with
seven
of
the
nine
marine
communities
containing
at
least
5%
Actinobacteria,
and
an
average
of
over
14%
for
the
sites
examined.
This
drastically
differs
from
the
terrestrial
sites,
where
no
terrestrial
biosphere
bacterial
communities
have
over
3%
Actinobacteria,
and
the
average
Actinobacteria
abundance
is
1%.
These
data
could
indicate
a
control
on
the
Actinobacteria
related
to
the
availability
of
organic
matter,
and
would
explain
the
relative
absence
in
the
organic
poor
terrestrial
deep
biosphere.
Like
the
Actinobacteria,
the
Chloroflexi
are
well
represented
in
the
marine
subsurface
sites
in
this
study,
and
with
the
exception
of
the
SURF
fluids,
are
not
abundant
56
in
the
terrestrial
subsurface.
This
is
in
accordance
with
other
studies,
which
have
noted
the
prevalence
of
the
Chloroflexi
in
the
marine
subsurface
(Blazejak
and
Schippers,
2010).
Chloroflexi
such
as
Dehaloccoides,
which
are
the
majority
of
the
Chloroflexi
in
marine
sediments
presented
here,
are
known
to
perform
reductive
dehalogenation
(Wagner
et
al.,
2009;
Adrian
et
al.,
2009).
Recent
genomic
evidence
suggests
that
the
non-‐phototrophic
Chloroflexi
are
able
to
maintain
heterotrophic
lifestyles
that
include
fermentation,
sugar
respiration,
and
fatty
acid
oxidation
(Hug
et
al.,
2013;
Wasmund
et
al.,
2013).
Most
of
the
Chloroflexi
sequences
found
at
SURF
are
related
to
the
Anaerolineae,
which
ferment
sugars
and
amino
acids
anaerobically.
Other
uncultured
Chloroflexi
grown
in
an
enriched
bioreactor
that
was
not
amended
with
organic
compounds
were
able
to
feed
off
dying
bacteria
(Kindaichi
et
al.,
2012).
Members
of
both
Dehalococcoides
and
the
Anaerolineae
have
been
found
to
contain
carbon
fixation
genes,
suggesting
possible
roles
for
both
heterotrophy
and
autotrophy
for
Chloroflexi
in
both
the
marine
and
terrestrial
subsurfaces,
but
additional
studies
are
needed
to
elucidate
the
relative
dearth
of
Chloroflexi
in
the
rock-‐
hosted
terrestrial
biosphere.
2.5.3
Concluding
Remarks
In
ocean
drilling
investigations,
it
has
recently
become
more
common
to
extract
genomic
DNA
from
deep
biosphere
core
samples
and
corresponding
pore
fluid
(e.g.-‐
Jungbluth
et
al.,
2013;
Lee
et
al.,
2015).
However,
in
the
terrestrial
realm,
this
is
the
first
high-‐throughput
16S
rDNA
sequencing
study
of
deep
subsurface
fluid
and
corresponding
host
rock.
In
fact,
we
noted
during
data
collection
that
the
terrestrial
subsurface
is
generally
less
well
represented
in
public
sequence
databases
than
in
its
marine
counterpart.
Furthermore,
approximately
25%
of
the
publicly
available
16S
rDNA
datasets
57
had
to
be
excluded
from
our
analysis
due
to
lack
of
even
minimal
metadata,
a
shortcoming
that
must
be
addressed
to
permit
further
holistic
global
studies.
In
terrestrial
environments,
the
major
unifying
characteristic
was
the
abundance
of
the
phylum
Firmicutes.
In
addition,
the
Betaproteobacteria
were
the
second
most
common
group
in
the
terrestrial
subsurface,
and
when
abundant,
generally
dominated
by
the
genus
Hydrogenophaga.
To
date,
H2
is
the
only
known
electron
donor
used
in
energy
metabolism
by
this
genus
(Kampfer
et
al.,
2005;
Schwartz
et
al.,
2009;
Brazelton
et
al.,
2013).
When
interrogated
at
the
family
and
genus
levels,
the
most
abundant
Firmicutes
were
Peptococcaceae,
which
are
typically
sulfate
reducers
or
fermenters
that
utilize
and
produce
H2,
respectively.
Previous
studies
have
speculated
on
the
importance
of
hydrogen,
perhaps
even
the
existence
of
communities
that
are
entirely
powered
by
reducing
equivalents
provided
by
hydrogen
produced
in
situ,
in
deep
subsurface
environments
that
are
cut
off
from
photosynthetically-‐derived
electron
donors
(Lin
et
al.,
2005;
Nealson
et
al.,
2005;
Blair
et
al.,
2007;
Schrenk
et
al.,
2013).
However,
H2
concentrations
are
not
generally
tabulated
in
publicly
available
datasets.
In
the
SURF
fluids
collected
for
this
study,
hydrogen
levels
were
low
but
measureable
(0.1-‐0.6
nM),
and
previous
thermodynamic
modeling
of
SURF
fluids
showed
that
hydrogen
oxidation
reactions
are
exergonic,
especially
when
coupled
to
the
reduction
of
O2,
NO3
-‐
,
and
Mn
IV
(Osburn
et
al.,
2014).
The
dominance
of
hydrogen
transformers
in
the
terrestrial
subsurface
sites
reviewed
in
the
present
study
further
supports
the
view
that
H2
is
a
key
energy
source
in
the
deep
biosphere.
58
Our
analysis
also
confirms
that
the
phylum
Actinobacteria
is
a
major
component
of
subseafloor
sediment
microbial
communities
(Orsi
et
al,
2013;
Claverías
et
al.,
2015;
Inagaki
et
al.,
2015;
Ruvindy
et
al.,
2015).
Sedimentary
organic
matter
can
survive
early
diagenesis
and
remain
available
for
fermentation
and
other
heterotrophic
metabolisms
by
deep
subsurface
microorganisms,
including
the
Actinobacteria
and
Chloroflexi
(Harnett
et
al.,
1998;
Dunne
et
al.,
2007;
Wallman
et
al.,
2012;
Orsi
et
al.,
2013;
Inagaki
et
al.,
2015).
The
relatively
minor
proportions
of
Deltaproteobacteria
and
other
putative
SRBs
in
the
marine
subsurface
support
recent
suggestions
that
sulfate
reduction
may
not
be
quite
as
dominant
in
marine
sediments
as
previously
thought
(Orsi
et
al.,
2013;
Bowles
et
al.,
2014).
Recent
estimates
place
the
total
mass
of
living
cells
in
marine
sediments
at
1.5–22
petagrams
carbon
(Pg
C)
(Hinrichs
and
Inagaki,
2012;
Kallmeyer
et
al.,
2012).
Similarly,
a
recent
review
evaluated
the
mass
of
the
terrestrial
subsurface
biosphere
at
14–135
Pg
C
(McMahon
and
Parnell,
2013),
approximately
10×
that
in
marine
sediments.
The
vastness,
variability,
and
carbon
content
of
the
terrestrial
subsurface
underscores
the
need
for
more
studies
and
highlights
the
relative
dearth
of
publicly
available
data
from
terrestrial
subsurface
sites.
Acknowledgements
This
work
was
supported
by
the
NASA
Astrobiology
Institute
under
cooperative
agreement
NNA13AA92A.
Many
people
have
contributed
to
the
success
of
this
project.
Huge
thanks
are
to
Karen
Momper
for
her
graphics
expertise
and
to
Sean
Jungbluth
for
technical
assistance.
We
would
also
like
to
recognize
the
science
and
support
staff
at
SURF,
including
Jaret
Heiss,
Tom
Reagan,
and
Kathy
Hart
for
making
mine
access
and
sample
collection
possible.
We
also
acknowledge
other
members
of
the
NAI
team
including
Ken
59
Nealson,
Rohit
Bhartia,
Moh
El-‐Naggar,
and
Yamini
Jangir
for
both
physical
and
intellectual
contributions
to
sample
collection
and
data
acquisition.
Conflict
of
interest
The
authors
have
no
conflicts
of
interest
to
report.
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65
CHAPTER
3
Metagenome
analysis
of
fluids
1.5
km
below
surface
reveals
new
energy
and
carbon
metabolisms
in
microbial
dark
matter
By
Lily
Momper
Coauthors:
Sean
Jungbluth,
Michael
Lee
and
Jan
Amend
In
preparation
for
The
International
Society
for
Microbial
Ecology
Journal
66
What
darkness
to
you
is
light
to
me
―
Jules
Verne,
Journey
to
the
Center
of
the
Earth
67
3.1
Abstract
The
terrestrial
deep
subsurface
is
a
huge
repository
of
microbial
biomass
and
organic
carbon.
However,
biological
samples
from
there
are
relatively
sparse,
especially
in
relation
to
its
size
and
physical
heterogeneity.
Here,
we
applied
a
culture-‐independent
metagenomic
approach
to
characterize
the
microbial
community
composition
in
deep
(1,500
meters
below
surface)
terrestrial
fluids.
Samples
were
collected
from
a
former
gold
mine
in
Lead,
South
Dakota,
USA
(44°21′3″N
103°45′57″W),
now
the
Sanford
Underground
Research
Facility
(SURF).
We
reconstructed
over
90
genomic
bins
from
metagenomic
sequences,
enabling
the
identification
of
common
metabolic
pathways
in
terrestrial
subsurface
microbes.
Sulfate-‐
and
nitrate/nitrite-‐
reduction
were
the
most
commonly
observed
energy
metabolisms.
More
than
a
quarter
(26
of
90)
of
our
genomes
belong
to
bacterial
phyla
without
any
cultivated
members;
two
of
them
constitute
the
most
complete
genomes
of
the
candidate
phyla
Omnitrophica
(formerly
OP3)
and
Hydrogenedentes
(formerly
NKB19).
In
both
of
these
genomes
we
found
the
complete
reductive
acetyl-‐CoA
carbon
fixation
pathway
and
several
genes
indicative
of
dissimilatory
nitrate
reduction.
This
is
the
first
report
of
these
metabolic
capabilities
in
the
cosmopolitan
subsurface
phylum
Hydrogenedentes,
the
first
to
inform
on
their
role
(and
that
of
Omnitrophica)
in
the
biogeochemical
cycles
of
carbon
and
nitrogen,
and
one
of
only
a
handful
of
studies
investigating
the
microbial
ecology
in
the
deep
terrestrial
subsurface
using
next
generation
(Illumina)
sequencing
technology.
68
Keywords:
Subsurface
biosphere/geomicrobiology/carbon
fixation
3.2
Introduction
Most
of
the
Earth’s
deep
subsurface
is
an
energy-‐starved
biome
that
is
functionally
defined
by
the
presence
of
microbial
life
and
the
lack
of
light
or
light-‐derived
biomass.
The
deep
subsurface,
in
particular
the
terrestrial
subsurface,
has
only
recently
been
appreciated
as
a
dynamic,
populated,
metabolically-‐active
biome,
interacting
with,
perhaps
controlling,
global
elemental
cycles.
The
deep
subsurface
biosphere
now
appears
to
harbor
an
astonishing
abundance
and
diversity
of
microorganisms
(e.g.,
Baker
et
al.,
2016;
Castelle
et
al.,
2015;
Chivian
et
al.,
2008;
Magnabosco
et
al.,
2015;
Rinke
et
al.,
2013).
Recent
estimates
put
the
total
deep
subsurface
biomass
(terrestrial
and
marine)
at
16-‐157
Pg
C
(Kallmeyer
et
al.,
2013;
McMahon
and
Parnell,
2013;
Teske
et
al.,
2005),
but
the
metabolisms
employed
therein,
together
with
the
corresponding
bioenergetics,
remain
almost
completely
unmapped.
Of
particular
interest
in
Earth’s
terrestrial
deep
subsurface
biosphere
(DSB)
are
the
carbon
sources
and
carbon
cycling
processes.
Due
in
large
part
to
limited
global
samples,
these
remain
poorly
constrained
(Onstott
et
al.,
1998;
Pfiffner
et
al.,
2006;
Simkus
et
al.,
2016).
The
terrestrial
subsurface,
in
particular,
appears
to
contain
a
vast
reservoir
of
carbon
(14-‐135
Pg)
that
we
are
only
beginning
to
appreciate.
Indeed,
a
call
in
late
2015
for
a
‘global
microbiome’
effort,
including
in
subsurface
environments,
sought
to
shed
light
on
microbial
processes
in
these
habitats
(Alivisatos
et
al.,
2015;
Dubilier
et
al.,
2015).
Metagenomic
and
single
cell
genomic
sequencing
(SCGS)
studies
in
shallow
(≤100
m)
69
terrestrial
systems
provided
insight
into
metabolic
capabilities
of
microbial
dark
matter
(Rinke
et
al.,
2013)
and
genomic
expansion
of
the
domain
Archaea
(Baker
et
al.,
2016;
Castelle
et
al.,
2015;
Seitz
et
al.,
2016;
Tyson
et
al.,
2004;
Youssef
et
al.,
2015a).
However,
metagenomic
analyses
in
samples
from
the
deeper
terrestrial
biosphere
remain
rare
(see
Chivian
et
al.,
2008;
Dong
et
al.,
2014;
Edwards
et
al.,
2006;
Magnabosco
et
al.,
2015).
Meta-‐omic
(genomic
and
transcriptominc)
techniques
have
been
widely
applied
to
marine
subsurface
samples
over
the
past
5
years
(e.g.,
Ananthraman
et
al.,
2015;
Baker
et
al.,
2012;
Biddle
et
al.,
2011;
Fortunato
and
Huber,
2016;
Lesniewski
et
al.,
2012),
but
the
terrestrial
deep
subsurface
has
not
received
the
same
attention.
Following
a
microbial
analysis
of
a
deep
(1.8
km
below
surface,
kmbs)
Cambrian
sandstone
reservoir
in
the
Illinois
Basin,
USA
(ref),
this
is
only
the
second
study
to
use
next
generation
(e.g.
Illumina)
metagenomic
techniques
on
terrestrial
deep
(defined
as
>1
kmbs)
subsurface
fluids.
Unlike
the
present
study,
which
identified
a
highly
diverse
microbial
community,
the
earlier
Illinois
study
found
an
ecosystem
dominated
by
a
single
genus,
Halomonas
(Dong
et
al.,
2014).
We
recently
analyzed
the
microbial
community
diversity
in
deep
subsurface
fluids
at
the
Sanford
Underground
Research
Facility
(SURF)
in
Lead,
South
Dakota,
USA,
using
high-‐throughput
tag
sequencing
of
the
16S
rRNA
gene
(Osburn
et
al.,
2014;
Momper
et
al.,
2016).
These
analyses
revealed
that
borehole
fluids
1.5
kmbs
contained
microbial
assemblages
distinct
from
those
observed
at
shallower
depths
at
SURF.
The
purpose
of
this
70
study
was
to
carry
out
a
detailed
metagenomic
analysis
on
1.5
km-‐deep
fluids
and
assess
the
corresponding
carbon
and
energy
metabolisms.
3.3
Materials
and
methods
3.3.1
Field
sampling
All
fluid
samples
and
corresponding
geochemical
data
were
collected
in
the
former
Homestake
gold
mine
(now
Sanford
Underground
Research
Facility,
SURF)
near
Lead,
South
Dakota,
USA
(44°21’
N
103°45’
W)
in
October
of
2013.
In
this
study,
we
examine
two
separate
samples
from
SURF:
both
are
ultra
deep
fracture
fluids
from
legacy
boreholes
drilled
1.4
kilometers
below
surface
(kmbs).
These
boreholes
were
drilled
600
and
900
horizontal
feet,
respectively,
into
host
rock.
The
SURF-‐designated
names
given
to
these
boreholes
in
2001
at
the
time
of
drilling
are
DUSEL-‐B
and
DUSEL-‐D.
These
are
the
official
names
in
SURF
archives.
However,
for
the
sake
of
simplicity
we
will
hereafter
refer
to
the
borehole
fluid
samples
as
SURF-‐B
(DUSEL-‐B)
and
SURF-‐D
(DUSEL-‐D).
A
comprehensive
description
of
sampling
methods
for
geochemistry
can
be
found
in
Osburn
et
al.,
2014
and
Momper
et
al.,
2016.
Details
of
samples
and
sample
location
are
provided
in
Table
3.1.
3.3.2
DNA
extraction
and
sequencing
Total
microbial
cells
were
collected
from
borehole
fluids
on
47
mm,
0.2µm
Supor
filters
(Pall
Corporation,
Port
Washington,
NY,
USA).
Filters
were
stored
on
dry
ice,
transported
to
University
of
Southern
California
and
immediately
frozen
at
-‐80
o
C.
Whole
genomic
DNA
was
extracted
using
a
modified
phenol-‐chloroform
extraction
with
ethanol
precipitation
as
previously
described
in
Osburn
et
al.,
2014.
DNA
concentration
was
71
checked
on
a
Qubit
2.0
fluorometer
(Thermo
Fisher
Scientific),
and
purity
was
measured
on
a
NanoDrop
2000
spectrophotometer
(Thermo
Fisher
Scientific)
before
samples
were
sent
for
sequencing.
Sequencing
was
performed
at
the
University
of
Southern
California’s
Genome
and
Cytometry
Core
Facility
(Los
Angeles,
USA)
on
an
Illumina
HiSeq
2500
(San
Diego,
California)
yielding
150
base
pair
(bp)
reads.
3.3.3
De
novo
assembly
and
read
mapping
Reads
were
assembled
using
IDBA-‐UD
1.1.1
(Peng
et
al.,
2012).
Sequences
from
each
of
the
two
borehole
fluids
were
assembled
individually.
Additionally,
sequences
from
both
fluids
were
co-‐assembled
in
order
to
implement
differential
coverage
binning
methods
for
genome
bin
analysis.
Minimum
contig
length
for
the
co-‐assembly
was
set
at
10,000
bp.
Coverage
information
was
then
attained
by
individually
mapping
the
paired-‐end
reads
of
each
of
the
two
samples
to
this
co-‐assembly
using
Bowtie2
(Langmead
and
Salzberg
2012).
To
convert
alignments
to
the
SAM
format
the
BWA-‐SAMPE
algorithm
was
used
with
default
parameters.
The
mapped
read
counts
and
coverage
information
were
extracted
using
SAMtools
0.1.17
(Li
et
al.,
2009).
3.3.4
Genome
binning
and
identification
Individual
genomes
were
binned
using
sequence
composition,
differential
coverage
and
read-‐pair
linkage
through
the
CONCOCT
program
(Alneberg
et
al.,
2013;
Alneberg
et
al.,
2014).
Genome
bins
were
manually
refined
and
curated
using
the
interactive
interface
in
the
Anvi’o
program
(Eren
et
al.,
2015).
Anvi’o
allows
for
interactive,
human-‐guided
refinement
of
bins
based
on
individual
contig
analysis
and
real-‐time
estimates
of
percent
72
completeness/percent
contamination
calculated
according
to
4
independent,
single-‐copy
gene
Hidden
Markov
Model
profiles
(Eren
et
al.
2015).
After
refinement,
genome
bin
completeness
and
contamination
were
re-‐calculated
using
five
widely
accepted
marker
gene
suites
compiled
from
Alneberg
et
al.,
Campbell
et
al.,
2013,
Creevey
et
al.,
2011,
Dupont
et
al.,
2012
and
Wu
and
Scott,
2012.
Reconstruction
and
identification
of
16S
rRNA
gene
sequences
within
each
bin
was
completed
using
the
CheckM
pipeline
(Parks
et
al.,
2015).
This
process
allowed
the
assembly
and
identification
of
96
genomic-‐representative
bins
>20%
complete
(Figure
3.2
and
3.3).
3.3.5
Assignment
of
putative
taxonomies
Genomic
bins
were
assigned
putative
taxonomic
identities
according
to
their
placement
in
a
phylogenomic
tree
using
the
"tree"
command
in
CheckM
(Parks
et
al.,
2015).
CheckM
employs
pplacer
(Matsen
et
al.,
2010)
to
place
concatenated
amino
acid
alignments
into
a
provided
database
of
complete
genomes
from
IMG
(CheckM
database
version
1.0.4).
Phylogenetic
identities
of
genomic
bins
were
further
refined
according
to
information
from
16S
rRNA
genes,
as
described
below.
3.3.6
16S
rRNA
tree
construction
Small
subunit
ribosomal
RNA
genes
were
extracted
from
the
genomic
bins
using
the
ssu_finder
tool
integrated
within
CheckM
(Parks
et
al.,
2015).
Genomic
bin
SSU
rRNA
genes
and
their
closest
neighbors
identified
via
a
BLAST
(Basic
Local
Alignment
Search
Tool)
query
against
the
non-‐redundant
NCBI
database
were
pooled
and
aligned
using
the
online
SINA
tool
version
1.2.11
(Pruesse
et
al.,
2012)
of
the
SILVA
database
(Quast
et
al.,
2013).
For
comparison,
additional
SSU
rRNA
sequences
from
Rinke
et
al.,
(2013)
and
Castelle
et
73
al.,
(2015)
were
aligned
in
a
similar
fashion.
All
aligned
sequences
were
imported
into
the
ARB
software
package
(version
6.0.3;
Ludwig
et
al.,
2004)
and
additional
closest
relatives
to
the
genome
bin
SSU
rRNA
genes
were
identified
within
the
SSURef_NR99_123_SILVA_12_07_15
and
LTPs123_SSU
databases
(Pruesse
et
al.,
2007;
Yarza
et
al.,
2008).
Phylogenetic
analyses
were
performed
with
the
RAxML
maximum
likelihood
method
using
the
GTR
model
of
nucleotide
substitution
under
the
gamma-‐
and
invariable-‐
models
of
rate
heterogeneity
(Stamatakis
et
al.,
2006).
The
tree
with
the
highest
log
likelihood
score
was
selected
from
performing
10
iterations
of
the
RAxML
method.
Bootstrap
analysis
of
the
sequence
alignment
was
determined
by
RAxML
using
the
rapid
bootstrap
analysis
algorithm
(2000
boostraps)
implemented
within
ARB
(Stamatakis
et
al.,
2008).
3.3.7
Metabolic
pathway
analysis
Assembled
metagenomes
and
individual
genomes
were
submitted
for
gene
calling
and
annotations
through
the
DOE
Joint
Genome
Institute
(JGI)
Integrated
Microbial
Genomes
metagenomics
expert
review
(IMG-‐MER)
pipeline
(Markowitz
et
al.,
2008).
Genes
were
searched
within
the
IMG-‐MER
interface
and
assigned
to
genome
bins
based
on
scaffold
ID
(Figure
1).
Genome
bins
were
examined
for
carbon
fixing
capabilities
using
KEGG
orthology
maps
through
the
IMG/ER
portal.
We
individually
checked
functional
genes
that
were
found
in
candidate
phyla
bins
within
this
study
that
have
not
been
reported
previously
in
those
phyla.
We
did
this
by
extracting
the
coding
region
for
the
gene
of
interest
and
performing
a
BLAST
search
for
nearest
neighbors.
Alignments
were
74
examined
and
if
the
alignment
was
of
poor
quality
(e.g.
large
gaps)
the
gene
was
deemed
a
false
hit
and
was
not
included
in
our
results
and
discussion.
Possible
autotrophy
for
all
genome
bins
was
investigated.
To
do
this
we
examined
KEGG
(Kyoto
Encyclopedia
for
Genes
and
Genomes)
biochemical
maps
for
the
six
known
carbon
fixation
pathways
in
each
bin.
Only
genes
that
are
known
to
code
for
enzymes
unique
to
carbon
fixation
were
included
(for
example,
genes
involved
in
glycolysis
were
not
included
in
the
gene
suite
for
the
reductive
citric
acid
cycle).
A
complete
list
of
the
KEGG
identifiers
for
each
of
the
6
pathways
can
be
found
in
Table
3.2.
3.3.8
Average
nucleotide
and
average
amino
acid
identity
comparison
All
bins
from
this
study
that
were
assigned
to
bacterial
candidate
phyla
NKB-‐19
(Hydrogenedentes),
OP3
(Omnitrophica),
WS3
(Latescibacteria)
(Rinke
et
al.,
2013)
and
archaeal
candidate
phyla
Woesarchaeota
(Castelle
et
al.,
2015)
were
compared
against
all
existing
genomes
for
those
phyla.
Average
nucleotide
identity
(ANI)
and
average
amino
acid
identity
(AAI)
were
analyzed
using
the
publicly
available
tools
provided
through
the
Environmental
Microbial
Genomics
Laboratory
at
Georgia
Tech
(http://enve-‐
omics.ce.gatech.edu/tool).
First,
pairwise
ANI
was
compared
between
the
genome
bin(s)
in
this
study
belonging
to
a
certain
candidate
phylum
and
all
other
previously
sequenced
genomes
from
that
phylum.
Previously
sequenced
genomes
from
a
single
phylum
were
also
compared
pairwise
against
each
other.
The
following
ANI
and
AAI
cutoffs
were
determined
by
Rodriguez
and
Konstantinidis
(2014)
and
Goris
et
al.,
2007.
If
pairwise
ANI
was
>72%,
the
value
was
recorded
and
the
genome
bin
from
the
current
study
was
determined
to
fall
75
within
the
division
(candidate
phylum)
to
which
it
was
assigned
using
methods
described
above.
If
the
ANI
value
was
<70%,
the
bins
were
deemed
too
divergent
to
be
compared
based
on
the
ANI
measurement.
We
performed
two-‐way
AAI
for
those
genomes.
If
the
two-‐
way
AAI
yielded
>40%
similarity
the
bin
from
this
study
was
determined
to
fall
within
the
division
(candidate
phylum)
to
which
it
was
assigned
using
methods
described
above.
If
the
two-‐way
AAI
yielded
<40%
similarity
the
bin
from
this
study
was
deemed
a
new
candidate
phylum
division.
These
new
candidate
phyla
will
be
interrogated
further
in
the
following
Results
and
Discussion.
3.3.9
Phylogenomic
analyses
The
concatenated
ribosomal
protein
tree
was
generated
using
single
copy
marker
genes
that
have
been
shown
to
have
minimal
lateral
gene
transfer.
The
genes
were
annotated
using
anvi’o
(Eren
et
al.,
2015)
and
chosen
based
on
ubiquity
among
the
genome
bins.
Amino
acid
alignments
of
the
individual
genes
were
generated
using
MUSCLE
(Edgar,
2004a;
Edgar,
2004b)
and
manually
curated.
The
curated
alignments
were
then
concatenated
for
phylogenomic
analyses.
The
phylogeny
was
generated
in
Geneious
Pro
version
8.1.5
using
PhyML
maximum
likelihood
(Guindon
and
Gasquel,
2003).
Bootstrap
values
were
generated
from
1000
replicates
of
UPGMA
tree
building
method
and
Jukes-‐
Cantor
distance
modeling.
76
3.4
Results
3.4.1
Sequencing
and
assembly
Shotgun
sequencing
of
total
community
genomic
DNA
on
1
split
lane
of
Illumina
HiSeq2500
produced
147,742,812
and
137,946,268
150
base
pair
(bp)
paired-‐end
reads
with
average
insert-‐size
of
420
bp
for
SURF-‐B
and
–D
fluids,
respectively.
Before
quality
filtering,
the
mean
quality
score
of
the
reads
was
37.
After
quality
filtering,
94.68%
of
reads
had
a
quality
score
of
≥36
(Figure
3.1).
The
minimum
sequence
length
was
set
to
40
bp,
and
the
maximum
length
was
151
bp.
Both
samples
had
an
average
GC
content
of
50%.
De
novo
assemblies
of
quality-‐filtered
reads
generated
a
total
of
276,553
contigs
for
SURF-‐B,
442,676
contigs
for
SURF-‐D
and
637,833
contigs
for
the
co-‐assembly.
Maximum
contig
lengths
were
576,430
(SURF-‐B),
293,691
bp
(SURF-‐D)
and
576,430
bp
(co-‐assembly).
Prediction
of
open
reading
frames
(ORFs)
resulted
in
478,845
and
816,244
putative
genes
in
SURF-‐B
and
–D
fluids,
respectively,
and
1,187,179
in
the
co-‐assembly
(Table
3.3).
77
SURF -D -re ve rse SURF -D -forwa rd
SURF -B -re ve rse
SURF -B -forwa rd
Quality scor e acr oss all r eads
Quality scor e acr oss all r eads
Quality scor e acr oss all r eads Quality scor e acr oss all r eads
a
b
c
d
Figure 3.1 Quality score information at each base pair position for reads in a) SURF-
B-forward b) SURF-B-reverse c) SURF-D-forward and d) SURF-D-reverse
78
3.4.2.Genome
binning
and
functional
annotation
Of
the
96
individual
genome
bins
(>20%
completeness)
considered
in
this
study
and
plotted
in
Figure
3.2,
14
were
80-‐90%
complete
and
24
were
>90%
complete.
Also
shown
in
Figure
3.2
for
each
bin
are
bin
size,
number
of
contigs,
recruitment,
GC
content,
presence/absence
of
the
16S
rRNA
gene,
and
contamination
levels.
Completeness
and
contamination
determined
from
5
sets
of
single-‐copy
marker
genes
is
shown
in
Figure
3.3.
Lastly,
metabolic
pathways,
both
assimilatory
and
dissimilatory,
were
mapped
in
Figure
3.2
Table 3.1. Sample metadata and shotgun sequencing results
SURF-B SURF-D Coassembly
Longitude -103.765784 -103.765784 -103.765784
Latitude 44.350967 44.350967 44.350967
Depth (km) 1.4 1.4 1.4
Temperature 23 18 ---
Reads 147,742,812 137,946,268 285,689,080
Contigs 276,553 442,676 637,833
Max contig (bp) 576,430 293,691 576,430
ORFs 478,845 816,244 1,187,179
Temperature is recorded in degrees Celsius
ORF= open reading frame
bp= base pairs
79
to
individual
genome
bins.
Given
the
in
situ
geochemical
conditions
and
calculations
of
redox
reaction
energetics
(Osburn
et
al.,
2014),
particular
interest
was
paid
to
energy
metabolisms
involving
nitrogen,
sulfur
and
methane.
Among
the
96
reconstructed
genomes,
the
genes
encoding
for
nar
(all
enzyme
subunits)
and
periplasmic
nitrate
and
nitrite
reductases
are
abundant.
One
or
more
subunits
of
the
nar
gene,
which
is
involved
in
nitrate
reduction,
is
present
in
42
of
the
96
genome
bins
shown
in
Figure
3.2;
another
nitrate
reduction
gene,
implicated
in
assimilatory
nitrate
reduction
via
a
periplasmic
nitrate
reducing
protein,
(napA),
is
present
in
every
genome
bin.
Periplasmic
nitrate-‐
and
nitrite
reductase
were
present
in
7
and
80
genomes
(Figure
3.2).
All
subunits
of
the
nar
operon
(alpha,
beta,
gamma,
delta,
periplasmic)
were
found
in
genome
bins
25
and
23
of
the
candidate
phyla
NKB-‐
19/Hydrogenedentes
and
OP3/Omnitrophica,
respectively.
This
is
the
first
genomic
evidence
of
dissimilatory
nitrate
reduction
in
the
phylum
Hydrogenedentes
and
only
the
second
report
of
nitrate
respiration
in
the
phylum
Omnitrophica
(Speth
et
al,
2016).
Nitric
oxide
reductase
(norB)
was
present
in
23
of
the
genomes,
but
nitrous
oxide
reductase
(nosZ)
was
only
found
in
3
genomes.
The
gene
encoding
ammonia
monooxygenase
(amoA),
an
enzyme
involved
in
dissimilatory
ammonium
oxidation,
was
found
in
3
genome
bins.
The
key
gene
for
assimilatory
nitrogen
fixation
(nifH)
was
present
in
16
genome
bins
and
particularly
common
in
the
various
bins
for
Deltaproteobacteria.
80
Bin Size
Site B recruitment
Site D recruitment
GC Content
Contamination
Completeness
Num Contigs
SSU rRNA
No
Yes
0
100
200
300
4e+06
8e+06
Bin Size
(bp)
Num.
Contigs
Bin
Recruit. (%)
GC
Content (%)
Gene
Presence
Completeness/
Contamination (%)
amoA
norB
nifH
nirB
nosZ
narG-periplasmic
narA
narB
narD
narG
nar (all subunits)
dsrAB
cbb3
Ni-Fe-hydrogenases
napA
mcrA
nitrite reductase (periplasmic)
citrate lyase (all subunits)
isocitrate lyase
0
Bin_7_1 - Microgenomates
Bin_2 - Armatimonadetes - unknown Chthonomonas
Bin_48_2 - unknown Betaproteobacteria
Bin_44_6 - unknown Firmicutes
Bin_1_2 - Chlorobi - Ignavibacteria - IheB3-7
Bin_68 - Thaumarchaeota - Candidatus Nitrososphaera
Bin_67_1 - Deltaproteobacteria - Nitrospinaceae
Bin_3_2 - unknown Gemmatimonadetes
Bin_3_1−TA06?
Bin_0 - Firmicutes - Pelotomaculum
Bin_60_1 - Deltaproteobacteria - unknown Desulfurivibrio
Bin_28 - Bacteria - Microgenomates?
Bin_13_1 - Bacteria - Parcubacteria?
Bin_49_1 - Chlofoflexi - Anaerolinea
Bin_21_3 - Parcubacteria
Bin_38_3 - Armatimonadetes - unknown Chthonomonas
Bin_49_2 - Chloroflexi - unknown Anaerolinea
Bin_35_1 - Deltaproteobacteria - Desulfobacteraceae
Bin_30_1 - unknown Nitrospirae
Bin_22_1 - Woesearchaeota (DHVEG−6)
Bin_47_3 - Bacteria - Microgenomates?
Bin_7_4 - Armatimonadetes - unknown Chthonomonas
Bin_38_2 - unknown Bacteria
Bin_40 - Deltaproteobacteria - unknown Desulfobacteraceae
Bin_48_3 - unknown Betaproteobacteria
Bin_18 - Firmicutes - Candidatus Desulforudis
Bin_46 - unknown Gemmatimonadetes
Bin_44_1 - unknown Bacteria
Bin_56_1 - Parcubacteria
Bin_41 - unknown Bacteria
Bin_62 - Firmicutes - Ammonifex
Bin_56_3 - Bacteria - Parcubacteria?
Bin_31 - unknown Gemmatimonadetes
Bin_21_1 - unknown Bacteria
Bin_59 - unknown Deltaproteobacteria
Bin_24_2 - Parcubacteria
Bin_16 - Parcubacteria
Bin_47_1 - Microgenomates
Bin_64 - Deltaproteobacteria - unknown Desulfobulbus
Bin_24_1 − Actinobacteria - OPB41
Bin_7_3 - Parcubacteria
Bin_57_1 - unknown Nitrospirae
Bin_38_1 - Bacteria - Microgenomates
Bin_36_1 - Chloroflexi - Dehalococcoidaceae - JG30−KF−CM66
Bin_11 - Chlofoflexi - Anaerolinea
Bin_21_2 - Parcubacteria
Bin_5 - Chloroflexi - unknown Dehalococcoidaceae
Bin_35_2 - Firmicutes - Dethiobacter
Bin_56_2 - unknown Chloroflexi
Bin_13_2 - unknown Chloroflexi
Bin_65 - Firmicutes - unknown Symbiobacterium
Bin_29_2 - unknown Nitrospirae
Bin_27_2 - unknown Deltaproteobacteria
Bin_45 - unknown Deltaproteobacteria
Bin_36_2 - Planctomycetes - CCM11a
Bin_52_2 - unknown Chloroflexi
Bin_61 - Chlofoflexi - unknown Anaerolinea
Bin_27_1 - Armatimonadetes - unknown Chthonomonas
Bin_44_4 - Chlorobi - unknown Ignavibacteria
Bin_19 - Chloroflexi - vadinBA26
Bin_22_2 - Candidate Division OP3
Bin_52_1 - unknown Bacteria
Bin_1_1 - Chlorobi - Ignavibacteria - BSV40
Bin_29_3 - Firmicutes - Ruminiclostridium
Bin_30_2 - unknown Nitrospirae
Bin_44_5 - Firmicutes - Acholeplasma
Bin_43 − Actinobacteria - OPB41
Bin_8_5 - Betaproteobacteria - unknown Comamonadaceae
Bin_37_1 - Actinobacteria - Gaiellales
Bin_7_2 - Candidate Division OP3
Bin_25 - unknown Hydrogenedentes
Bin_60_2 - Deltaproteobacteria - Desulfurivibrio
Bin_58 - Deltaproteobacteria - Sva0081 sediment group
Bin_26 - Deltaproteobacteria - 43F−1404R
Bin_48_1 - unknown Gammaproteobacteria?
Bin_23 - Omnitrophica - NPL-UPA2
Bin_57_2 - unknown Nitrospirae
Bin_33 - Deltaproteobacteria - Desulfarculus
Bin_54 - Fibrobacteres - order 07
Bin_67_2 - unknown Deltaproteobacteria
Bin_63 - unknown Deltaproteobacteria
Bin_50 - unknown Actinobacteria
Bin_42 - unknown Deltaproteobacteria
Bin_32 - Deltaproteobacteria - Desulfatitalea
Bin_55 - unknown Deltaproteobacteria
Bin_10 - Chlorobi - unknown Ignavibacteria - SR−FBR−L83
Bin_34 - Betaproteobacteria - uncultured Oxalobacteraceae
20
30
40
0
10
40
60
0
20
0
20
60
100
Figure 3.2. Overview of genome bin size, differential coverage, completeness,
contamination and metabolic gene presence/absence. Bin number identifier and putative
phylogeny are listed on far right.
81
Putative
sulfate
reducers
are
quite
abundant
among
the
96
reconstructed
genome
bins
in
this
study.
More
than
1/3
of
the
bins
contain
the
gene
for
dissimilatory
sulfite
reductase
(dsrAB),
a
key
enzyme
in
sulfate
reduction
(Figure
3.2).
It
has
been
shown
that
hydrogen
is
often
used
as
an
electron
donor
for
sulfate
reduction
in
deep
subsurface
microbes
(Gihring
et
al.,
2007;
Lin
et
al.,
2006).
However,
because
hydrogenases,
which
are
found
in
every
bin,
catalyze
many
different
metabolic
reactions,
we
cannot
say
with
confidence
that
they
involved
in
microbial
sulfate
reduction.
Interestingly,
canonical
genes
involved
in
sulfur
oxidation
(soxABKXYZ)
were
not
detected
in
any
genome
bins.
It
should
Table 3.2. List of enzymes used to determine carbon fixation capability in genome bins
Reductive
Acetyl CoA
3-
Hydroxypropionate
bicycle
3-
Hydroxypropionate
/4-Hydroxybutyrate
Dicarboxylate/4-
Hydroxybutyrate
Reductive
Citric Acid
Calvin
Cycle
1.2.99.2 1.3.4.1 6.4.1.3 2.7.9.1 CCS
1.3.1.6 1.2.1.76 2.7.9.2 4.1.1.39
1.2.1.43 2.8.3.22 1.1.1.- 4.1.1.31 CCL
5.4.99.2 6.2.1.40 1.1.1.37
3.5.4.9 6.4.1.3 6.2.1.- 4.2.1.2 4.1.1.31
1.3.1.84 4.2.1.120 1.3.4.1
1.5.1.20 42.1.116 4.2.1.17 1.3.1.6 1.3.1.6
6.2.1.36 1.1.1.35 2.8.3.22
2.3.1.169 6.2.1. 6.4.1.2 1.2.1.76 2.7.9.1
1.1.1.298 1.2.1.75 1.1.1.-
1.2.7.4 1.1.1. 1.1.1.298 6.2.1.40 2.7.9.2
1.2.1.75 6.2.1.36 6.2.1.-
6.3.4.3 6.4.1.2 4.2.1.116 4.2.1.17 1.3.4.1
4.2.1.1.53 1.3.1.84 1.1.1.35
1.5.1.5 5.4.1.3
4.1.3.24
2.1.1.258 4.2.1.148
* Kyoto Encyclopedia of Genes and Genomes (KEGG) identifiers
CCL = citryl-CoA lyase
CCS = citryl-CoA ligase
82
also
be
noted
that
genes
involved
in
methanogenesis
were
not
detected
(and
neither
were
marker
genes
for
Euryarchaeota).
3.4.3
Putative
genome
bin
phylogeny
Phylogenetic
classification
for
all
genomes
in
this
study
can
be
found
in
Figures
3.2
and
3.4.
Most
of
the
genomes
(92
of
96)
were
from
the
domain
Bacteria;
only
4
were
from
Archaea.
Within
the
Bacteria,
members
of
the
class
Deltaproteobacteria
are
highly
represented
in
both
SURF-‐B
and
–D
fluids.
Differential
coverage
information
between
these
two
fluids
is
shown
in
Figure
3.2,
with
similar
coverage
values
for
most
genomes
investigated.
The
exceptions
included
the
abundant
Chloroflexi
(Bin_52_2)
and
unknown
bacterial
lineage
(Bin_52_1)
in
SURF-‐B
and
numerous
members
of
the
Patescibacteria
superphylum,
Microgenomates
(formerly
OP11)
and
Parcubacteria
(formerly
OD1),
which
are
relatively
more
abundant
in
SURF-‐D.
We
note
that
the
genome
bins
for
these
two
phyla
are
40-‐75%
complete
(Figures
3.2,
3.3
and
3.4),
which
however,
does
not
necessarily
indicate
a
lack
of
coverage
or
faulty
assembly.
It
has
been
shown
83
Figure
3.3.
Breakdown
of
completeness
and
contamination
for
each
genomic
bin
as
measured
by
5
separate
single-‐copy
marker
gene
sets
(Alnberg,
et
al.,
2013;
Dupont
et
al.,
2012;
Campbell
et
al.,
2013;
Creevey
et
al.,
2011;
Wu
and
Scott,
2012).
Composite
values
are
in
the
right
hand
columns.
Strain
heterogeneity
was
measured
using
CheckM
(Parks
et
al.,
2015).
Wu et al.
Creevey et al.
Average
Campbell et al.
Alnberg et al.
Dupont et al.
Wu et al.
Creevey et al.
Average
Campbell et al.
Alnberg et al.
Dupont et al.
Completeness Contamination
Genome Bin Stats (%)
Strain Heterogeneity
0 20 60 100
Bin_7_1 - Microgenomates
Bin_2 - Armatimonadetes - unknown Chthonomonas
Bin_48_2 - unknown Betaproteobacteria
Bin_44_6 - unknown Firmicutes
Bin_1_2 - Chlorobi - Ignavibacteria - IheB3-7
Bin_68 - Thaumarchaeota - Candidatus Nitrososphaera
Bin_67_1 - Deltaproteobacteria - Nitrospinaceae
Bin_3_2 - unknown Gemmatimonadetes
Bin_3_1−TA06?
Bin_0 - Firmicutes - Pelotomaculum
Bin_60_1 - Deltaproteobacteria - unknown Desulfurivibrio
Bin_28 - Bacteria - Microgenomates?
Bin_13_1 - Bacteria - Parcubacteria?
Bin_49_1 - Chlofoflexi - Anaerolinea
Bin_21_3 - Parcubacteria
Bin_38_3 - Armatimonadetes - unknown Chthonomonas
Bin_49_2 - Chloroflexi - unknown Anaerolinea
Bin_35_1 - Deltaproteobacteria - Desulfobacteraceae
Bin_30_1 - unknown Nitrospirae
Bin_22_1 - Woesearchaeota (DHVEG−6)
Bin_47_3 - Bacteria - Microgenomates?
Bin_7_4 - Armatimonadetes - unknown Chthonomonas
Bin_38_2 - unknown Bacteria
Bin_40 - Deltaproteobacteria - unknown Desulfobacteraceae
Bin_48_3 - unknown Betaproteobacteria
Bin_18 - Firmicutes - Candidatus Desulforudis
Bin_46 - unknown Gemmatimonadetes
Bin_44_1 - unknown Bacteria
Bin_56_1 - Parcubacteria
Bin_41 - unknown Bacteria
Bin_62 - Firmicutes - Ammonifex
Bin_56_3 - Bacteria - Parcubacteria?
Bin_31 - unknown Gemmatimonadetes
Bin_21_1 - unknown Bacteria
Bin_59 - unknown Deltaproteobacteria
Bin_24_2 - Parcubacteria
Bin_16 - Parcubacteria
Bin_47_1 - Microgenomates
Bin_64 - Deltaproteobacteria - unknown Desulfobulbus
Bin_24_1 − Actinobacteria - OPB41
Bin_7_3 - Parcubacteria
Bin_57_1 - unknown Nitrospirae
Bin_38_1 - Bacteria - Microgenomates
Bin_36_1 - Chloroflexi - Dehalococcoidaceae - JG30−KF−CM66
Bin_11 - Chlofoflexi - Anaerolinea
Bin_21_2 - Parcubacteria
Bin_5 - Chloroflexi - unknown Dehalococcoidaceae
Bin_35_2 - Firmicutes - Dethiobacter
Bin_56_2 - unknown Chloroflexi
Bin_13_2 - unknown Chloroflexi
Bin_65 - Firmicutes - unknown Symbiobacterium
Bin_29_2 - unknown Nitrospirae
Bin_27_2 - unknown Deltaproteobacteria
Bin_45 - unknown Deltaproteobacteria
Bin_36_2 - Planctomycetes - CCM11a
Bin_52_2 - unknown Chloroflexi
Bin_61 - Chlofoflexi - unknown Anaerolinea
Bin_27_1 - Armatimonadetes - unknown Chthonomonas
Bin_44_4 - Chlorobi - unknown Ignavibacteria
Bin_19 - Chloroflexi - vadinBA26
Bin_22_2 - Candidate Division OP3
Bin_52_1 - unknown Bacteria
Bin_1_1 - Chlorobi - Ignavibacteria - BSV40
Bin_29_3 - Firmicutes - Ruminiclostridium
Bin_30_2 - unknown Nitrospirae
Bin_44_5 - Firmicutes - Acholeplasma
Bin_43 − Actinobacteria - OPB41
Bin_8_5 - Betaproteobacteria - unknown Comamonadaceae
Bin_37_1 - Actinobacteria - Gaiellales
Bin_7_2 - Candidate Division OP3
Bin_25 - unknown Hydrogenedentes
Bin_60_2 - Deltaproteobacteria - Desulfurivibrio
Bin_58 - Deltaproteobacteria - Sva0081 sediment group
Bin_26 - Deltaproteobacteria - 43F−1404R
Bin_48_1 - unknown Gammaproteobacteria?
Bin_23 - Omnitrophica - NPL-UPA2
Bin_57_2 - unknown Nitrospirae
Bin_33 - Deltaproteobacteria - Desulfarculus
Bin_54 - Fibrobacteres - order 07
Bin_67_2 - unknown Deltaproteobacteria
Bin_63 - unknown Deltaproteobacteria
Bin_50 - unknown Actinobacteria
Bin_42 - unknown Deltaproteobacteria
Bin_32 - Deltaproteobacteria - Desulfatitalea
Bin_55 - unknown Deltaproteobacteria
Bin_10 - Chlorobi - unknown Ignavibacteria - SR−FBR−L83
Bin_34 - Betaproteobacteria - uncultured Oxalobacteraceae
84
previously
that
these
bacteria
are
parasites
or
symbionts
and
hence,
do
not
contain
many
of
the
‘essential’
single
copy
marker
genes
(Hu
et
al.,
2016;
Nelson
et
al.,
2015;
Rinke
et
al.,
2013).
Candidate
phyla
are
extremely
abundant
in
deep
fluids
at
SURF
(25
of
96
genome
bins).
Four
genomes
(bins
7_2,
22_2,
23,
51_1)
from
microbial
dark
matter
(MDM)
are
tentatively
placed
within
the
PVC
superphylum,
and
closely
associated
with
the
phylum
Omnitrophica
(formerly
OP3).
However
their
placement
in
a
16S
rRNA
tree
is
not
monophyletic
with
Omnitrophica
(Figure
3.4),
requiring
further
investigation
with
comparisons
of
average
nucleotide
identity
(ANI)
and
average
amino
acid
identity
(AAI)
(see
below).
One
nearly
complete
(90%)
genome
is
associated
with
the
Hydrogenedentes
phylum
(formerly
NKB-‐19),
which
is
currently
defined
by
only
4
single
cell
amplified
genomes
(SAGs).
Lastly,
two
of
our
genome
bins
(22_1
and
44_1)
are
affiliated
with
the
recently
named
archaeal
phylum
Woesearchaeota
(Castelle
et
al.,
2015).
A
complete
list
of
the
SAGs
or
reconstructed
genomic
bins
from
this
and
other
studies
for
the
four
aforementioned
candidate
phyla
is
found
in
Table
3.3.
A
number
of
other
genome
bins
associate
with
candidate
phyla
(Armatimonadetes
,
formerly
OP10;
Microgenomates,
formerly
OP11;
Parcubacteria,
formerly
OD1),
but
their
investigation
will
be
reported
elsewhere.
85
Table&3.2.&Genomic(bin(information(for(four((
candidate(phyla(compared(in(this(study
Omnitrophica Scaffolds Genes %(Complete
Bin_7_2 10 1696 90
Bin_22_2 105 1589 94
Bin_23 192 3357 95
Bin_52_1 36 1480 87
2264867163 26 402 80
2264867160 35 432 13
2645728145 54 460 39
2264867158 60 685 41
LMZT00000000 90
JYNY0000000 87
Hydrogenedentes
Bin_25 144 2116 90
2527291518 106 2439 13K74
2264867134 84 1216 13K74
2264687135 84 1548 13K74
2264867132 30 344 13K74
2264867133 72 1046 13K74
Latescibacteria
Bin_41 24 812 57
2264867251 138 1578 57
2264867253 60 666 38
2264867252 120 1562 23
2264867254 164 1985 73
2654587945 83 3019
Woesearchaeota
Bin_44_1 6 1515 84
Bin_22_1 26 1363 70
CP010426( 1 1015 100
JWKX00000000( 17 1047 87
JWKW00000000( 26 796 78
JWKV00000000( 10 425 61
CP010425( 1 1309 100
JWKT00000000( 61 1008 76
JWKS00000000( 12 577 76
JWKP00000000( 19 1243 76
JWKO00000000( 14 996 63
86
87
3.4.4
Modes
of
carbon
fixation
Carbon
fixation
capability
was
examined
in
each
of
the
96
genome
bins
(see
Figure
3.5).
The
reductive
Acetyl-‐CoA
(Wood-‐Ljungdahl)
pathway
was
the
most
common;
33
genomes
contained
at
least
75%
of
the
necessary
genes
involved
in
this
pathway.
The
corresponding
lineages
were
diverse
and
included
Ammonifex,
Ca.
Desulforudis,
Dehalococcoidia,
Dethiobacter,
numerous
Deltaproteobacteria,
Actinobacteria,
Firmicutes
and
Chloroflexi,
as
well
as
members
of
the
candidate
phyla
Omnitrophica
and
Hydrogenedentes.
Only
4
genomes
contained
the
gene
encoding
Rubisco,
the
canonical
enzyme
involved
in
carbon
fixation
via
the
reductive
pentose
phosphate
(Calvin-‐Benson)
cycle:
a
member
of
the
phylum
Chloroflexi
(Anaerolineaceae),
and
3
members
of
the
phylum
Proteobacteria
(a
Gammaproteobacterium
and
two
Betaproteobacteria).
For
the
four
other
carbon
fixation
pathways
(3-‐hydroxypropionate
bi-‐cycle,
3-‐hydroxypropionate/4-‐
hydroxybutyrate,
dicarboxylate/4-‐hydroxybutyrate,
reductive
citric
acid
cycle)
results
were
less
conclusive.
No
genome
contained
all
of
the
known
genes
involved
in
any
of
these
pathways,
but
numerous
members
of
the
Deltaproteobacteria
contained
>80%
of
the
necessary
genes
for
the
reductive
citric
acid
(rTCA),
3-‐hydroxypropionate
bi-‐cycle,
3-‐
hydroxypropionate/4-‐hydroxybutyrate
and
dicarboxylate/4-‐hydroxybutyrate
cycles
(Figure
3.5).
Figure 3.4. Phylogeny based on 16S rRNA sequence analysis. Genomes indicated by
colored circles to right of taxonomic branch, color coded by % completeness. Red
branches indicate that one or more of our genomic bins falls within that lineage. Grey
branches are included for robust and diverse comparisons.
88
Bin Putative phylogeny # scaffolds # genes reductive 3-hydroxyprop 3-hydroxy/ dicarboxylate/ Calvin
number Acetyl CoA bicycle 4-hydroxybut 4-hydroxybut cycle
0 Peptococcaceae 120 2622 100%
2 Armatimonadetes 172 2763
5 Dehalococcoidia 74 1662 80%
10 Ignavibacteriales 114 3654
11 Anaerolineaceae 143 2257 60%
16 Parcubacteria 104 2242
18 Desulforudis 75 3205 40%
19 Dehalococcoidia 71 2067
23 OP3 192 3357 20%
25 Hydrogenedentes 144 4105
26 Deltaproteobacteria 105 3075 0%
28 Microgenomates 107 1705
31 Gemmatimonadetes 220 3924
32 Deltaproteobacteria 197 5969
33 Desulfarculus 78 3539
34 Betaproteobacteria 17 2253
40 Deltaproteobacteria 316 7377
41 Latescibacteria 24 810
42 Pelobacter 145 4482
43 Actinobacteria;OPB41 104 3392
45 Deltaproteobacteria 137 3507
46 Gemmatimonadetes 99 1631
50 Actinobacteria 58 2812
54 Fibrobacteria 140 3190
55 Desulfobacteraceae 90 4281
58 Desulfobacteraceae 268 5070
59 Deltaproteobacteria 179 3873
61 Anaerolineaceae 84 2238
62 Ammonifex 89 1892
63 Deltaproteobacteria 100 4680
64 Deltaproteobacteria 108 2395
65 Firmicutes 101 2261
66 Ignavibacteriales 105 1519
68 Thaumarchaeota 127 2595
1_1 Ignavibacteriales 173 4163
1_2 Ignavibacteriales 132 2224
13_1 Parcubacteria 28 637
13_2 Chloroflexi 13 1017
21_1 Bacteria 40 1043
21_2 Parcubacteria 18 804
21_3 Parcubacteria 29 747
22_1 Woesearchaeota 26 1363
22_2 OP3 105 2775
24_1 Actinobacteria 22 1501
24_2 Parcubacteria 34 769
27_1 Armatimonadetes 61 1573
27_2 Deltaproteobacteria 108 2002
29_2 Nitrospiraceae 63 1502
29_3 Ruminiclostridium 225 5242
3_1 TA06 40 835
3_2 Gemmatimonadetes 105 1577
30_1 Nitrospirae 120 2914
30_2 Nitrospirae 106 2771
30_3 Nitrospirae 122 2451
35_1 Deltaproteobacteria 149 2911
35_2 Dethiobacter 79 2434
36_1 Dehalococcoidia 82 2131
36_2 Phycisphaerae 155 3489
37_1 Gaiellales 77 2392
38_1 Microgenomates 34 1343
38_2 Bacteria 52 1672
38_3 Armatimonadetes 28 597
44_1 Bacteria 6 1515
44_4 Ignavibacteriales 117 2337
44_5 Acholeplasma 46 1705
44_6 Firmicutes 97 1356
47_1 Microgenomates 29 1300
47_3 Microgenomates 32 991
48_1 Gammaproteobacteria 50 1774
48_2 Betaproteobacteria 64 1121
48_3 Betaproteobacteria 104 2203
49_1 Anaerolineaceae 111 2053
49_2 Anaerolineaceae 149 4034
52_1 OP3 36 1480
52_2 Chloroflexi 21 1047
56_1 Parcubacteria 35 931
56_2 Chloroflexi 14 774
56_3 Parcubacteria 19 885
57_1 Nitrospiraceae 74 3041
57_2 Nitrospiraceae 87 2653
60_1 Deltaproteobacteria 82 1123
60_2 Deltaproteobacteria 118 3854
67_1 Nitrospinaceae 90 1328
67_2 Deltaproteobacteria 183 4450
7_1 Microgenomates 20 476
7_2 OP3 10 1696
7_3 Parcubacteria 25 1169
7_4 Armatimonadetes 27 874
8_5 Commomonadacea 106 4253
rTCA Legend
89
3.4.5
ANI/AAI
comparisons
Average
nucleotide
identity
(ANI)
and
average
amino
acid
identity
(AAI)
were
compared
between
genomes
from
the
present
study
and
previously
sequenced
genomes
for
the
following
four
candidate
phyla:
Omnitrophica
(formerly
OP3),
Hydrogenedentes
(formerly
NKB-‐19),
Latescibacteria
(formerly
WS3)
and
the
Woesearchaeota
(Figure
3.6).
Maximum
ANI
values
between
previously
sequenced
and
genomes
in
this
study
were
72.5-‐
82.7%
(Figure
3.5c
and
d).
In
general,
ANI
values
were
below
the
accepted
cutoff
for
direct
comparison
(cutoff
=
72%);
this
is
equally
true
for
the
four
candidate
phyla
of
interest,
as
well
as
all
other
candidate
phyla
identified
in
this
study.
AAI
values
were
also
very
low
(<43.8%).
Note
that
bins
for
members
of
the
Omnitrophica
(OP3)
and
Hydrogenedentes
(NKB-‐19)
were
especially
divergent
(Figure
3.6a
and
c).
3.5
Discussion
Next
generation
Illumina
sequencing
was
employed
to
explore
microbial
community
composition
and
metabolic
capabilities
in
the
terrestrial
deep
biosphere.
This
is
one
of
very
few
studies
using
this
sequencing
technology
on
deep
terrestrial
fluids
(Dong
et
al.,
2014;
Magnabosco
et
al.,
2015).
We
recovered
96
reconstructed
genomes
that
are
>20%
complete
and
gained
new
insights
into
the
metabolic
capacity
of
microbial
dark
matter
by
Figure 3.5. Carbon fixation pathways found in genomic bins. Dark red
indicates that all genes coding for irreversible enzymes involved in
prokaryotic carbon fixation are present, white indicates no genes belonging
to that pathway are present. Candidate phylum bins are in grey.
90
examining
25
genomes
of
candidate
phyla.
Here
we
describe
the
first
report
of
autotrophy
within
the
candidate
phylum
NKB19
(Hydrogenedentes)
(Rinke
et
al.,
2013).
We
identified
4
near
complete
(>90%)
genomes
associated
with
the
cosmopolitan
candidate
phylum
OP3
(Omnitrophica),
a
phylum
that
previously
had
a
total
of
only
5
single
cell
or
reconstructed
genomes
(Kolinko
et
al.,
2016;
Rinke
et
al.,
2013;
Speth
et
al.,
2016).
Furthermore,
average
nucleotide
identity
(ANI)
and
average
amino
acid
identity
(AAI)
analysis
between
ours
and
previously
published
candidate
phyla
genomes
indicate
that
four
of
the
genomes
in
this
study
constitute
two
previously
unrecognized
phyla
within
the
domain
Bacteria.
3.5.1
Biological
transformation
of
nitrogen,
sulfur,
hydrogen
and
methane
Genetic
evidence
of
nitrogen
transformation,
both
denitrification
to
ammonium
and
fixation
of
dinitrogen,
was
observed
in
many
genomic
bins.
The
genes
for
the
nitrogenase
complex
(e.g.
nifH)
were
found
in
almost
25%
of
bins.
indicating
that
numerous
members
of
this
deep
subsurface
community
are
capable
of
fixing
N2.
This
fixed
nitrogen
likely
feeds
the
dissimilatory
nitrogen-‐transforming
metabolisms
(denitrification),
indicated
by
the
commonality
of
narG
and
periplasmic
nitrite
and
nitrate
reductases
in
genomic
bins.
Although
nitrite
was
below
detection
limit,
nitrate
measured
10.3
and
23.7
µM
in
SURF-‐D
and
–B
fluids,
respectively
(Osburn
et
al.,
2014;
Momper
et
al.,
2016).
Thermodynamic
calculations
indicate
that
nitrate
reduction
(especially
with
hydrogen
as
an
electron
donor)
is
highly
exergonic
in
SURF-‐B
and
–D
fluids
(Osburn
et
al.,
2014),
supporting
the
genomic
evidence
that
nitrogen
cycling
is
highly
active
in
these
subsurface
fluids.
Putative
sulfate
reducers
are
abundant
among
the
reconstructed
genome
bins
in
this
study,
with
>1/3
of
all
bins
containing
the
dsrAB
gene
(Figure
3.2).
Interestingly,
the
91
genes
for
dsrAB
and
nifH
were
both
present
in
Bin_18,
a
phylogenetic
relative
to
Ca.
Desulforudis
audaxviator,
a
member
of
the
Firmicutes
that
has
been
found
in
other
terrestrial
and
marine
subsurface
environments
(Baker
et
al.,
2003;
Jungbluth
et
al.,
2012;
Magnobosco
et
al.,
2015).
In
ultra-‐deep
fracture
water
in
South
Africa
it
was
found
to
dominate
(>99%)
the
microbial
community.
Genomic
sequencing
of
that
lineage
revealed
an
almost
self-‐sufficient
chemolithoautotrophic
microbe,
capable
of
carbon
and
nitrogen
fixation
and
sulfate
reduction
using
hydrogen
as
an
electron
donor
(Chivian
et
al.,
2008).
Notably,
genes
involved
in
sulfur
oxidation
are
completely
absent,
both
from
genomic
bins
and
from
the
annotated
metagenomes
as
a
whole.
This
is
especially
striking
when
in
situ
geochemical
conditions
are
considered:
S
2-‐
is
found
at
83-‐130
µg/L
in
SURF-‐B
and
–D
fluids
(Osburn
et
al.,
2014)
making
it
an
abundant
electron
donor.
However,
thermodynamic
calculations
indicate
that
the
energy
density
for
sulfur
oxidation
with
either
oxygen
or
nitrate
is
too
low
for
either
of
these
reactions
to
be
energetically
favorable
(Osburn
et
al.,
2014).
Using
a
combination
of
metagenomic,
geochemical
and
thermodynamic
results,
we
conclude
that
sulfate
reduction
to
sulfur
or
sulfide
is
likely
an
important
energy
strategy
in
these
subsurface
fluids;
however,
the
cycling
of
reduced
sulfur
back
to
sulfate
is
likely
not
mediated
biotically
due
to
extremely
low
concentrations
of
electron
acceptors.
92
a
b
c
d
Figure 3.6. Average nucleotide (ANI) and amino acid (AA) comparisons for candidate
phyla a) Hydrogenedentes b) Woesarchaeota c) Omnitrophica and d) Latescibacteria.
ANI values are in the top triangle of the heatmaps, AAI values are in the bottom boxes.
Grey boxes indicate a value too low for valid comparison.
93
3.5.2
Carbon
fixation
Although
photosynthetically
derived
organic
carbon
can
be
found
in
Earth’s
subsurface
it
is
often
recalcitrant
and
a
limiting
nutrient
(Pedersen,
2000).
At
the
deep
sites
in
SURF,
where
surface-‐derived
fixed
carbon
is
likely
limited,
many
resident
microbes
must
rely
on
in
situ
fixation
of
inorganic
carbon
(CO2,
HCO3
-‐
)
by
chemolithoautotrophs,
including
nitrate
reducers,
methanogens,
acetogens,
sulphate
reducers
and
iron
reducers
(Chivian
et
al.,
2008;
Beal
et
al.,
2009;
Stevens,
1997;
Stevens
and
McKinley,
1995;
Pedersen,
2000;
Sherwood
Lollar
et
al.,
2006;
Magnabosco
et
al.,
2015).
As
noted
above,
the
most
common
mode
of
carbon
fixation
in
the
96
genome
bins
was
the
reductive
acetyl-‐CoA
cycle.
This
ancient
pathway
is
the
only
one
known
to
be
used
by
both
Archaea
and
Bacteria
(Hugler
and
Sievert,
2010).
The
predominance
of
this
pathway
was
also
documented
in
the
metagenomic
analysis
of
another
terrestrial
deep
subsurface
environment,
the
Witwatersrand
Basin,
South
Africa
(Magnabosco
et
al.,
2015).
That
study
concluded
that
the
preference
for
the
reductive
acetyl-‐CoA
cycle
was
in
response
to
energy
limitation,
it
being
energetically
inexpensive
compared
to
the
other
5
pathways
(Berg,
2011;
Hugler
and
Sievert,
2010),
and
hence
ideal
for
organisms
operating
near
the
thermodynamic
limits
of
life.
Furthermore,
the
acetyl-‐CoA
cycle
requires
anoxic
conditions,
since
some
of
its
enzymes,
especially
the
crucial
acetyl-‐CoA
synthase,
are
highly
oxygen
sensitive
(Berg,
2011).
This
pathway’s
high
requirement
for
metals
(Mo,
Co,
Ni,
Fe)
(Berg,
2011)
that
are
far
more
soluble
under
reducing
conditions
also
points
to
anoxic
environments.
Because
of
energetic
efficiency
and
the
necessity
for
anoxia,
the
acetyl-‐CoA
94
pathway
is
the
ideal
mode
of
inorganic
carbon
fixation
in
highly
reducing,
aphotic
and
energy-‐deplete
deep
subsurface
fluids,
including
those
encountered
at
SURF
(ORP
is
-‐235
to
-‐276
mV).
Members
of
the
phylum
Chloroflexi
commonly
use
the
3-‐hydroxypropionate
bi-‐cycle
for
carbon
fixation
(Hugler
and
Sievert,
2010).
In
our
10
Chloroflexi
genome
bins,
however,
evidence
for
this
pathway
was
rare.
Our
results
may
be
explained
by
the
high
energetic
costs
of
this
pathway;
it
requires
seven
ATP
equivalents
for
the
synthesis
of
pyruvate
and
three
additional
ATPs
for
triose
phosphate
(Berg,
2011).
In
many
lineages
of
Chloroflexi,
the
energy
cost
is
offset
by
phototrophy,
which
is
not
possible
in
the
deep
dark
subsurface
at
SURF.
Instead,
5
Chloroflexi
genomes
contain
the
complete
or
near-‐complete
reductive
acetyl-‐CoA
pathway
(85-‐100%
of
genes)
and
one
(bin
61)
has
the
gene
coding
for
RuBisCO,
the
canonical
gene
indicative
of
the
Calvin
cycle
(Figure
3.5).
3.5.3
Comparisons
of
candidate
phyla
in
this
and
other
studies
In
the
literature,
the
candidate
phylum
Hydrogenedentes
is
represented
only
by
4
SAGs,
with
a
maximum
completeness
of
74%
(Rinke
et
al.,
2013).
Here
we
report
a
reconstructed
genome
(bin
25)
that
is
nearly
complete
(90%,
Table
3.3).
Hydrogenedentes
(formerly
NKB19)
was
first
identified
in
the
deep
marine
subsurface
at
Nankai
Trough
(Japan),
but
since
has
been
detected
globally
in
freshwater
sediments
(Ni
et
al.,
2015),
terrestrial
geothermal
springs
(Badhai
et
al.,
2015),
anaerobic
bioreactors
(Lykidis
et
al.,
2011;
Nobu
et
al.,
2015;
Rinke
et
al.,
2013)
and
wastewater
treatment
facilities
(Choi
and
Liu,
2014;
Chouari
et
al.,
2015).
Bin_25
in
the
present
study
possesses
genes
for
the
autotrophic
fixation
of
carbon
dioxide
via
the
reductive
acetyl-‐CoA
pathway
and
all
genes
95
encoding
the
essential
enzymes
in
both
sulfate
reduction
and
nitrate
reduction
(Figure
3.7).
This
is
the
first
evidence
of
autotrophy
and
the
first
evidence
of
these
dissimilatory
metabolisms
within
this
phylum,
which
may
have
major
ramifications
for
how
we
view
carbon,
nitrogen
and
sulfur
cycling
in
the
subsurface,
but
also
in
other
marine
and
terrestrial
environments.
96
NO
3
–
NO
2
–
H
+
4 H
+
A
B
C
Periplasm
Cytosol
Periplasm
Cytosol
narG
Cyt b
Figure 3.7. Anabolic and catabolic pathways in Bin_25 (NKB-19). A) Cytosolic
nitrate reduction to nitrite. B) Carbon fixation via the acetyl-CoA pathway
C) Sulfate reduction: sat (ATP sulfurylase), apsAB (adenyl sulfate reductase) dsrAB
97
Data
presented
in
this
study
nearly
doubled
the
number
of
analyzed
genomes
in
the
candidate
phylum
Omnitrophica
(OP3)
(Kolinko
et
al.,
2015;
Rinke
et
al.,
2013;
Speth
et
al.,
2016).
This
phylum
was
originally
identified
in
a
terrestrial
hydrothermal
spring,
Obsidian
Pool,
in
Yellowstone
National
Park,
USA,
leading
to
its
moniker
“OP3”
(Hugenholtz
et
al.,
1998).
Since
then,
this
phylum
has
been
detected
globally
in
environments
such
as
flooded
paddy
soil
(Derakshani
et
al.,
2001),
freshwater
lakes
and
marine
estuaries
(Rinke
et
al.,
2013),
lake
sediments
(Kolinko
et
al.,
2015),
wastewater
bioreactors
(Speth
et
al.,
2016)
and
the
terrestrial
subsurface
(Rinke
et
al.,
2013
and
this
study).
Without
cultured
members,
and
with
previously
very
little
genetic
sequence
data
to
analyze,
OP3
was
placed
within
the
Planctomycetes-‐Verrucomicrobia-‐Chlamydiae
(PVC)
superphylum,
along
with
Lentisphaerae
(added
later)
(Pilhofer
et
al.,
2008;
Wagner
and
Horn,
2006).
Here
we
present
the
most
complete
Ca.
Omnitrophica
genome
to
date
(95%,
Bin_23).
Among
our
four
genomic
bins
(7_2,
22_2,
23,
52_1)
and
similar
to
Rinke
et
al.
(2013),
we
found
genes
for
carbon
fixation
via
the
reductive
acetyl-‐CoA
pathway.
However,
unlike
Rinke
et
al.
(2013),
we
did
not
detect
evidence
for
sulfate
reduction,
but
found
the
complete
gene
suite
for
nitrate
reduction.
This
capability
and
an
active
role
in
nitrogen
cycling
was
recently
reported
by
Speth
et
al.
(2016).
We
posit
that
members
of
the
Ca.
Omnitrophica
phylum
likely
play
critical
roles
in
nitrogen
and
carbon
cycling
in
the
many
types
of
environments
in
which
they
have
been
found.
The
candidate
phylum
WS3
(Wurtsmith
aquifer
Sequences-‐3)
was
first
identified
in
98
a
16S
rRNA
gene-‐based
survey
of
anoxic
sediments
in
Michigan,
USA
(Dojka
et
al.,
1998).
Since
then,
this
phylum
has
been
documented
across
a
wide
range
of
habitats,
including
marine
hydrothermal
vents,
cold
methane
seeps,
cave
rock
walls,
marine
sediments,
soils,
wastewater
treatment
bioreactors,
the
deep
sea,
hypersaline
anoxic
lakes,
and
oil-‐exposed
microbial
mats
(Briggs
et
al.,
2011;
Carbonetto
et
al.,2014;
Fuchsman
et
al.,
2011;
Hernandez-‐Raquet
et
al.,
2006;
Ikenaga
et
al.,
2010;
Kormas
et
al.,
2008;
Periera
et
al.,
2014;
Reed
et
al.,
2006;
Schabereiter-‐Gurtner
et
al.,
2004).
Phylogenomic-‐based
analysis
using
conserved
marker
genes
indicated
the
monophyletic
nature
of
WS3
as
part
of
the
Fibrobacteres–Chlorobi–Bacteroidetes
(FCB)
superphylum.
(Youssef
et
al.,
2015)
To
date,
this
phylum
is
comprised
of
4
SAGs
amplified
as
part
of
the
MDM
project
and
reported
in
Rinke
et
al.,
(2013).
Here
we
add
a
new
and
relatively
complete
(57%)
reconstructed
genome
to
this
candidate
phylum
(Table
3.3),
for
which
the
name
“Latescibacteria”
(hiding
small
rods)
was
suggested
(Rinke
et
al.,
2013).
Finally,
we
identified
two
genomic
bins
within
the
Woesearchaeota,
a
phylum
described
previously
as
consisting
of
symbiotic
or
parasitic
microbes
(Castelle
et
al.,
2015).
The
Woesearchaeota
appear
to
lack
most
metabolic
pathways
for
the
production
of
amino
acids
and
carbohydrates,
as
well
as
those
for
the
fermentation
or
glycolysis
of
sugars.
However,
in
the
present
study,
Bin_22_1,
putatively
classified
as
Woesearchaeota,
has
a
near-‐complete
citric
acid
(TCA)
cycle,
indicating
that
this
phylum
may
not
be
as
co-‐
dependent
as
previously
thought.
99
3.5.4
Concluding
remarks
This
study
used
high-‐throughput
Illumina
sequencing
to
investigate
microbial
ecosystems
in
the
terrestrial
DSB.
Contrary
to
two
previous
communications
on
microbial
genomics
in
deep
terrestrial
fluids
(Chivian
et
al.,
2008;
Dong
et
al.,
2014),
we
find
that
fluids
at
SURF
are
not
dominated
by
1-‐2
species,
but
rather
are
host
to
diverse
microbial
assemblages.
This
is
more
in
line
with
the
findings
of
Magnabosco
et
al.
(2015)
in
Precambrian
deep
continental
crust,
who
detected
a
variety
of
bacterial
and
archaeal
phyla,
dominated
by
Firmicutes.
Of
particular
interest
in
this
study
are
the
metabolic
capabilities
within
the
25
reconstructed
genomes
of
microbial
dark
matter.
Within
those
genomes,
according
to
16S
rRNA
phylogeny,
4
were
loosely
associated
with
the
phylum
Omnitrophica
(Figure
3.4).
However,
Bin_7_2
was
highly
divergent
and
not
closely
associated
with
any
phylum,
branching
somewhat
near
the
Verrucomicrobia
(of
the
PVC
superphylum).
ANI
and
AAI
comparisons
between
Bin_7_2
and
all
SAGs
and
reconstructed
genomes
of
this
phylum
revealed
that
Bin_7_2
was
also
highly
divergent
on
the
amino
acid
level
(too
low
for
accurate
comparison).
Based
on
both
16S
rRNA
and
amino
acid
comparisons,
we
propose
a
new
candidate
phylum,
within
the
PVC
superphylum.
5.6
Data
deposit
Sequence
data
for
metagenomic
reads,
contigs
and
genes
were
submitted
to
the
JGI-‐
IMG
and
under
accession
number
IMG
3300007354,
3300007352
and
3300007351
for
SURF-‐B
and
–D
fluids,
and
the
combined
assembly,
respectively.
100
Acknowledgements
This
work
was
supported
by
the
NASA
Astrobiology
Institute
under
cooperative
agreement
NNA13AA92A.
Many
thanks
to
John
Heidelberg
and
Rohan
Sachdeva
at
USC
for
use
of
their
servers
and
help
in
metagenomic
analysis.
We
would
also
like
to
recognize
A.
Murat
Eren
(http://merenlab.org)
for
his
invaluable
help
in
utilizing
the
anvi’o
interface.
We
want
to
thank
especially
staff
and
personnel
at
SURF
for
access
to
the
deep
subsurface
and
repeated
access
to
samples
used
in
this
study.
Conflict
of
interest
The
authors
have
no
conflicts
of
interest
to
report.
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113
CHAPTER
4
Physiological
Characterization
of
Low-‐Energy
Adapted,
Deep-‐Subsurface
Anaerobes:
Spirosphaera
subterraneum
gen.
nov.,
sp.
nov.
isolated
from
fluids
1.5
km
in
the
terrestrial
subsurface
By
Lily
Momper
Coauthors:
Amanda
Semler,
Guang
Sin-‐Lu,
Hiroyuki
Imachi
and
Jan
P.
Amend
In
preparation
for
International
Journal
of
Systematic
and
Evolutionary
Microbiology
114
However
great
and
mighty
the
marvels
of
nature
may
seem
to
us,
they
are
always
to
be
explained
by
physical
reasons.
Everything
is
subordinate
to
some
great
law
of
nature.
―
Jules
Verne,
Journey
to
the
Center
of
the
Earth
115
4.1
Abstract
A
novel,
obligately
anaerobic
bacterium
(strain
SURF-‐ANA1
T
)
was
isolated
from
deep
continental
subsurface
fluids
at
a
depth
of
1,500
meters
below
surface
in
the
former
Homestake
Gold
Mine
(now
Sanford
Underground
Research
Facility,
in
Lead,
South
Dakota
USA).
I
employed
specialized
culturing
techniques
to
target
and
isolate
this
deep
subsurface
strain.
My
collaborator
and
co-‐author,
Dr.
Hiroyuki
Imachi,
instructed
me
in
the
building
and
operation
of
low
energy,
continuous
flow
bioreactors.
I
collected
fluids
1.5
km
below
surface
at
SURF
and
incubated
them
in
these
bioreactors
for
18
months,
collecting
effluent
samples
every
6
months.
In
the
final
samples
I
collected,
I
detected
spiral
cells
using
phase
contract
microscopy.
16S
rRNA
gene
sequencing
revealed
a
divergent
bacterium,
related
to
the
family
Spirochaetaceae.
This
bacterium
was
targeted
for
isolation.
Serial
dilution
of
anaerobic
cultures
yielded
a
pure
strain,
dubbed
SURF-‐ANA1,
the
first
anaerobic
strain
isolated
from
the
SURF
location.
Cells
of
strain
SURF-‐ANA1
were
Gram-‐
negative,
helical,
non-‐spore-‐forming
and
ranged
in
size
from
0.25–0.55
x
5–75
µm
with
a
wavelength
of
0.5-‐0.62
µm.
Strain
SURF-‐ANA1
T
grew
at
15-‐50
o
C
(optimally
at
40
o
C)
at
pH
4.8-‐9.0
(optimally
at
pH
7.2-‐7.4).
The
major
cellular
fatty
acids
in
order
of
decreasing
abundance
(comprising
>5%
of
total)
were
10-‐methyl
C16:0,
iso-‐C15:0,
C18:2
and
C18:0
DMA
and
C20:0
Methylene-‐nonadecanoic
acid.
Phylogenetic
analysis
based
on
the
16S
rRNA
gene
sequence
of
strain
SURF-‐ANA1
indicated
a
distant
relationship
to
strains
representing
genera
within
the
family
Spirochaetaceae.
The
closest
relatives
according
to
16S
rRNA
sequence
similarity
are
Spirochaeta
stenostrepta
(88%)
and
Spirochaeta
caldaria
(87%).
Based
on
its
phylogenetic
and
metabolic
distinctness,
strain
SURF-‐ANA-‐1
is
considered
to
116
represent
a
novel
genus
and
species
within
the
family
Spirochaetaceae,
for
which
the
name
Spirosphaera
subterraneum
gen.
nov.,
sp.
nov.
is
proposed.
The
type
strain
of
Spirosphaera
subterraneum
is
SURF-‐ANA1
T
.
To
our
knowledge,
this
is
the
first
report
of
an
isolate
within
the
phylum
Spirochaetes
from
the
deep
terrestrial
subsurface.
The
GenBank/EMBL/DDBJ
accession
number
for
the
16S
rRNA
gene
sequence
of
strain
SURF-‐ANA1
T
is
KU359248.
4.
2
Background
and
specialized
culturing
methods
Earth’s
deep
subsurface
is,
for
the
most
part,
an
energy-‐starved
biome
functionally
defined
by
the
presence
of
microbial
life
and
the
lack
of
light
or
light-‐derived
biomass.
This
vast
biosphere
represents
the
largest
microbial
habitat
on
Earth
(Edwards
et
al.,
2012).
The
deep
subsurface,
in
particular
the
terrestrial
subsurface,
has
only
recently
been
appreciated
as
a
dynamic,
populated,
metabolically
active
biome,
actively
interacting
with,
perhaps
controlling,
global
elemental
cycles.
The
habitable
limits
of
the
subsurface
are
as
yet
unknown:
in
the
continental
subsurface,
microbial
life
has
been
detected
in
even
the
deepest
samples
(to
date,
3.3
km
below
Earth
surface)
(Baker,
et
al.,
2003).
Estimates
of
the
total
marine
and
terrestrial
deep
subsurface
biomass
are
17-‐155
petagrams
carbon
(Kallmeyer
et
al.,
2013;
McMahon
and
Parnell,
2014;
Teske
et
al.,
2005;
Whitman
and
Coleman,
1998)
but
the
metabolisms
employed
therein
remain
almost
completely
unknown.
It
is
known,
however,
that
prokaryotes
reside
in
the
subsurface;
the
overwhelming
majority
has
eluded
cultivation,
and
hence,
how
they
are
making
a
metabolic
living
remains
unclear.
Elucidating
the
energetics,
metabolisms
and
physiologies
of
deep
subsurface
117
microbes
remains
an
elusive
yet
fundamental
goal
in
understanding
the
subsurface
biosphere.
Biological
samples
retrieved
from
the
deep
subsurface
have
been
recalcitrant
to
traditional
cultivation
techniques
and
successfully
cultivating
its
residents
requires
innovative
methodology.
Culture-‐based
characterization
of
subsurface
prokaryotes
has
fallen
far
behind
genomics-‐based
community
surveys.
This
is
primarily
because
a)
microbes
in
the
deep
subsurface
are
typically
fastidious
anaerobes
(organisms
that
use
a
terminal
electron
acceptor
other
than
O2)
adapted
to
energy-‐limiting
conditions
and
b)
traditional
laboratory
culturing
techniques
do
not
address
the
extreme
environmental
conditions
to
which
the
microbes
are
adapted.
Novel
organisms
sampled
from
new
environments
require
innovative
culturing
techniques.
To
address
this
dearth
between
in
situ
versus
culturing
conditions
our
collaborator,
Dr.
Hiroyuki
Imachi,
designed
a
variety
of
anoxic
bioreactors
for
cultivation
of
fastidious
subsurface
prokaryotes
(Figure
4.1)
(Imachi
et
al.,
2011).
The
bioreactors
are
inoculated
with
environmental
water
samples
or
mineral
slurry
and
each
reactor
can
be
adapted
to
mimic
a
specific
subsurface
environment.
The
temperature,
influent
medium
composition,
gaseous
phase
and
colonization
substrate
can
be
changed
to
mimic
conditions
found
in
a
particular
study
site.
Influent
flow
rate
is
tightly
controlled
via
a
peristaltic
pump
to
maintain
an
energy-‐limited
ecosystem
inside
the
bioreactor.
Sterile
polyurethane
sponges
or
mineral
substrate
are
placed
inside
the
bioreactor,
through
which
inoculum
or
media
slowly
percolates,
thereby
allowing
microbial
colonization
and
establishing
microbial
assemblages
on
the
physical
substrate.
Effluent
exiting
the
bottom
of
the
reactor
removes
metabolic
waste
products
and
prevents
toxin
buildup.
118
Figure
4.1.
Schematic
of
a
low
energy,
low
flow
bioreactor
utilized
in
the
current
study
a)
media
reservoir
b)
gas
phase
inlet
c)
gas
phase
outlet
d)
optional
sample
ports
e)
media
effluent
outlet
valve
P)
peristaltic
pump
for
media
and
gas
phase
input.
119
For
the
current
study,
water
and
rock
samples
were
collected
from
1.4
kilometers
below
surface
at
Homestake
Mine,
South
Dakota,
USA.
These
samples
incubated
in
low-‐flow
bioreactors
built
by
Lily
Momper
in
an
adaptation
of
the
original
design
by
Dr.
Imachi.
The
bioreactors
contained
mineral
(Fe
and
Mn)
substrates
and
media
designed
to
mimic
the
deep
subsurface
fluids
from
which
samples
were
collected.
After
18
months
of
incubation
in
low
energy,
anoxic
conditions,
collection
of
effluent
and
mineral
substrate
from
the
reactors
yielded
enrichments
of
subsurface
anaerobes.
These
enrichments
proved
excellent
inoculum
for
isolation
and
metabolic
characterization
of
typically
low-‐density,
slow-‐
growing
microorganisms.
Numerous
new
species
were
isolated
including
sulfate
reducers
of
the
genus
Desulfovibrio,
iron
reducers
of
the
genera
Bacteroidetes
and
Thermincola,
nitrate
reducers
of
the
genus
Delftia
and
hydrogen
oxidizers
of
the
family
Spirochaetaceae.
According
to
16S
rRNA
gene
sequence
analysis,
the
hydrogen
oxidizing
bacterium
was
the
most
divergent
from
any
previously
cultured
relatives
(88%
sequence
identity).
For
that
reason
the
Spirochaetaceae
was
selected
for
complete
characterization
and
publication,
discussed
in
the
following
manuscript.
Members
of
the
nitrate
reducing
Delftia
and
iron
reducing
Bacteroidetes
are
currently
being
characterized
by
undergraduate
Provost
and
WiSE
(Women
in
Science
and
Engineering)
fellows
mentored
by
Lily
Momper
during
her
time
in
Dr.
Jan
Amend’s
laboratory.
4.3
Introduction
In
the
past
two
decades,
exploration
of
the
marine
subsurface
by
international
ocean
drilling
programs
has
increased,
and
correspondingly
the
number
and
variety
of
120
microbial
isolates
from
that
biosphere
has
also
increased.
The
first
isolate
retrieved
from
the
marine
subsurface
was
Desulfovibrio
profundus,
a
sulfate-‐reducing
bacterium
isolated
from
500
m
below
the
seafloor
in
the
Japan
Sea
(Bale
et
al.,
1997).
Since
then,
several
studies
have
focused
on
cultivation
of
microbial
isolates
from
deep
sediment
(Barnes
et
al.,
1998;
Fitchel
et
al.,
2012;
Imachi
et
al.,
2011;
Mikucki
et
al.,
2003;
Miyazaki
et
al.,
2012;
Miyazaki
et
al.,
2014a;
Miyazaki
et
al.,
2014b;
Toffin
et
al.,
2004,
among
others)
and
marine
hydrothermal
vent
fields
(Alain
et
al.,
2002;
Imachi
et
al.,
2008;
Inagaki
et
al.,
2003;
Inagaki
et
al.,
2004;
Reysenbach
et
al.,
2000).
One
bacterium,
Bacillus
rigiliprofundi,
was
isolated
even
deeper
than
marine
sediments,
from
subseafloor
basaltic
crust
(Sylvan
et
al.,
2015)
In
the
terrestrial
realm,
dedicated
scientific
drilling
programs
are
extremely
rare.
Instead,
legacy
mines
have
been
used
as
portals
to
understand
microbial
ecology
in
the
terrestrial
deep
biosphere
(Baker
et
al.,
2003;
Chivian
et
al.,
2008;
Edwards
et
al.,
2006;
Gihring
et
al.,
2006;
Hirayama
et
al.,
2005;
Ino
et
al.,
2016;
Takai
et
al.,
2002;
Takai
et
al.,
2003).
However,
the
vast
majority
of
these
studies
have
utilized
culture-‐independent
methods,
resulting
in
a
grand
total
of
1
terrestrial
isolate
collected
from
a
legacy
mine
deeper
than
100
meters
below
surface:
Sulfurihydrogenibium
subterraneum,
a
member
of
the
Aquificales
(Takai
et
al.,
2003).
In
the
current
study
my
objective
was
to
isolate
new
species
from
the
terrestrial
subsurface
using
specialized
culturing
techniques
that
were
designed
to
target
subsurface-‐adapted
microorganisms.
Members
of
the
phylum
Spirochaetes
are
cosmopolitan,
recently
reported
to
inhabit
environments
even
as
deep
as
2.5
km
below
sea
floor
(Inagaki
et
al.,
2015).
Members
of
the
family
Spirochaetaceae
have
been
isolated
globally
from
anoxic
environments
including
121
lake
sediment,
marine
intertidal
mud,
oilfields,
hot
springs
and,
most
recently,
from
deep-‐
sea
sediment
and
near
marine
hydrothermal
vents
(Imachi
et
al.,
2008;
Miyazaki
et
al.,
2014a,
Leschine
&
Paster,
2010).
I
successfully
isolated
strain
SURF-‐ANA1
T
from
brackish
aquifer
fluids
collected
1,500
meters
below
surface
(mbs)
at
Sanford
Underground
Research
Facility
(SURF)
in
Lead,
South
Dakota
USA.
At
time
of
collection,
fluids
were
~20
o
C
and
were
anoxic
(oxidation
reduction
potential
=
-‐276
mV).
Initial
enrichment
of
the
strain
was
achieved
by
a
continuous-‐flow
bioreactor
approach
after
Imachi
et
al.,
2011.
Subsequent
isolation
was
achieved
by
6
repetitions
of
the
serial
dilution
method.
In
this
study
we
describe
the
isolation
procedure
as
well
as
the
physiological,
morphological
and
genetic
characteristics
of
strain
SURF-‐ANA1
T
.
This
constitutes
the
first
formal
report
of
isolation
of
a
member
in
the
phylum
Spirochaetes
from
the
deep
terrestrial
subsurface.
4.4
Materials
and
Methods
After
12
months
of
incubation,
I
removed
effluent
from
a
continuous
flow
bioreactor
at
USC,
described
above.
DNA
extraction
and
sequencing
of
the
16S
rRNA
gene
revealed
multiple
previously
uncultured
species.
The
most
divergent
clone
sequence
was
88%
similar
to
the
closest
cultured
relative.
This
bacterium
was
targeted
for
isolation
using
serial
dilution
of
batch
cultures.
The
isolation
medium
used
in
this
study
was
designed
to
mimic
as
closely
as
possible
the
geochemical
conditions
of
the
fluid
from
which
the
isolate
was
collected.
The
in
situ
fluid
was
circumneutral
(pH=
7.2)
anoxic,
with
a
reducing
potential
of
-‐276
millivolts.
A
complete
list
of
in
situ
physical
and
geochemical
conditions
can
be
found
in
Osburn,
et
al.
(2014).
This
medium
contained
(l
-‐1
):
0.012g
KCl,
0.03
g
122
MgSO4,
0.0076
g
CaSO4,
0.04g
Na2SO4,
0.0024g
CaCl2),
0.025g
NH4Cl,
0.032g
(NH4)2SO4,
0.045g
KH2PO4,
0.006g
NaF,
0.002g
LiCl,
1
g
HEPES
(as
pH
buffer),
1
g
peptone
and
1
g
yeast
extract,
2
mL
vitamin
solution
and
2
mL
trace
metal
solution
DSMZ
141.
The
primary
enrichment
culture
was
incubated
anaerobically
at
30
o
C.
After
isolation
and
initial
temperature
test
of
the
strain,
all
cultures
were
grown
at
30
o
C
in
100ml
serum
vials
with
50ml
of
anoxic
media
and
a
headspace
of
N2
(100%),
N2/CO2
(80:20
v/v)
or
H2/CO2
(80:20
v/v)
depending
upon
the
carbon
substrate
being
tested.
Medium
was
adjusted
to
pH
7.2
and
vials
were
not
shaken
during
incubation.
Serum
vials
were
sealed
with
blue
butyl
rubber
stoppers
and
crimped
with
aluminum
seals
before
autoclaving.
After
autoclaving,
neutralized
stock
solutions
of
substrates,
including
bicarbonate,
and
reducing
agents
were
added
to
vials
containing
basal
medium
via
filter
sterilization.
Growth
rate
and
substrate
utilization
were
monitored
by
absorbance
at
600
nm
wavelength
on
a
Shimadzu
UV2600
spectrophotometer
(Kyoto,
Japan)
or
by
0.2
µm
filtration
and
DAPI
nucleic
acid
staining
and
visualization
under
a
fluorescence
microscope
(Zeiss
Axiokam
2).
All
substrate
utilization
tests
were
performed
using
exponentially
growing
cells
in
a
0.1%
yeast
extract
medium,
inoculated
at
10%
v/v,
and
maintained
at
40
o
C
for
at
least
1
month
with
periodic
checks
for
growth
(Table
1).
Temperature,
pH
and
NaCl
optima
were
determined
in
basal
medium
containing
0.1%
yeast
extract
and
0.1%
peptone
with
a
headspace
of
H2/CO2
(80:20
v/v).
To
test
temperature
range,
initial
incubations
were
performed
in
triplicate
from
5-‐55
o
C
at
5
degree
increments.
Fastest
123
growth
was
observed
between
35-‐45
o
C,
and
growth
curves
were
performed
again
within
that
temperature
range
at
2
degree
increments.
To
determine
the
pH
range
of
growth,
medium
was
adjusted
to
pH
4.4-‐9.4
at
0.2
pH
unit
intervals
with
1M
stock
solution
of
either
HCl
or
NaOH.
In
media
below
pH
6.0
the
pH
was
buffered
with
sodium
acetate
conjugate
base
solution.
In
media
above
pH
8.0
the
value
was
maintained
with
sodium
sulfide.
The
pH
and
reducing
potential
of
the
medium
were
monitored
every
day
during
growth
using
a
portable
meter
(Myron
L
Company,
Ultrameter
II).
If
the
pH
strayed
>0.1
from
the
initial
value,
it
was
readjusted
to
the
appropriate
value
using
either
sterile
filtered
HCl
or
NaOH.
NaCl
tolerance
was
tested
at
additions
from
0
to
100
g
NaCl
l
-‐1
in
the
basal
medium
(with
0.1%
yeast
extract
and
0.1%
peptone).
Organic
acids
were
measured
using
high
pressure
liquid
chromatography
(HPLC)
on
an
Agilent
1100
series
with
a
Hi-‐Plex
H
7.7
×
300
mm,
8µm
column
according
to
manufacturer’s
recommended
conditions.
Hydrogen
and
carbon
dioxide
were
measured
by
gas
chromatography
(GC)
on
a
Shimadzu
GC-‐2014ATF
headspace
GC
equipped
with
Haysep
80/100
(5m)
and
MS-‐5A
60/80
(2.5m)
molecular
sieve
columns
and
TCD
and
FID
detectors.
Dissolved
gas
concentrations
were
calculated
from
headspace
gas
concentrations
based
on
solubility
constants
of
each
gas
at
the
analytical
temperature
and
pressure
and
comparison
to
standard
gas
mixtures.
Antibiotic
resistance
was
tested
by
supplementing
the
peptone/yeast
extract
medium
with
each
antibiotic
via
filter
sterilization,
after
autoclaving.
Final
concentration
for
all
antibiotics
was
50
µg
ml
-‐1
with
the
exception
of
rifampicin
which
was
added
to
a
final
124
concentration
of
10
µg
ml
-‐1
.
All
antibiotic
tests
were
performed
in
triplicate,
using
exponentially
growing
pre-‐cultures,
inoculated
at
10%
v/v.
Gram-‐staining
was
performed
according
to
Hucker’s
method
(Doetsch,
1981).
Cell
morphology
was
observed
under
a
fluorescence
microscope
(Olympus
BX51F)
with
a
CCD
color
camera
system
(Olympus
DP72).
For
fatty
acid
methyl
ester
(FAME)
analysis,
cells
were
harvested
and
frozen
pellets
were
shipped
on
dry
ice
to
Microbial
ID,
Inc.
(Newark,
Delaware,
USA)
for
FAME
analysis.
Cytochrome
oxidase
activity
was
determined
by
spreading
cell
pellets
on
oxidase
test
paper
(Nissui
Pharmaceutical).
Catalase
activity
was
determined
based
on
O2
bubble
production
in
3
%
(v/v)
H2O2
solution
(Barrow
&
Feltham,
1993).
To
test
for
sporulation,
MnCl2
(1mM
final
concentration)
was
added
to
basal
media
described
above
(modified
from
Tebo
et
al.,
2007).
I
searched
for
spores
using
phase
contrast
and
scanning
electron
microscopes.
Cultures
were
prepared
for
scanning
electron
microscopy
(SEM)
using
a
modified
method
from
Choa
and
Zhang,
2011
and
Sylvan
et
al.,
2015.
Briefly,
cells
were
fixed
in
a
2.5%
formalin
solution
in
sterile
filtered
phosphate
buffered
saline
(PBS).
Fixed
cells
were
sequentially
dehydrated
in
10,
25,
50,
75
and
95%
ethanol
and
critically
point
dried.
Dried
cells
were
gold
coated
and
visualized
on
the
scanning
electron
microscope
(JEOL
JSM-‐7001F-‐LV)
at
the
Center
for
Electron
Microscopy
and
MicroAnalysis
(CEMMA),
University
of
Southern
California.
Total
genomic
DNA
was
extracted
from
cell
pellets
according
Momper
et
al.,
(2015).
PCR
amplification,
cloning
and
sequencing
were
performed
as
described
previously
(Imachi
et
al.,
2011;
Momper
et
al.,
2015).
The
16S
rRNA
gene
sequence
of
the
isolate
was
125
amplified
with
the
primer
pair
8f/1492R.
Purity
was
confirmed
throughout
the
experiments
by
direct
amplification
of
total
genomic
DNA
using
primers
8f
and
1492R.
Sequencing
was
performed
at
the
Genewiz
sequencing
facility
(San
Diego,
California,
USA)
using
the
Sanger
sequencing
technology.
To
determine
phylogenetic
relatedness,
the
16S
rRNA
gene
sequence
of
strain
SURF-‐ANA1
T
was
compared
to
existing
isolates
and
environmental
clones
using
the
NCBI
Basic
Local
Alignment
Search
Tool
(BLAST)
database.
Related
sequences
belonging
to
organisms
within
the
phylum
Spirochaetes
were
collected
for
construction
of
phylogenetic
trees.
Sequences
were
trimmed,
assembled
and
aligned
using
the
MUSCLE
algorithm.
Phylogenetic
trees
were
created
using
Geneious
Pro
version
8.1.5
(Biomatters
Ltd.,
San
Francisco,
CA,
USA).
PhyML
consensus
trees
were
constructed
(Guindon
and
Gascuel,
2003)
using
the
Jukes
Cantor
genetic
distance
model
with
1000
bootstrap
replicates.
4.5
Results
Cells
of
strain
SURF-‐ANA1
T
were
highly
motile
by
characteristic
helical
movement.
Although
some
spiral
filaments
reached
up
to
100
µm
in
length
(Figure
4.1),
cells
were
predominantly
curved
rods
0.25–0.55
x
5–75
µm
(Figure
4.2a).
Spherical
bodies
were
observed
in
late
exponential
phase,
a
tendency
to
form
that
has
been
reported
previously
for
members
of
the
Spirochaetaceae
(Imachi
et
al.,
2008;
Miyazaki
et
al.,
2014a).
In
addition
to
spherical
bodies
formed
in
late
stationary
phase,
distinct
morphologies
were
observed
for
SURF-‐ANA1
T
dependent
upon
the
growth
phase
and
composition
of
the
growth
medium.
In
nutrient
replete
medium,
and
in
exponential
growth
phase
the
majority
of
cells
126
were
rod
shaped,
0.25-‐0.5
x
0.5-‐1.0
µm
(Figure
4.3a).
However,
under
stress
the
morphology
of
the
cells
changed
dramatically
to
canonical
elongated
spiral
helices.
In
organic
carbon-‐deplete
medium,
high
salt
medium,
antibiotic-‐containing
medium,
and
high
manganese
sporulation
medium
strain
SURF-‐ANA1
T
exhibited
elongated
helical
cells
(Figure
4.3b).
Figure
4.2.
Strain
SURF-‐
ANA1
T
a)
under
phase
contrast
microscopy
and
b)
the
same
spiral
filament
after
fluorescence
in
situ
hybridization
(FISH)
staining.
a
b
10 µm
10 µm
127
Figure
4.3.
Scanning
electron
micrographs
of
strain
SURF-‐ANA1
T
under
different
nutrient
and
external
stressors:
a)
nutrient
replete
medium
b)
sporulation
medium
c)
nutrient
deplete
medium
and
d)
spherical
bodies
produced
in
late
stationary
phase.
Strain
SURF-‐ANA1
T
was
Gram-‐stain
negative.
Strain
SURF-‐ANA1
T
was
oxidase-‐
and
catalase-‐negative,
and
was
unable
to
grow
aerobically.
No
growth
was
observed
in
rich
medium
in
the
absence
of
reducing
agents
such
as
sulfide
or
cysteine.
Yeast
extract
stimulated
growth.
Results
for
growth
on
alternative
carbon
sources
are
in
Table
4.2.
a
b
d
c
128
Strain
SURF-‐ANA1
T
grew
at
15–50
o
C,
with
an
optimum
growth
temperature
at
40
o
C.
The
strain
grew
at
pH
4.6-‐9.0,
with
optimum
growth
at
around
pH
7.2-‐
7.4.
Strain
SURF-‐ANA1
T
was
resistant
to
streptomycin
and
was
susceptible
to
ampicillin,
kanamycin,
neomycin
and
tetracycline.
The
strain
showed
slowed
growth
and
stressed
morphology
but
mild
resistance
in
the
presence
of
penicillin,
rifampicin
and
vancomycin.
Polar
lipid
and
FAME
analysis
showed
that
the
strain
contained
the
branched
chain
10-‐methylhexadecanoate
as
the
dominant
fatty
acid
(20.6%).
The
other
major
cellular
fatty
acids
(>5%
of
the
total)
in
order
of
descending
abundance
were:
iso-‐C15
:0,
C18
:
2
dimethyl
acetal
(DMA),
iso-‐C17
:
0,
cyclo-‐C20
:
0
ω6
and
C18
:
0
DMA.
Numerous
other
fatty
acids
were
detected
and
a
complete
list
Table 4.1. Fatty acid composition
of strain SURF-ANA-1
T
percent
abundance
Straight chain
C
14 : 0
0.3
C
15 : 0
0.3
C
16 : 0
3.7
C
18 : 0
2.3
C
14 :1
ω9 --
C
16 : 1
ω9 --
C
16 : 1
ω7 0.8
C
16 : 1
ω11 --
C
17 : 1
ω9 0.5
C
18 : 1
ω7 1.4
C
18 : 1
ω9 0.6
C
16 : 0
DMA 2.3
C
16 : 2
DMA 0.8
C
18 : 0
DMA 6.8
C
18 : 2
DMA 15.9
Branched chain
iso-C
13 : 0
0.2
iso-C
14 : 0
--
iso-C
15 : 0
16.5
iso-C
16 : 0
0.9
iso-C
17 : 0
13
anteiso-C
13 : 0
--
anteiso-C
15 : 0
1.3
anteiso-C
17 : 0
1.9
cylo C
20 : 0
ω6 7.4
10-methyl C
16 : 0
20.6
Fatty acid
129
can
be
found
in
Table
4.1.
A
near-‐complete
16S
rRNA
gene
sequence
(1492
bp)
was
amplified
from
strain
SURF-‐ANA1
T
.
Comparative
nucleotide
sequence
analysis
indicated
that
SURF-‐ANA1
T
was
affiliated
with
the
family
Spirochaetaceae
(Figure
4.4).
It
is
most
closely
related
to
cultured
isolates
Spirochaeta
stenostrepta
(88%
nucleotide
identity)
and
Spirochaeta
caldaria
(87%
nucleotide
identity).
Notably,
it
does
not
relate
most
closely
to
the
genus
Spirochaete
although
it
exhibits
the
canonical
helical
morphology
of
that
genus.
Also
of
note
is
that
strain
SURF-‐ANA1
T
identifies
most
closely
with
isolates
of
Spirochaete
from
terrestrial
hot
springs,
but
very
clearly
falls
outside
that
clade
(Figure
4.4).
130
Figure
4.4.
Phylogenetic
tree
of
the
full
length
16S
rRNA
gene
sequence
of
SURF-‐ANA1.
Phylogenetic
trees
were
created
using
Geneious
Pro
version
8.1.5
(Biomatters
Ltd.,
San
Francisco,
CA,
USA).
PhyML
consensus
trees
were
constructed
(Guindon
and
Gascuel,
2003)
using
the
Jukes
Cantor
genetic
distance
model
with
1000
bootstrap
replicates.
131
4.6
Discussion
The
strain,
SURF-‐ANA1
T
,
isolated
in
this
study
is
the
proposed
type
strain
for
a
new
genus
and
species
on
the
basis
of
16S
rRNA
nucleotide
identity
and
differing
physiological
and
metabolic
characteristics
from
its
closest
cultured
relatives.
The
closest
cultured
relatives
according
to
16S
rRNA
sequence
similarity
are
Spirochaeta
stenostrepta
and
Spirochaeta
caldaria
at
88
and
87%
identity,
respectively.
This
is
well
below
the
widely
accepted
cutoffs
for
species
(97%)
and
genus
(95%)
classification.
Physiological
characteristics
of
strain
SURF-‐ANA1
T
are
compared
to
its
closest
relative
and
other
deep
subsurface
(marine)
isolates
in
Table
4.2.
Strain
SURF-‐ANA1
T
is
distinct
from
closest
characterized
relatives
(S.
caldaria)
and
other
Spirochaetaceae
isolated
from
subsurface
environments
(S.
psychrophila,
E.
thermophila).
Metabolically,
strain
SURF-‐ANA1
T
can
utilize
hydrogen
for
an
electron
donor
and
can
grow
on
acetate,
characteristics
that
were
not
discovered
in
other
strains
(Table
4.2).
Furthermore,
strain
SURF-‐ANA1
T
can
tolerate
a
greater
pH
range
than
reported
for
other
strains
and
optimum
temperature
is
8-‐12
degrees
lower
than
that
for
closest
relative,
S.
caldaria.
Antibiotic
resistance
is
unique
in
strain
SURF-‐ANA1
T
(Table
4.2).
It
shows
susceptibility
to
ampicillin
and
kanamycin
while
relatives
do
not.
Considering
the
antibiotic
resistance,
growth
temperature
optimum,
pH
tolerance
and
unique
isolation
source
of
strain
SURF-‐ANA1
T
we
propose
a
novel
genus
and
species,
type
strain
SURF-‐ANA1
T
as
described
in
the
following
section.
132
Table&4.2.&Differential*phenotypic*characteristics*of*strain*SURF5ANA1
T*
and*its*closest*phylogenetic*relative
(S.#caldaria)*and*type*strains*isolated*from*other*subsurface*environements*(S.#psychrophila,#E.#thermophila)
Characteristic SURF-ANA1 S. caldaria S. psychrophila E. thermophila
Cell size (µm) 0.25–0.55 x 5–75 0.2-0.3 x 15-25 0.25-0.55 x 3.6-15 0.23-0.28 x 15-27
Optimum temperature (°C) 40 48-52 15 50
Optimum pH (range) 7.2-7.4 7.2-7.5 6.8-7.0 7.0
Optimum NaCl concentration (%) 0.25 0.30 3.00
Substrate utilization
Acetate + - - -
Casamino Acids - + - -
Citrate + +
Dextrose - - -
Formate - -
Fructose + + -
Glucose + + -
Lactate + - -
L-arabinose - + + -
Peptone + - -
Sucrose + + -
Xylose + + -
Antibiotic Resistance
Ampicillin - + +
Kanamycin - + -
Neomycin - - + -
Penicillin ± - +
Rifampicin ± + - -
Streptomycin +
Tetrocyclin - - -
Vancomycin ± -
DNA G+C content (mol%) 45.60 39.80 27.10
deep terrestrial freshwater marine subsurface hydrothermal
subsurface hot spring sediment vent chimney
±*indicates*slowed*growth*but*mild*resistance
Isolation source
133
Description
of
Spirosphaera
gen.
nov
Spirosphaera
(Spi.ro.sphae’ra.
L.
mas.
N.
spiro
helix;
L.
fem.
sphaera
sphere;
Spirosphaera
helix
sphere,
referring
to
the
two
major
cellular
shapes).
Description
of
Spirosphaera
subterraneum,
sp.
nov.
Strictly
anaerobic,
motile,
Gram-‐stain-‐negative
organism.
Cells
are
pleomorphic,
demonstrating
rod
morphology
under
nutrient
replete
conditions
and
elongated
helices
under
nutrient
stress..
Motility
occurs
in
helical
cells.
Cells
are
0.25–0.55
x
5–75
µm
with
a
wavelength
of
0.5-‐0.62
µm.
Growth
occurs
at
15-‐50
o
C
with
the
optimal
temperature
for
growth
at
40
o
C.
No
growth
was
observed
below
15
or
above
51
o
C.
Growth
was
observed
between
pH
4.8-‐9.0
and
optimally
at
7.2-‐7.4.
Cells
are
catalase-‐negative
and
weakly
positive
for
cytochrome
oxidase.
Isoprenoid
quinones
are
absent.
Strain
SURF-‐ANA1
T
is
facultatively
chemoautotrophic,
using
hydrogen
as
an
electron
donor
and
carbon
dioxide
as
a
carbon
source.
The
following
substrates
are
utilized
for
carbon
source
or
electron
donor:
yeast
extract,
peptone,
glucose,
sucrose,
lactate,
pyruvate
and
acetate.
The
dominant
fatty
acid
is
branched
chain
10-‐methylhexadecanoate
(20.6%).
The
other
major
cellular
fatty
acids
(>5%
of
the
total)
in
order
of
descending
abundance
were:
iso-‐C15
:0,
C18
:
2
dimethyl
acetal
(DMA),
iso-‐C17
:
0,
cyclo-‐C20
:
0
ω6
and
C18
:
0
DMA.
The
type
strain
is
SURF-‐ANA1
T
isolated
from
ultra-‐deep
subsurface
terrestrial
fluids
in
Lead,
South
Dakota,
USA
(44°21′3″N
103°45′57″W).
134
Acknowledgements
This
work
was
supported
by
the
NASA
Astrobiology
Institute
under
cooperative
agreement
NNA13AA92A.
Many
thanks
to
SURF
scientists
and
personnel
for
providing
access
and
technical
assistance
during
the
collection
of
fluid
samples
in
2013.
Electron
microscopy
images
in
this
article
were
generated
at
the
Center
for
Electron
Microscopy
and
MicroAnalysis
(CEMMA),
University
of
Southern
California.
Special
thanks
to
Casey
Barr
for
help
generating
those
images.
This
study
was
partially
supported
by
the
National
Aeronautics
and
Space
Administration
(NASA),
the
National
Science
Foundation
(NSF),
and
the
Japan
Agency
for
the
Promotion
of
Science
(JSPS).
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Kobayashi,
H.,
Nealson,
K.H.
and
Horikoshi,
K.
(2003).
Sulfurihydrogenibium
subterraneum
gen.
nov.,
sp.
nov.,
from
a
subsurface
hot
aquifer.
Intl
J
Syst
Evol
Microbiol
53,
823-‐827.
141
Tebo,
B.
M.,
Clement,
B.
G.
&
Dick,
G.
J.
(2007).
Biotransformations
of
manganese.
In
Manual
of
Environmental
Microbiology,
3rd
edn.,
1223–1238.
Edited
by
C.
J.
Hurst,
R.
L.
Crawford,
J.
L.
Garland,
D.
A.
Lipson,
A.
L.
Mills
&
L.
D.
Stetzenbach.,
Washington,
DC:
American
Society
for
Microbiology.
Teske,
A.
P.
(2005).
The
deep
subsurface
biosphere
is
alive
and
well.
Trends
in
Microbiol
13,
402-‐404.
Toffin,
L.,
Bidault,
A.,
Pignet,
P.,
Tindall,
B.
J.,
Slobodkin,
A.,
Kato,
C.
and
Prieur,
D.
(2004).
Shewanella
profunda
sp.
nov.,
isolated
from
deep
marine
sediment
of
the
Nankai
Trough.
Int
J
Syst
Evol
Microbiol
54,
1943–1949.
Whitman,
W.B.
and
Coleman,
D.C.
(1998)
Prokaryotes:
the
unseen
majority.
Proc
Natl
Acad
Sci
12:
6578-‐6583.
Yarza,
P.,
Spröer,
C.,
Swiderski,
J.,
Mrotzek,
N.,
Spring,
S.,
Tindall,
B.
J.
and
other
authors
(2013).
Sequencing
orphan
species
initiative
(SOS):
Filling
the
gaps
in
the
16S
rRNA
gene
sequence
database
for
all
species
with
validly
published
names.
Syst
Appl
Microbiol
36,
69-‐73.
142
CHAPTER
5
Concluding
remarks
By
Lily
Momper
143
5.1
Concluding
remarks
The
SURF
laboratory
is
a
portal
into
the
deep
subsurface
biosphere
that
reveals
an
array
of
geochemically,
taxonomically,
and
metabolically
diverse
microbial
communities.
Sanford
Underground
Research
Facility
provides
an
excellent
preexisting
infrastructure
for
delving
into
the
biosphere
of
the
continental
subsurface.
Candidate
phyla
and
members
of
the
microbial
‘dark
matter’
are
abundant
at
all
sampled
levels
within
SURF.
Previous
molecular
biological
studies
at
SURF
revealed
57
phylotypes
that
could
not
be
identified
on
even
the
phylum
level
(Rastogi
et
al.,
2009;
Rastogi
et
al.,
2010).
There
is
a
dearth
of
knowledge
and
lack
of
cultured
representatives
of
anaerobic,
subsurface-‐adapted
microorganisms.
Sequencing
and
culturing
efforts
at
SURF
could
help
to
elucidate
the
biogeochemical
and
metabolic
roles
of
these
uncharacterized
phyla.
Of
particular
interest
in
this
study
are
the
metabolic
capabilities
within
the
26
reconstructed
genomes
of
microbial
dark
matter.
Specifically,
we
discovered
genes
involved
in
nitrate
reduction,
sulfate
reduction
and
autotrophic
carbon
fixation
in
two
candidate
phyla
genomes.
A
brief
search
of
the
most
closely
related
bacteria
indicated
that
these
phyla
are
globally
distributed
in
marine
and
terrestrial
subsurface
habitats,
however
no
information
on
their
metabolic
ability
was
available
until
the
present
study.
Within
those
genomes,
according
to
16S
rRNA
phylogeny,
4
were
loosely
associated
with
the
phylum
Omnitrophica
(Figure
3.4).
However,
Bin_7_2
was
highly
divergent
and
not
closely
associated
with
any
phylum,
branching
somewhat
near
the
Verrucomicrobia
(of
the
PVC
superphylum).
Based
on
both
16S
rRNA
and
amino
acid
comparisons,
we
propose
a
new
candidate
phylum,
within
the
PVC
superphylum.
144
On
a
global
scale,
our
analysis
has
shown
that
terrestrial
deep
subsurface
biomes
tend
to
be
dominated
by
the
phylum
Firmicutes,
and
in
fact
the
shift
from
phylum
Proteobacteria
to
Firmicutes
appears
to
be
the
This
study
also
confirms
that
the
phylum
Actinobacteria
is
a
major
component
of
subseafloor
sediment
microbial
communities.
Sedimentary
organic
matter
can
survive
early
diagenesis
and
remain
available
for
fermentation
and
other
heterotrophic
metabolisms
by
deep
subsurface
microorganisms,
including
the
Actinobacteria
and
Chloroflexi
(Dunne
et
al.,
2007;
Orsi
et
al.,
2013;
Inagaki
et
al.,
2015).
The
relatively
minor
proportions
of
Deltaproteobacteria
and
other
putative
SRBs
in
the
marine
subsurface
support
recent
suggestions
that
sulfate
reduction
may
not
be
quite
as
dominant
in
marine
sediments
as
previously
thought
(Orsi
et
al.,
2013).
Recent
estimates
place
the
total
mass
of
living
cells
in
marine
sediments
at
1.5–22
petagrams
carbon
(Pg
C)
(Hinrichs
and
Inagaki,
2012;
Kallmeyer
et
al.,
2012).
Similarly,
a
recent
review
evaluated
the
mass
of
the
terrestrial
subsurface
biosphere
at
14–135
Pg
C
(McMahon
and
Parnell,
2013),
approximately
10×
that
in
marine
sediments.
The
vastness,
variability,
and
carbon
content
of
the
terrestrial
subsurface
underscores
the
need
for
more
studies
and
highlights
the
relative
dearth
of
publicly
available
data
from
terrestrial
subsurface
sites.
References
Dunne,
J.P.,
Sarmiento,
J.L.
and
Gnanadesikan,
A.,
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synthesis
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the
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and
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Global
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Hinrichs,
K.-‐U.,
and
Inagaki,
F.
(2012).
Biogeochemistry.
Downsizing
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Science
338,
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Adhikari,
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D.
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and
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S.
(2012).
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G.D.
and
Biddle,
J.F.,
2013.
Gene
expression
in
the
deep
biosphere.
Nature,
499:
205-‐208.
Rastogi,
G.,
Osman,
S.,
Kukkadapu,
R.,
Engelhard,
M.,
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Andersen,
G.
L.,
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K.
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G.,
Stetler,
L.
D.,
Peyton,
B.
M.,
and
Sani,
R.
K.
(2009).
Molecular
analysis
of
prokaryotic
diversity
in
the
deep
subsurface
of
the
former
Homestake
gold
mine,
South
Dakota,
USA.
J
Microbiol.
47,
371–384.
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Abstract (if available)
Abstract
Earth’s deep subsurface biosphere (DSB) supports an enormous store of microbial life. Although difficult to access and sample, both marine and terrestrial subsurface environments have recently been probed through ocean drilling programs and mine exploration. The advent of high throughput sequencing has enabled molecular (DNA) analyses of the microbial communities inhabiting the DSB that has resulted in a huge expansion of the publicly available sequences from those global sites. In an effort to understand better the microbial ecology of deep terrestrial habitats, we examined bacterial and archaeal diversity in the Sanford Underground Research Facility (SURF) in the former Homestake Gold Mine, South Dakota, USA. We extracted whole genomic DNA from both deeply (~1.5 milometers below surface) circulating groundwater and host rock. Pyrotag DNA sequencing of the 16S rRNA gene reveals diverse communities of putative chemolithoautotrophs, aerobic and anaerobic heterotrophs, numerous candidate phyla, and a unique rock-associated microbial assemblage. We then gathered 15 similarly sequenced global terrestrial and marine subsurface samples and compared them against the SURF sequences and each other. Major biogeographic trends were discovered. Among all terrestrial samples, the phylum Firmicutes is dominant (average ~33% of total sequences). We also performed metagenomic shotgun sequencing on the same SURF fluids (1.5 kmbs). We reconstructed over 90 genomic bins from metagenomic sequences, enabling investigation of common metabolic pathways in terrestrial subsurface microbes. Sulfate and nitrate/nitrite reduction were the most commonly recovered energy metabolisms among genomes. The most abundant autotrophic carbon fixation pathway across all genomic bins was the reductive acetyl-CoA pathway, presumably due to the anoxic conditions in the fluids and the relative energetic efficiency of this pathway. More than a quarter (24) of our genomes belong to bacterial phyla without any cultivated members
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Asset Metadata
Creator
Momper, Lily M.
(author)
Core Title
Microbial ecology in the deep terrestrial biosphere: a geochemical, metagenomic and culture-based approach
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Marine Biology and Biological Oceanography
Publication Date
08/03/2016
Defense Date
05/11/2016
Publisher
University of Southern California
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Tag
metagenomics,microbial dark matter,microbial ecology,OAI-PMH Harvest,subsurface biosphere
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Electronically uploaded by the author
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Advisor
Amend, Jan (
committee chair
), El-Naggar, Moh (
committee member
), Heidelberg, John (
committee member
)
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lmmomper@gmail.com,momper@usc.edu
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Tags
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microbial dark matter
microbial ecology
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