ASTROBIOLOGY
Volume 10, Number 2, 2010
ª Mary Ann Liebert, Inc.
DOI: 10.1089=ast.2009.0378
Novel Microbial Diversity Retrieved by Autonomous
Robotic Exploration of the World’s Deepest
Vertical Phreatic Sinkhole
Jason W. Sahl,1 Nathaniel Fairfield,2 J. Kirk Harris,3 David Wettergreen,2
William C. Stone,4 and John R. Spear1
Abstract
The deep phreatic thermal explorer (DEPTHX) is an autonomous underwater vehicle designed to navigate an
unexplored environment, generate high-resolution three-dimensional (3-D) maps, collect biological samples based
on an autonomous sampling decision, and return to its origin. In the spring of 2007, DEPTHX was deployed in
Zacatón, a deep (*318 m), limestone, phreatic sinkhole (cenote) in northeastern Mexico. As DEPTHX descended,
it generated a 3-D map based on the processing of range data from 54 onboard sonars. The vehicle collected water
column samples and wall biomat samples throughout the depth profile of the cenote. Post-expedition sample
analysis via comparative analysis of 16S rRNA gene sequences revealed a wealth of microbial diversity. Traditional Sanger gene sequencing combined with a barcoded-amplicon pyrosequencing approach revealed novel,
phylum-level lineages from the domains Bacteria and Archaea; in addition, several novel subphylum lineages
were also identified. Overall, DEPTHX successfully navigated and mapped Zacatón, and collected biological
samples based on an autonomous decision, which revealed novel microbial diversity in a previously unexplored
environment. Key Words: Autonomous—Robotics—Microbial diversity—16S rRNA. Astrobiology 10, 201–213.
1. Introduction
I
n the search for extraterrestrial microbial life, there is
emergent interest in the field of exploratory autonomous
robotics (Diaz-Calderon et al., 2007). To search for microbial
life on planetary bodies that contain liquid water, such as
the jovian moon Europa (Squyres et al., 1983), a robotic vehicle would be required to navigate through space, land on
the moon’s surface, penetrate the ice sheet, explore the alien
ocean for microscopic life, and transmit its findings back to
Earth. In an effort to develop tools for autonomous navigation and exploration of unexplored water bodies, a vehicle
known as the deep phreatic thermal explorer (DEPTHX) was
designed (Krajick, 2007; Stone, 2007). The deployment goals of
DEPTHX were to map and navigate autonomously through
an unexplored environment and collect biological samples
through autonomous decisions based on a set of programmed
criteria.
Located in northeastern Mexico (Fig. 1A), Zacatón, the
deepest water-filled vertical sinkhole (cenote) in the world
(Gary and Sharp, 2006), provided the site for deployment of the
DEPTHX vehicle (Fig. 1B). Zacatón (Fig. 1C) is fed by subsurface hydrothermal input from nearby volcanic activity, which
provides a spatially and temporally stable temperature profile
of *308C throughout the year, and a sulfur input that supplies
the surrounding area its name: Rancho la Azufrosa (azufrosa is
Spanish for sulfurous). The cenote is thought to have been
created as hydrothermal water supplied from nearby volcanic
activity was acidified with carbon dioxide or hydrogen sulfide,
or both, to dissolve the host limestone as it moved upward
through the subsurface (Gary et al., 2008). In a preliminary
equipment test for DEPTHX, a sonar sonde dropped into
Zacatón in May 2005 revealed that the cenote was at least
290 m deep and potentially much deeper. Zacatón was chosen
as the test site for the deployment of DEPTHX because the
cenote was deep, likely mappable, and unexplored.
2. Methods and Materials
2.1. Site description
Sistema Zacatón is located in a localized karst region
of northeastern Mexico (228590 35.10@N, 988090 55.96@W). The
1
Colorado School of Mines, Environmental Science and Engineering, Golden, Colorado.
Carnegie Mellon Robotics Institute, Pittsburgh, Pennsylvania.
3
Pediatrics, University of Colorado, Denver, Colorado.
4
Stone Aerospace, Del Valle, Texas.
2
201
202
SAHL ET AL.
sor). Fifty-four narrow-beam long-range sonar sensors allow
the vehicle to detect subsurface features and generate maps in
3-D. The vehicle is equipped with a science package for microbiological sampling (see below).
The System Executive (i.e., the multiprocessor autonomous
‘‘brain’’ of the robot) allocates out steps in a dive specification
based on the state of the system and its location, which is
estimated using a combination of dead-reckoning and sonarbased localization. In the DEPTHX system, there is no onboard planning; rather, the Executive chooses actions from a
primary or cascading set of contingency plans. Dive specifications are composed into mission plan files that encode, in an
interpreted command language, the sequence of behaviors to
execute. These behaviors may initiate sensing, navigate to
locations, trigger on conditions, perform maneuvers, or acquire samples. The primary plan file is backed up by a number
of contingency plans that are triggered on conditions encoded
in the currently executing plan description.
2.3. Vehicle navigation and SLAM
FIG. 1. A map showing the location of Zacatón (A); the
deep phreatic thermal explorer (DEPTHX) (B); an aerial view
of Zacatón (C); biomats in Zacatón from depths of 11 m (D),
273 m (E), 10 m (F).
geological formations of this system are part of a larger system of travertine-capped sinkholes, dry karst cave passages,
and dolines in the vicinity (Gary et al., 2008). Zacatón is a
105 m diameter, *318 m deep sinkhole with a 17 m deep
limestone rim=cliff band above the water. Two meter-thick,
round, floating reed islands, known as zacates, float and are
blown around on the surface of the cenote. In direct sunlight,
the clear blue water can turn milky-white as sulfur clouds
develop in the top 5 m of the surface water and disappear
when the Sun sets.
2.2. Vehicle engineering
A detailed description of the DEPTHX vehicle has been
reported previously (Stone, 2007). In short, DEPTHX is approximately 1.5 m tall and 1.9 m in length and width and is
powered by lithium-ion batteries. The vehicle has a full suite
of underwater navigation sensors, including a Honeywell
HG2001 Inertial Measurement Unit (IMU), two Paroscientific
Digiquartz depth sensors, and an RDI Navigator 600 Doppler
Velocity Log (DVL). There is also a conductivity, temperature,
and depth sensor for measuring the speed of sound for corrections to sonar-based measurements. The specifications for
the inertial navigation system are roll=pitch 0.28 2s, yaw 0.48
2s, for the DVL velocities 0.3 cm=s 1s, and for the depth
sensors 0.01% of full range (10 cm for our 1000 m rated sen-
The high-grade IMU had negligible yaw drift over time,
and under nominal conditions the onboard sensors provided
dead-reckoned navigation on the order of 0.5% of distance
traveled. Under certain conditions [e.g., when the vehicle
gets too close to a wall (<*2 m)], the DVL cannot provide
velocity measurements, at which point the quality of the
dead-reckoned solution degrades significantly (*10% of distance traveled). To estimate velocity, the DVL requires that
each of its four sonars receive a reflected signal from either
the water column or a surface. At times of transition, however, as when moving away from a surface into open water
or when the orientation of the surface deflects the return
away, the unit can ‘‘lose lock’’ and become unable to estimate
velocity. As a result, dead-reckoning accuracy is reduced
until velocity lock is re-established. Underwater, there is
no absolute position information (like GPS), so this deadreckoning position error is cumulative.
The vehicle can bound this position error by performing its
own localization estimate (using the onboard sonars) relative to a map. However, exploration implies that there is no
map available, thus the vehicle has to build a map with new
data and simultaneously localize itself using that map; this is
known as the simultaneous localization and mapping (SLAM)
problem. The approach we describe below builds highly accurate 3-D maps; but, due to the coupled nature of localization
and mapping, it is susceptible to unavoidable error (or drift)
over long distances, except when the vehicle can close loops
and return to previously mapped areas. We developed a realtime probabilistic SLAM algorithm that provides an estimate
of the vehicle trajectory as well as a 3-D metric map of the
environment [see Fairfield et al. (2006, 2007) for a more complete description of our SLAM approach].
For SLAM, the DEPTHX vehicle has an array of 54 narrowbeam (24 at 300 kHz, 1.758 beam width; and 32 at 600 kHz, 28
beam width with ranges of 200 and 100 m, respectively) sonars that provide a constellation of range measurements
around the vehicle at about 1 Hz. This array is in the shape of
three great circles, a configuration that was arrived at after
studying the suitability of various sonar geometries for 3-D
mapping (Fairfield et al., 2006). The sonars have long ranges
(100 or 200 m), and the accuracy of the range measurements
MICROBIAL DIVERSITY IN ZACATÓN
is approximately 10 cm; however, the low resolution, update
rate, and point density make the mapping problem underwater significantly more difficult than it is with ranging
sensors, like a laser scanner, that provide fast, accurate, highresolution ranges in typical air-filled environments.
2.4. Sample collection and preparation
Wall biomat samples were collected with an onboard springloaded sampling arm and stored in a stainless-steel borer
with a sealable door; once a single wall sample was taken, the
door closed on the collection borer. Immediately after the
vehicle surfaced, wall biomat samples were ejected into 2 ml
cryovials and frozen in liquid nitrogen. Between sampling
trips, the borer was cleaned with a dilute bleach solution (3%)
and thoroughly rinsed with sterile, double-distilled water.
Prior to water column sample collection, DEPTHX ran its
pump for 2 min, which allowed for the sample collection
tubing to be completely flushed with in situ water. Samples
were then collected into sterile urinary collection bags (2 L
capacity). Upon surfacing, the collection bags were connected
to a peristaltic pump, and water was filtered directly onto
0.2 mm polyethersulfone filters (Pall Corporation, East Hills,
NY) housed in a stainless steel high-pressure filter manifold
(Millipore, Billerica, MA). One quarter of each filter was cut
with flame-sterilized scissors and frozen in liquid nitrogen.
New collection bags were used for each sampling trip.
2.5. Water and wall biomat chemistry
Values for pH, temperature, dissolved oxygen, and specific
conductivity were recorded with a modified Hydrotech HT6
meter (Hydrotech, Hutto, TX) mounted on the DEPTHX vehicle. Sulfide concentrations were calculated with a modified
laboratory sensor mounted on the vehicle. For shallow water
column samples, concentrations of ammonia and hydrogen
sulfide were manually recorded with CHEMetrics kits (CHEMetrics, Calverton, VA) and a field photometer. Prior to filtering for microbiological analysis, water for the DEPTHX
sampling missions was collected for laboratory water analyte
analysis. Fifteen milliliters of water was syringe filtered
(0.45 mm) and acidified (pH < 2) for analysis of metals and
major cations with inductively coupled plasma atomic emission spectroscopy. An additional 15 ml of water was filtered for
analysis of major anions with ion chromatography, and approximately 200 ml of water was collected in glass amber vials
for analysis of total organic carbon and alkalinity. All samples
were stored at 48C until analyzed. A Spearman correlation test
(Spearman, 1904) of specific water chemistry species with
depth was calculated with XLSTAT (www.XLSTAT.com).
DEPTHX had the ability to collect five individual 2 L water
samples per dive, and site-dependant replicate sampling was
possible based on the generated map by SLAM.
For wall biomat compositional analysis, between 17 and
125 mg of mat material was dried for 24 h at 558C. The dried
mat material was digested in 100% nitric acid on a hot plate
for 24 hours. The digest was centrifuged, the supernatant was
diluted 1:10 in Milli-Q water (Millipore, Billerica, MA), and
syringe filtered (0.45 mm). Samples were then run with inductively coupled plasma atomic emission spectroscopy for
compositional analysis. Aqueous concentrations were converted to a percentage of each element, considering the mass
of the entire extraction.
203
2.6. DNA extraction, PCR, cloning, sequencing
DNA was extracted from wall samples and filter sections
with the Mobio Powersoil Kit (Mobio, Carlsbad, CA) and
eluted in a volume of 30 ml TE. Polymerase chain reaction
(PCR) of the 16S rRNA gene was performed on all samples
by using universal forward primer 515F (50 -GTG CCA GCM
GCC GCG GTA A-30 ) (Lane, 1991), bacterial-specific forward
primer 8F (50 -AGA GTT TGA TCC TGG CTC AG-30 ) (Lane,
1991), and archaeal-specific forward primers 4Fa (50 -TCC
GGT TGA TCC TGC CRG-30 ) (Hershberger et al., 1996) and
21Fa (50 -TTC CGG TTG ATC CYG CCG GA-30 ) (Reysenbach
and Pace, 1995). Reverse primers included the nominally
universal 1391R (50 -GAC GGG CGG TGW GTR CA-30 ) and
1492R (50 -GGT TAC CTT GTT ACG ACT T-30 ) (Lane, 1991).
PCRs were performed with Promega PCR Master Mix (Promega, Madison, WI) and a primer concentration of 1 mM. PCR
thermal cycling conditions consisted of an initial denaturation
at 948C for 2 min, followed by 30 cycles of denaturation (948C
for 1 min), annealing (50–558C for 1 min) and elongation (728C
for 1 min). PCR amplicons were gel purified (Millipore, Billerica, MA), TOPO TA (Invitrogen, Carlsbad, CA) cloned and
sequenced on a MegaBACE 1000 dye-terminating sequencer.
2.7. Sanger sequence analysis
Sequences were processed by PHRED and PHRAP with
use of the XplorSeq interface (Frank, 2008). Multi-direction
reads were performed on each sample, and contiguous reads
were compiled. Sequences were screened for chimeras with
Mallard (Ashelford et al., 2006), and non-chimeric sequences
were aligned with the NAST aligner (DeSantis et al., 2006a).
Sequences were inserted into the ARB dendogram by parsimony insertion with use of the Lanemask filter (Lane, 1991). A
global sequence comparison was made by building a distance matrix between the query and target sequence in ARB;
the sequence distance was then converted to a percent relatedness.
2.8. Phylogenetic analyses
Sequences that did not group with known phyla in ARB
with use of the current Greengenes (DeSantis et al., 2006b)
database release were analyzed in a phylogenetic tree that
contained representative sequences from currently recognized bacterial phyla and candidate phyla. Sequences were
exported unaligned and subsequently aligned with the Infernal aligner (Nawrocki et al., 2009). Covariance model (Eddy
and Durbin, 1994) files were created with a seed alignment
downloaded from the comparative RNA web site (Cannone
and Subramanian, 2002), and a consensus secondary structure
was calculated with RNAalifold (Hofacker, 2003). Alignment
confidence estimates were calculated with Infernal, with use
of the–p flag, for each residue of each sequence. Columns with
high alignment confidence values (*100%) were considered
to be conserved, and a filter mask, which masks out variable
columns, was created with custom Perl script. The conserved
column alignment mask, along with all aligned sequences,
was imported back into ARB. Manual adjustments were made
to the alignment, and sequences were exported with use of
the custom conserved filter mask. For the Archaea, a phylogenetic tree was inferred via the RAxML web server (Stamatakis et al., 2008). For the Bacteria, the number of taxa
204
analyzed (*36,000) exceeded the capabilities of the
maximum-likelihood algorithm, and a neighbor-joining
method, which uses a minimum evolution principle (Price
et al., 2009), was used to infer the tree topology.
2.9. Pyrosequencing and analysis
Polymerase chain reactions were conducted with Promega
PCR Master Mix and the bacterial-specific primers 8F and
338R (50 -CAT GCT GCC TCC CGT AGG AGT-30 ) (Amann
et al., 1990); these priming sites span the hypervariable V1 and
V2 regions of the 16S rRNA gene (Van de Peer et al., 1996).
The reverse primer contained a Hamming barcode, which
allowed for the pooling of multiple samples in a single highthroughput sequencing run (Hamady et al., 2008). PCR amplicons were normalized, quantified on a NanoDrop ND-1000
(NanoDrop, Wilmington, DE), diluted in molecular-grade
water to a concentration of *0.01 ng=ml, and sequenced on
a 454 GS FLX machine (454 Life Sciences, Branford, CT).
Sequences were sorted by barcode and quality filtered to remove sequences with ambiguous characters (Ns) and sequences that were of an insufficient length (<2 standard
deviations of the mean) with custom Java script. The barcode
and primer sequence were trimmed prior to analysis.
Sequences were aligned with use of the ribosomal database
project (RDP) pyrosequencing pipeline (Cole et al., 2009),
which uses the Infernal aligner in conjunction with a consensus secondary structure alignment. The pyrosequencing
pipeline was also used to cluster sequences based on a furthest
neighbor algorithm; operational taxonomic units (OTUs)
were then formed based on a 97% sequence identity threshold. A representative sequence from each OTU was aligned
with the NAST aligner and classified with the Greengenes
classifier (DeSantis et al., 2006b). Taxonomic classifications
were made according to the Hugenholtz taxonomic system
(DeSantis et al., 2006b).
2.10. qPCR
The total number of 16S rRNA gene copies in extracted
samples was estimated with quantitative PCR (qPCR) on a
Lightcycler 480 (Roche). Degenerate primers for conserved
regions flanking the hypervariable V4 region of the 16S rRNA
gene (Raué et al., 1988) were forward primer (50 -AYT GGG
YDT AAA GNG-30 ) and reverse primer (50 -TAC NVG GGT
ATC TAA TCC-30 ) (Cole et al., 2009); these primers cover
>94.5% of long (>1200 nucleotides), high-quality sequences
in the RDP database, using the probe match tool (Cole et al.,
2009). Primers were also designed specifically for the C2
Crenarchaeota; these include forward primer 303F (50 -GGG
AGC CCG GAG ATG-30 ) and reverse primer 1069R (50 -ACC
TCA CGG CAC GAA-30 ) (Dr. Bradley Stevenson, unpublished). A calibration curve was calculated by using seven
standards, in triplicate, and purified and serial diluted amplicons. Gradient PCR revealed an optimum annealing temperature for each primer of 488C.
The reverse primer 494R (50 -CTG AGG GTA CCG TCA
ATA-30 ) was also designed for the putative candidate phylum
Azufrosa group 1 (AG1). The primer was compared against
the RDP database with the probe match tool, which only returned six positive matches; all these sequences grouped with
AG1 and only two were >1200 nucleotides in length. This
reverse primer was paired with the bacterial primer 8F to
SAHL ET AL.
determine the distribution of this clade throughout the depth
profile of Zacatón.
2.11. Species richness estimators
Parametric species richness estimates were calculated with
methods reported previously ( Jeon et al., 2006). Six different
parametric models [single-point Poisson, negative binomial,
inverse Gaussian, lognormal, Pareto, mixture of two exponentials (M.E.)] were used to fit the observed data based on
the frequency of sequences at the designated percent identity
level. The best performing model estimate was chosen based
on a biologically relevant standard error [<(estimate=2)], the
highest goodness-of-fit value, and a high right truncation
point. For comparison, the non-parametric species richness
estimates Chao1 (Chao, 1987) and ACE (Chao and Lee, 1992)
were calculated with EstimateS (Colwell, 2005). Coverage
estimates were calculated by dividing the number of observed
OTUs by the best parametric estimate.
2.12. Secondary structure analysis
A comparative secondary structure analysis was performed by aligning a consensus sequence from AG1 against a
16S rRNA sequence from E. coli (accession #J01695). The sequence from AG1 was then manually mapped onto the wellestablished E. coli 16S rRNA secondary structure (Woese et al.,
1980) to identify regions of conservation and divergence.
2.13. Nucleotide accession numbers
The 1588 Sanger sequences generated in this study were
submitted to Genbank under accession numbers FJ484010–
FJ485598. Pyrosequences were submitted to the Genbank
short read archive under accession number SRA008128.3.
3. Results
3.1. Vehicle navigation and SLAM
With SLAM, we built a three-dimensional 0.5 m resolution
metric map of Zacatón. Without ground truth, it is difficult to
make strong statements about localization and map accuracy, but the maps showed consistent findings independently
produced across many dives. Our method was able to use
sparse, noisy, low-resolution, and low-rate sonar data to build
3-D maps, localize, run SLAM on board the DEPTHX vehicle,
and generate the first ever maps of this large-scale, complex,
natural formation.
3.2. Water chemistry
As DEPTHX maneuvered through the cenote, the vehicle
recorded thousands of water chemistry values with the Hydrotech sensor. The data reveal an incredibly homogeneous
water body, with nearly identical values for pH and temperature throughout the depth profile of the cenote (Table 1).
Major ion analyses confirmed this trend, with uniform concentrations of sulfate, nitrate, chloride, and sodium. However,
statistically significant (P < 0.03) negative Spearman correlations (r < !0.7) of zinc, iron, and manganese with depth were
observed. No clear chemistry boundaries were detectable; for
example, even the surface water sample showed reducing
conditions, with no clear oxic=anoxic boundary or chemocline
observed. Sulfate was the most abundant common terminal
BDL
0.97
0
0
6.32
0.15
3.37
0.07
0.44
1.20
0.01
37.17
0
0.73
8.20
7.81
2.44
BDL
1.01
0
0
6.24
0.15
3.11
0.08
0.49
1.38
0.01
13.31
0
0.73
8.27
7.44
5.28
BDL, below detection limit; NM, not measured.
DO, dissolved oxygen; TOC, total organic carbon.
30.03
6.58
!168
0
0.15
767
1.63
0.07
30.10
6.67
!175
0
0.16
759
0.01
0.05
Temp (8C)
Ph
Eh (Mv)
DO (mM)
TOC (mM)
Spec Cond (mS=cm)
Fe - T (mM)
NH4þ (mM)
Major anions (mM)
PO34 !
CI!
NO3!
Br
HCO3!
SO24
Major cations (mM)
Ca
K
Mg
Na
H2S
Trace elements (mM)
Zn
Ni
Mn
S cations (meq)
S anions (meq)
Charge imbalance (%)
22 m
Surface
Parameter
7.80
0
0.73
0.23
0.13
1.70
2.79
0.08
0.44
1.27
NM
0
0.98
0
0
6.28
0.15
30.02
6.73
!177
0
0.18
767
0.52
NM
30 m
12.39
0
1.09
0.25
0.13
5.96
3.06
0.08
0.46
1.30
NM
0
0.97
0
0
6.20
0.15
30.03
6.72
!185
0
0.16
767
1.04
NM
60 m
14.84
0
1.46
0.24
0.13
3.23
2.80
0.08
0.46
1.31
NM
0
0.95
0
0
6.17
0.15
30.00
6.72
!200
0
0.18
767
0.38
NM
90 m
Water
4.13
0
0.55
0.26
0.13
7.65
2.94
0.08
0.49
1.72
NM
BDL
0.97
0
0
6.17
0.15
30.04
6.52
!274
0
0.24
760
10.20
NM
150 m
7.04
0
0.55
0.24
0.13
3.94
2.98
0.08
0.47
1.21
NM
BDL
0.96
0
0
6.30
0.16
30.10
6.54
!282
0
0.14
761
10.20
NM
198 m
1.84
0
0.55
0.25
0.13
4.58
2.96
0.08
0.49
1.32
NM
0
0.98
0
0
6.29
0.16
30.10
6.53
!282
0
0.14
761
0.20
NM
273 m
Table 1. Chemical Constituents in Zacatón
1.07
0
0.36
0.28
0.13
7.43
2.71
0.09
0.47
2.30
NM
BDL
0.95
0
0
6.32
0.14
30.06
6.50
!263
0
0.39
761
0.08
NM
285 m
0.0019%
0.0001%
0.0027%
6.57%
0.07%
0.08%
0.04%
NM
4m
0.0118%
0.0009%
0.0052%
6.05%
0.10%
0.10%
0.11%
NM
32 m
0.0120%
0.0011%
0.0110%
0.14%
0.03%
0.40%
0.02%
NM
114 m
Wall biomat
0.0180%
0.0026%
0.0049%
6.45%
0.05%
0.07%
0.05%
NM
273 m
206
SAHL ET AL.
Table 2. Microbial Diversity and Coverage Estimates for Samples in Zacatón
Matrix
Sample
depth (m)
Sequences
OTUs
(97%)
1
4
7
10
20
32
58
100
114
195
273
2490
3680
2697
3306
3948
2705
1459
1761
1898
2119
2338
584
1209
718
1444
1753
901
923
655
1129
1056
1186
0
17
273
2662
1810
2396
140
204
392
Best parametric
estimate
coverage
(%)
ACE
Chao 1
Shannon
index
Simpson
index (1=D)
128)
309)
267)
259)
270)
507)
754)
430)
475)
460)
395)
38
30
27
31
33
22
17
22
23
25
26
1187
2659
1689
3228
4149
2280
2991
1642
3025
2295
2686
1187
2659
1700
3237
4003
2293
3011
1654
3038
2303
2696
5.1
6.23
4.32
6.51
6.94
5.64
6.54
5.45
6.72
6.52
6.69
41.88
147.94
6.83
129.95
578.9
55.9
623.09
55.55
712.97
348.34
607.78
563 (se 134)
674 (se 94)
1697 (se 248)
25
30
23
297
513
951
305
527
962
2.29
3.24
3.69
5.51
10.5
12.66
mat
1552
4062
2656
4670
5389
4011
5482
3022
4984
4305
4485
(se
(se
(se
(se
(se
(se
(se
(se
(se
(se
(se
water
se, standard error.
electron acceptor, which, combined with lineages associated
with sulfate reduction (see below), suggests that this may be
an important mode of metabolism throughout the depth
profile of the cenote.
Inductively coupled plasma atomic emission spectroscopy
data were only obtained for four wall samples in Zacatón
(Table 1). Only zinc and nickel showed a correlation (r ¼ 1.0)
of concentration with depth (Table 1). Approximately 8% of
the biomat mass consisted of extractable metals; the remaining mass of the mat is likely non-digestible minerals.
Calcium comprised the highest mass percent of the mats.
3.3. 16S rRNA gene sequence analysis
To sample the microbiology of Zacatón, the DEPTHX
vehicle was equipped with five sterile collection bags for
aqueous samples and a spring-loaded extendable arm designed to sample the extensive biomats that coat the walls of
the cenote (Fig. 1D, 1E, 1F).
Culture-independent gene sequence analysis (Pace, 1997;
Spear et al., 2005) allowed for the analysis of microbial community structure in 3-D space, all in the context of the geochemistry of the cenote. Visualization of the mats by an
onboard camera on DEPTHX revealed thick, complex photosynthetic mats in the shallow subsurface (<30 m) (Fig. 1D,
1F) and delicate, striated mats 273 m deep in the cenote
(Fig. 1E). The initial survey of 16S rRNA gene sequences resulted in 1588 Sanger sequence reads that guided a more
comprehensive sampling of the microbial community with a
barcoded-amplicon pyrosequencing approach (Hamady et al.,
2008).
Results of the 16S rRNA gene sequence analysis of the filterable fraction of the water column suggest that the community is dominated by a few phylogenetic types (phylotypes)
such as Arcobacter spp. and Sulfuromales spp., which group
with the Epsilonproteobacteria. Sequences related to these
Arcobacter and Sulfuromales phylotypes have been isolated
from other sulfidic caves (Macalady et al., 2008; Porter and
Engel, 2008) and are thought to be important microbes in the
cycling of carbon, nitrogen, and sulfurous compounds
(Campbell et al., 2006). Even within the Epsilonproteobacteria,
clear differences were observed with depth; for example, deep
in the water column (273 m), the epsilon sequences were
phylogenetically related (98% global identity) to Sulfuricurvum
kujiense, a chemoautotrophic microbe capable of anaerobic
sulfide oxidation (Kodama and Watanabe, 2004).
Other sequences from the water column grouped with the
Gammaproteobacteria and were related (96% global identity)
to sequences amplified from deep subsurface mine fluids in
South Africa (Gihring et al., 2006). In general, the water column microbial diversity was low (Table 2); in fact, out of
*7500 full- and partial-length gene sequences screened from
a depth profile of the water column, only 850 OTUs were
compiled with a 97% sequence identity threshold. Even with
low observed diversity and dominance of a few phylotypes,
clear trends in phylotype distribution and diversity were
observed in the depth profile of the water column (Table 2).
In contrast, sequences obtained from the walls of the cenote
showed high microbial diversity within the bacterial and archaeal domains (Figures 2 and 3); for example, out of 105
bacterial phyla currently recognized by the Hugenholtz taxonomic system (DeSantis et al., 2006b), 74% were found in
the mats of Zacatón (more than 10 representative sequences
from each clade) based on *39,000 pyrosequences, as determined by the Greengenes classifier. Furthermore, a clear
depth profile of bacterial phylotypes was observed, with novel cyanobacterial sequences (95% global identity with nearest neighbor) dominant in shallow regions of the cenote and
sequences associated with sulfate reduction (Deltaproteobacteria, Thermodesulfovibrio) dominant below the photic
zone (>40 m) (Fig. 4). While some Deltaproteobacterial sequences showed relatedness to cultured taxa, such as Syntrophobacter pfennigii (93%) (Wallrabenstein et al., 1995) and
Desulfomonile tiedjei (95%) (DeWeerd et al., 1990), many
Deltaproteobacterial sequences (*45%) in deeper samples
(>100 m) belonged to 15 OTUs that were not closely related
(<95%) to Deltaproteobacterial sequences in public databases
from environmental samples. Sequences associated with
MICROBIAL DIVERSITY IN ZACATÓN
207
FIG. 2. Neighbor-joining phylogenetic tree of the bacterial domain. Numbers in parentheses indicate accession numbers.
Dark wedges indicate phyla that are represented in either biomat or water column samples.
photosynthetic anoxygenic sulfide oxidizers (Chlorobi) were
found throughout the depth profile of the cenote, even in
zones well below the photic zone (Fig. 4). At depths of >100m,
the community was dominated by sequences that grouped
with Deltaproteobacteria and Chloroflexi (Fig. 4). Epsilonproteobacterial sequences, which are ubiquitous in the water
column, were almost entirely absent from all wall biomat
samples, regardless of depth.
Archaeal diversity was dominated (58% of archaeal sequences) by the C2 Crenarchaeota (Fig. 4), which has been
recognized as a monophyletic group by some taxonomic
curators (DeLong and Pace, 2001; Robertson et al., 2005;
DeSantis et al., 2006b); this clade is also associated with the socalled miscellaneous Crenarchaeota group (Nunoura et al.,
2005). Sequences that group with this clade have been amplified from deep subsurface gold mines (Nunoura et al.,
2005), geothermal wells (Rogers and Amend, 2005), deep
marine anoxic basins ( Jeon et al., 2008), and a hydrothermal
vent in the Mariana Trough (unpublished data).
Clone libraries constructed with universal primers suggest
that C2 sequences were prevalent at depth in the cenote and in
lower abundance, or absent altogether, in shallow regions.
208
SAHL ET AL.
FIG. 3. Maximum-likelihood phylogenetic tree of the archaeal domain, including sequences from both wall biomat and
water column samples. Numbers in parentheses following accession numbers indicate the number of sequences associated
with that phylotype in this environment.
qPCR confirmed this trend, with an increased number of C2
16S rRNA gene copy numbers, compared to the total 16S
rRNA gene copy numbers, with sample depth (Fig. 5). Two
nucleotide mismatches were found between the V4 forward
primer and sequences of the C2 Crenarchaeota on the 30 end.
To allow for the potential of the primer not annealing to C2
gene templates, the percentage of C2 gene copy numbers
was calculated by comparison of the C2 gene copy numbers to the V4 gene copy numbers plus the C2 gene copy
numbers. This calculation is considered even more conservative, given the fact that bacteria generally contain more rrn
operon copies than archaea (Klappenbach et al., 2001; Acinas
et al., 2004).
Additional archaeal sequences grouped with known methanogens (Methanomicrobia) as well as anaerobic methane
oxidizers (ANME-1). Singleton, non-chimeric, sequences were
observed on the archaeal tree (Fig. 3) that showed low global
identity to nearest neighbors (*85% relatedness). Additional
sequencing with archaeal-specific primers may help to further
define these novel lineages.
3.4. Azufrosa candidate phyla
Initial phylogenetic analyses using ARB parsimony insertion suggested that some phylotypes did not group with
known bacterial phyla at a phylogenetic distance (*0.20)
consistent with phylum-level delineations (Schloss and Handelsman, 2005) (Fig. 2). In total, three novel candidate phyla
were identified, although two of these phyla were only represented by two sequences from Zacatón mats greater than
1250 nucleotides in length. Additional phylogenetic analyses
by maximum likelihood (Stamatakis, 2006) and neighborjoining algorithms (Price et al., 2009) support the presence of
these novel lineages.
Azufrosa group 1 (AG1) was the most represented novel
candidate phylum identified. Sequences from deep aquifers
MICROBIAL DIVERSITY IN ZACATÓN
209
FIG. 4. Vertical profile of Zacatón and a heat map showing the relative distribution of selected phyla at various depths.
Abbreviations include Proteob., Proteobacteria (a ¼ alpha, d ¼ delta, g ¼ gamma, e ¼ epsilon); Bactero., Bacteroidetes; Cyanoba., Cyanobacteria; Nitrosp., Nitrospirae; Firmicu., Firmicutes; Spiroch., Spirochaetes; Acidoba., Acidobacteria; Chlorob.,
Chlorobi; Chlorof., Chloroflexi. ‘‘Others’’ represents the summed remainder of bacterial phyla.
in Japan (Shimizu et al., 2006) and New York (Bakermans
and Madsen, 2002) are also affiliated with AG1 (Fig. 3). A
secondary structure analysis of AG1 sequences was compared
to the established secondary structure of E. coli, and a truncation of Helix 17 was identified (Fig. 6), which has also been
seen in archaea and other bacteria (Raué et al., 1988). The PCR
primer 494R spans the unique truncation in the helix, which
allows for high specificity to this group.
All sequences that group with AG1 were amplified from
deep samples in Zacatón (>82m). A qPCR reaction was run
to determine the relative abundance and distribution of this
clade throughout the cenote water column; however, no clear
trend in abundance was observed with depth (Fig. 5). The
distribution of these sequences, based on these results, does
not appear to be confined to deep environments.
3.5. Diversity and species richness
A positive and significant (P ¼ 0.001) Spearman correlation
(r ¼ 0.83) of biomat microbial diversity (1=D) with depth was
identified (Table 2). Parametric species richness estimators
were higher than non-parametric estimators for all samples,
which is consistent with other studies (Hong et al., 2006; Jeon
et al., 2006).
Species richness coverage estimates ranged from 17–38%.
However, richness estimates have been shown to scale with
sequencing effort (Morales et al., 2009), which suggests that
additional sequencing may reveal these estimates to be an
overestimation of the calculated community coverage.
FIG. 5. Relative abundance of C2 Crenarchaeota and
Azufrosa group 1 (AG1) gene copies in the depth profile of
Zacatón compared to the total gene copy number.
3.6. Autonomous sampling
One of the two primary missions of DEPTHX was to collect
a biological sample based on an autonomous decision.
210
SAHL ET AL.
from major cations, anions, or total organic carbon; these results correspond well with the homogeneous behavior of
other abiotic parameters.
4.2. Microbial diversity
FIG. 6. Helix 17 of the bacterial secondary structure model.
The structure from E. coli is in gray, and the truncated helix
from AG1 is in black. The bracketed section indicates the
position of primer 494R.
DEPTHX was programmed to detect chemical interfaces; we
hypothesized that these interfaces provide sources of chemical
disequilibrium and were the most likely location of varied
microbial diversity and metabolism. However, due to the
homogeneous nature of detectable chemical parameters in
Zacatón, DEPTHX was unable to locate a clear chemical interface in the water column for biological sampling. In fact,
only the sulfide concentration appeared to vary significantly
with depth. Based on this observation, a voltage threshold
that corresponded to an in situ sulfide concentration was
programmed into DEPTHX. In one sampling mission,
DEPTHX detected the programmed threshold, approached
the wall of the cenote, collected a wall biomat sample, and
returned to its origin based on the SLAM method. Although a
sulfide calibration was never successfully calculated, the autonomous decision to sample was successfully made by
DEPTHX.
4. Discussion
4.1. Water chemistry
The DEPTHX vehicle was programmed to detect chemical
interfaces. Currently within the field of astrobiology, the
presence of known electron donors and acceptors may indicate that the energetics are amenable to microbial metabolism
(Hand et al., 2007; Hoehler et al., 2007).
However, an analysis of water chemistry parameters in
Zacatón revealed an extremely homogeneous water body.
This homogeneity is likely caused by convective mixing as
hydrothermal groundwater from the deep subsurface moves
upward through the water column. The hydrothermal source
is likely deep, as no discernable differences in temperature
were observed between the surface and at 300 m depth.
Three-dimensional maps of the cenote also failed to reveal
any physical inflow to the water body, which suggests that
the opening is too small (<1 m) to be discerned by the low
resolution of the narrow-beam sonars on DEPTHX.
Dissolved oxygen was not detected at any depth in Zacatón
(Table 1). Even just below the surface, oxygen appeared to
be depleted, perhaps due to biotic or abiotic (Luthy et al.,
2000) sulfide oxidation. Elemental sulfur clouds, which
emerge in full sunlight in the top 5 m of the water column, are
likely formed from incomplete phototrophic sulfide oxidation
(Amend et al., 2004), due to oxygen-limited conditions (van
den Ende and van Gemerden, 2003). In the depth profile of the
cenote, no significant trends in concentration were observed
The geochemistry and morphology of Zacatón select for a
unique and diverse microbial community. A comparison of
microbial communities between Zacatón and other hydrothermal travertine systems (Breitbart et al., 2008), as well as
freshwater sulfurous lakes (Casamayor et al., 2000), showed
that the microbial communities in Zacatón are significantly
different than these comparable systems.
Initial Sanger sequencing of wall biomat samples revealed
a diverse microbial community. Subsequent directed pyrosequencing provided a higher-resolution analysis of the microbial community structure in Zacatón. This method has
been used previously to identify the low-abundance microbial phylotypes in an environment, which constitute the
‘‘rare biosphere’’ (Sogin et al., 2006).
Overall, pyrosequencing of wall biomat DNA revealed
higher microbial diversity than Sanger sequencing. Although
this is likely a result of the greater sequencing effort, results of
a recent study suggest that shorter sequences reveal higher
microbial diversity than longer sequences due to more efficient amplification (Huber et al., 2009). In this study, longer
Sanger sequences were used for accurate phylogenetic analysis and to anchor shorter pyrosequences on phylogenetic
trees, while shorter sequences were used for assessments of
microbial diversity and distribution. Such an approach may
take full advantage of the benefits and limitations of short and
long gene sequences.
A positive correlation of biomat microbial diversity with
depth was observed in the depth profile of Zacatón. Higher
microbial diversity with depth has also been observed in other
anoxic systems (Madrid et al., 2001) and may be attributed to
more varied metabolisms below the photic zone.
4.3. 16S rRNA gene sequence analysis
Water column samples showed low microbial diversity,
with Epsilonproteobacteria and Chlorobi phylotypes dominating the clone libraries (Fig. 4). Both these groups have been
associated with sulfide oxidation (Bergstein and Cavari, 1983;
Wagner et al., 1998); their presence, combined with elemental sulfur clouds that develop in sunlight, suggests that these
microbes may oxidize sulfide in Zacatón as well. Microbes
typically associated with oxygenic photosynthesis, such as
cyanobacteria and green algae, were absent from water column clone libraries. Epsilonproteobacteria sequences dominated water column samples and were absent or in low
abundance in wall biomat samples; cultivated members of
related Epsilonproteobacteria are motile (Miller et al., 2007;
Ho et al., 2008), which suggests that the Epsilonproteobacteria
in Zacatón prefer an unattached lifestyle.
Shallow biomat samples in the cenote contained sequences
from known phototrophic organisms (e.g., Cyanobacteria)
and photosynthetic sulfide oxidizers (e.g., Chlorobi). However, even deeper samples (>100 m) contained sequences that
group with Chlorobi. Microorganisms that group with
Chlorobi have been shown to oxidize sulfide in the presence
of geothermal radiation (Beatty et al., 2005), which may help
explain the presence of sequences associated with obligate
MICROBIAL DIVERSITY IN ZACATÓN
photosynthetic microbes 273 m below the surface in this hydrothermally fed system (Fig. 4).
Archaeal sequences in both biomat and water column
samples were dominated by the C2 Crenarchaeota clade.
In general, the majority of Crenarchaeota genomes contain
genetic machinery needed for sulfur metabolism (Amend
et al., 2004), although no closely related genome to the C2
Crenarchaeota has currently been sequenced. 16S rRNA gene
sequencing and qPCR revealed the composition and distribution of this clade throughout the depth profile of Zacatón.
The increase in C2 sequence abundance with depth (Fig. 5)
suggests that in the photic zone these archaea are outcompeted, perhaps by organisms that metabolize photosynthate
exuded by anoxygenic phototrophs (Medina-Sanchez and
Villar-Argaiz, 2006). The ecological role of these Archaea
cannot be determined through phylogenetic analyses, although the types of environments where C2 have previously
been observed may help to identify samples of significant C2
abundance, which can provide targets for exploratory metagenomics.
Comparative 16S rRNA gene sequence analysis revealed
the presence of three well-supported, novel candidate phyla.
In cenote Zacatón, two of these phyla are only represented
by two sequences, although a comparative molecular analysis
of four adjacent cenotes has revealed additional sequences
that group with these phyla (unpublished data). Although the
presence of 16S rRNA gene sequences from novel lineages
does not reveal their ecological function, their identification
may represent a fertile ground for bioprospecting for novel
proteins, hydrogenases, and lipids, for example. Metagenomic analyses of samples containing significant percentages
of novel microbes may allow for the assembly and interpretation of genomes (Kunin et al., 2008) from microbes that
represent phylum-level novelty (Hugenholtz and Kyrpides,
2009).
Deeper samples in Zacatón well below the photic zone may
be more representative of an astrobiological community than
shallow samples that receive full or partial sunlight. For example, sequences amplified from deep in the water column
(273 m) that show high relatedness to chemoautotrophic, anaerobic sulfide oxidizers suggest that these areas may be independent of photosynthetically derived carbon. This type of
metabolism may be applicable to deep, dark, extraterrestrial
aqueous systems with a hydrothermal feed source.
4.4. Autonomous robotic exploration
During the course of the field campaign, DEPTHX was able
to implement SLAM, detect a programmed chemical interface, sample both the water column and wall biomat, and
return to its origin, all based on decisions executed on board
without any human guidance. The sampling and subsequent
molecular analysis of diver-inaccessible samples, with correlated spatial and geochemical information, have allowed for
a more thorough understanding of microbial diversity and
distribution in hydrothermal, phreatic sinkholes.
As robotic technology advances, the integration of the
autonomous sampling and maneuvering methods discussed
in this study with advanced onboard chemistry and molecular analyses tools will enable the development of a vehicle
capable of searching for microbial life on planetary bodies
that contain liquid water, such as Europa.
211
Acknowledgments
The authors would like to graciously thank Alejandro Dávila for access to Rancho la Azufrosa and the entire DEPTHX
team including George Kantor, Antonio and Nora Fregoso,
John Kerr, Marcus and Robin Gary, Dom Jonak, and the
Southwest Research Institute. Additional thanks to the Pace
Lab at the University of Colorado for their assistance operating the MegaBACE 1000 and to the Consortium for Comparative Genomics located at the University of Colorado
Denver for operating the 454 GS FLX machine. Custom Java
script to decode barcodes was written by Erik Sahl and the
custom Perl infernal script was written by Chuck PepeRanney. The authors would also like to thank Dr. Bradley
Stevenson for providing the C2 primer set and Dr. Laura Beer
for her valuable comments. Funding for this project was
provided by a NASA ASTEP grant NNG04GC09G.
Abbreviations
AG1, Azufrosa Group 1; DEPTHX, deep phreatic thermal
explorer; DVL, Doppler Velocity Log; IMU, Inertial Measurement Unit; OTUs, operational taxonomic units; PCR,
polymerase chain reaction; qPCR, quantitative PCR; RDP,
ribosomal database project; SLAM, simultaneous localization
and mapping.
References
Acinas, S.G., Marcelino, L.A., Klepac-Ceraj, V., and Polz, M.F.
(2004) Divergence and redundancy of 16S rRNA sequences in
genomes with multiple rrn operons. J. Bacteriol. 186:2629–2635.
Amann, R., Binder, B.J., Olson, R.J., Chisholm, S.W., Devereux, R.,
and Stahl, D.A. (1990) Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed
microbial populations. Appl. Environ. Microbiol. 56:1919–1925.
Amend, J.P., Rogers, K.L., and Meyer-Dombard. D.R. (2004)
Microbially mediated sulfur-redox: energetics in marine hydrothermal vent systems. In Sulfur Biogeochemistry Past and
Future, GSA Special Paper 379, edited by J.P. Amend, K.J. Edwards, and T.W. Lyons, Geological Society of America,
Boulder, CO, pp 17–34.
Ashelford, K.E., Chuzhanova, N.A., Fry, J.C., Jones, A.J., and
Weightman, A.J. (2006) New screening software shows that
most recent large 16S rRNA gene clone libraries contain chimeras. Appl. Environ. Microbiol. 72:5734–5741.
Bakermans, C. and Madsen, E.L. (2002) Diversity of 16S rDNA
and naphthalene dioxygenase genes from coal-tar-wastecontaminated. Microb. Ecol. 44:95–106.
Beatty, J.T., Overmann, J., Lince, M.T., Manske, A.K., Lang, A.S.,
Blankenship, R.E., Van Dover, C.L., Martinson, T.A., and
Plumley, F.G. (2005) An obligately photosynthetic bacterial
anaerobe from a deep-sea hydrothermal vent. Proc. Natl. Acad.
Sci. U.S.A. 102:9306–9310.
Bergstein, T. and Cavari, B.Z. (1983) Sulfide utilization by the
photosynthetic bacterium Chlorobium phaeobacteroides. Hydrobiologia 106:241–246.
Breitbart, M., Hoare, A., Nitti, A., Siefert, J., Haynes, M., Dinsdale, E.A., Edwards, R.A., Souza, V., Rohwer, F., and Hollander, D. (2008) Metagenomic and stable isotopic analyses of
modern freshwater microbialites in Cuatro Cienegas, Mexico.
Environ. Microbiol. 11:16–34.
Campbell, B.J., Engel, A.S., Porter, M.L., and Takai, K. (2006) The
versatile e-proteobacteria: key players in sulphidic habitats.
Nat. Rev. Microbiol. 4:458–468.
212
Cannone, J.J. and Subramanian, S. (2002) The comparative RNA
web (CRW) site: an online database of comparative sequence
and structure information for ribosomal, intron, and other
RNAs. BMC Bioinformatics 3:2.
Casamayor, E.O., Schafer, H., Baneras, L., Pedros-Alio, C., and
Muyzer, G. (2000) Identification of and spatio-temporal differences between microbial assemblages from two neighboring
sulfurous lakes: comparison by microscopy and denaturing
gradient gel electrophoresis. Appl. Environ. Microbiol. 66:499–
508.
Chao, A. (1987) Estimating the population size for capturerecapture data with unequal catchability. Biometrics 43:783–
791.
Chao, A. and Lee, S.M. (1992) Estimating the number of classes
via sample coverage. J. Am. Stat. Assoc. 87:210–217.
Cole, J.R., Wang, Q., Cardenas, E., Fish, J., Chai, B., Farris, R.J.,
Kulam-Syed-Mohideen, A.S., McGarrell, D.M., Marsh, T.,
Garrity, G.M., and Tiedje, J.M. (2009) The Ribosomal Database
Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37:D141–D145.
Colwell, R.K. (2005) EstimateS: Statistical estimation of species
richness and shared species from samples, Version 8. User’s
guide and application available online at http:==purl.oclc.org=
estimates.
DeLong, E.F. and Pace, N.R. (2001) Environmental diversity of
Bacteria and Archaea. Syst. Biol. 50:470–478.
DeSantis, T.Z., Hugenholtz, P., Keller, K., Brodie, E.L., Larsen,
N., Piceno, Y.M., Phan, R., and Andersen, G.L. (2006a) NAST:
a multiple sequence alignment server for comparative analysis
of 16S rRNA genes. Nucleic Acids Res. 34, doi:10.1093=nar=
gkl244.
DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie,
E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., and Andersen,
G.L. (2006b) Greengenes, a chimera-checked 16S rRNA gene
database and workbench compatible with ARB. Appl. Environ.
Microbiol. 72:5069–5072.
DeWeerd, K.A., Mandelco, L., Tanner, R.S., Woese, C., and
Suflita, J.M. (1990) Desulfomonile tiedjei gen. nov. and sp. nov.,
a novel anaerobic, dehalogenating, sulfate-reducing bacterium. Arch. Microbiol. 154:23–30.
Diaz-Calderon, A., Backes, P.G., and Bonitz, R.G. (2007) An
autonomous robotic scooping approach for planetary sample
acquisition. In IEEE=RSJ International Conference on Intelligent
Robots and Systems (IROS), Vol. 10, doi:10.1109=IROS.2007
.4398966.
Eddy, S.R. and Durbin, R. (1994) RNA sequence analysis using
covariance models. Nucleic Acids Res. 22:2079–2088.
Fairfield, N., Kantor, G.A., and Wettergreen, D. (2006) Towards
particle filter SLAM with three dimensional evidence grids in
a flooded subterranean environment. In IEEE Internal Conference on Robotics and Automation (ICRA).
Fairfield, N., Kantor, G.A., and Wettergreen, D. (2007) Real-time
SLAM with octree evidence grids for exploration in underwater tunnels. Journal of Field Robotics 24:3–21.
Frank, D.N. (2008) XplorSeq: a software environment for integrated management and phylogenetic analysis of metagenomic
sequence data. BMC Bioinformatics 9, doi:10.1186=1471-21059-420.
Gary, M.O. and Sharp, J.M., Jr. (2006) Volcanogenic karstification
of Sistema Zacatón, Mexico. In Perspectives on Karst Geomorphology, Hydrology, and Geochemistry—A Tribute Volume to Derek
C. Ford and William B. White, edited by R.S. Harmon and C.M.
Wicks, GSA Special Paper 404, Geological Society of America,
Boulder, CO, pp 79–89.
SAHL ET AL.
Gary, M.O., Fairfield, N., Stone, W.C., Wettergreen, D., Kantor,
G.A., and Sharp, J.M., Jr. (2008) 3D mapping and characterization of Sistema Zacatón from DEPTHX (DEep Phreatic
THermal eXplorer). In Proceedings of KARST08: 11th Sinkhole
conference ASCE, pp 1–11.
Gihring, T.M., Moser, D.P., Lin, L.-H., Davidson, M., Onstott,
T.C., Morgan, L., Milleson, M., Kieft, T.L., Trimarco, E.,
Balkwill, D.L., and Dollhopf, M.E. (2006) The distribution of
microbial taxa in the subsurface water of the Kalahari Shield,
South Africa. Geomicrobiol. J. 23:415–430.
Hamady, M., Walker, J.J., Harris, J.K., Gold, N.J., and Knight, R.
(2008) Error-correcting barcoded primers for pyrosequencing
hundreds of samples in multiplex. Nat. Methods 5:235–237.
Hand, K.P., Carlson, R.W., and Chyba, C.F. (2007) Energy, chemical disequilibrium, and geological constraints on Europa.
Astrobiology 7:1006–1022.
Hershberger, K.L., Barns, S.M., Reysenbach, A.-L., Dawson, S.C.,
and Pace, N.R. (1996) Wide diversity of Crenarchaeota. Nature
384:420.
Ho, H.T.K., Lipman, L.J.A., Wosten, M.S.M., and van Asten,
A.J.A.M. (2008) Arcobacter spp. possess two very short flagellins of which FlaA is essential for motility. FEMS Immunol.
Med. Microbiol. 53:85–95.
Hoehler, T.M., Amend, J.P., and Shock, E.L. (2007) A ‘‘follow the
energy’’ approach for astrobiology. Astrobiology 7:819–823.
Hofacker, I.L. (2003) Vienna RNA secondary structure server.
Nucleic Acids Res. 31:3429–3431.
Hong, S.-H., Bunge, J., Jeon, S.-O., and Epstein, S.S. (2006) Predicting microbial species richness. Proc. Natl. Acad. Sci. U.S.A.
103:117–122.
Huber, J.A., Morrison, H.G., Huse, S.M., Neal, P.R., Sogin, M.L.,
and Welch, D.B.M. (2009) Effect of PCR amplicon size on assessments of clone library microbial diversity and community
structure. Environ. Microbiol. 11:1292–1302.
Hugenholtz, P. and Kyrpides, N.C. (2009) A changing of the
guard. Environ. Microbiol. 11:551–553.
Jeon, S.-O., Bunge, J., Stoeck, T., Barger, K.J.-A., Hong, S.-H., and
Epstein, S.S. (2006) Synthetic statistical approach reveals a
high degree of richness of microbial eukaryotes in an anoxic
water column. Appl. Environ. Microbiol. 72:6578–6583.
Jeon, S.-O., Ahn, T.-S., and Hong, S.-H. (2008) A novel archaeal
group in the phylum Crenarchaeota found unexpectedly in an
eukaryotic survey in the Cariaco basin. J. Microbiol. 46:34–39.
Klappenbach, J.A., Saxman, P.R., Cole, J.R., and Schmidt, T.M.
(2001) rrndb: the ribosomal RNA operon copy number database. Nucleic Acids Res. 29:181–184.
Kodama, Y. and Watanabe, K. (2004) Sulfuricurvum kujiense
gen. nov., sp. nov., a facultatively anaerobic, chemolithoautotrophic, sulfur-oxidizing bacterium isolated from an underground crude-oil storage cavity. Int. J. Syst. Evol. Microbiol.
54:2297–2300.
Krajick, K. (2007) Robot seeks new life—and new funding—in
the abyss of Zacatón. Science 315:322–324.
Kunin, V., Copeland, A.C., Lapidus, A., Mavromatis, K., and
Hugenholtz, P. (2008) A bioinformatics guide to metagenomics. Microbiol. Mol. Biol. Rev. 72:557–578.
Lane, D.J. (1991) 16S=23S rRNA sequencing. In Nucleic Acid
Techniques in Bacterial Systematics, edited by E. Stackebrandt
and M. Goodfellow, John Wiley & Sons, Chichester, UK.
Luthy, L., Fritz, M., and Bachofen, R. (2000) In situ determination
of sulfide turnover rates in a meromictic alpine lake. Appl.
Environ. Microbiol. 66:712–717.
Macalady, J.L., Dattagupta, S., Schaperdoth, I., Jones, D.S.,
Druschel, G.K., and Eastman, D. (2008) Niche differentiation
MICROBIAL DIVERSITY IN ZACATÓN
among sulfur-oxidizing bacterial populations in cave waters.
ISME J. 2:590–601.
Madrid, V.M., Taylor, G.T., Scranton, M.I., and Chistoserdov,
A.Y. (2001) Phylogenetic diversity of bacterial and archaeal
communities in the anoxic zone of the Cariaco Basin. Appl.
Environ. Microbiol. 67:1663–1674.
Medina-Sanchez, J.M. and Villar-Argaiz, M. (2006) Solar
radiation–nutrient interaction enhances the resource and predation algal control on bacterioplankton: a short-term experimental study. Limnol. Oceanogr. 51:913–924.
Miller, W.G., Parker, C.T., Rubenfield, M., Mendz, G.L., Wosten,
M.M., Ussery, D.W., Stolz, J.F., Binnewies, T.T., Hallin, P.F.,
Wang, G., Malek, J.A., Rogosin, A., Stanker, L.H., and Mandrell, R.E. (2007) The complete genome sequence and analysis
of the epsilonproteobacterium Arcobacter butzleri. PLoS ONE
26:e1358.
Morales, S.E., Cosart, T.F., Johnson, J.V., and Holben, W.E. (2009)
Extensive phylogenetic analysis of a soil bacterial community
illustrates extreme taxon evenness and the effects of amplicon
length, degree of coverage, and DNA fractionation on classification and ecological parameters. Appl. Environ. Microbiol.
75:668–675.
Nawrocki, E.P., Kolbe, D.L., and Eddy, S.R. (2009) Infernal 1.0:
inference of RNA alignments. Bioinformatics 25:1335–1337.
Nunoura, T., Hirayama, H., Takami, H., Oida, H., Nishi, S., Shimamura, S., Suzuki, Y. Inagaki, F., Takai, K., Nealson, K.H.,
and Horikoshi, K. (2005) Genetic and functional properties of
uncultivated thermophilic crenarchaeotes from a subsurface
gold mine as revealed by analysis of genome fragments. Environ. Microbiol. 7:1967–1984.
Pace, N.R. (1997) A molecular view of microbial diversity and
the biosphere. Science 276:734–740.
Porter, M.L. and Engel, A.S. (2008) Diversity of uncultured epsilonproteobacteria from terrestrial sulfidic caves and springs.
Appl. Environ. Microbiol. 74:4973–4977.
Price, M.N., Dehal, P.S., and Arkin, A.P. (2009) FastTree: computing large minimum evolution trees with profiles instead of
a distance matrix. Mol. Biol. Evol. 26:1641–1650.
Raué, H.A., Klootwijk, J., and Musters, W. (1988) Evolutionary
conservation of structure and function of high molecular
weight ribosomal RNA. Prog. Biophys. Mol. Biol. 51:77–129.
Reysenbach, A.-L. and Pace, N.R. (1995) Protocol 16. Reliable
amplification of hyperthermophilic archaeal 16S rRNA genes
by the polymerase chain reaction. In Thermophiles. Archaea: A
Laboratory Manual 3, Cold Spring Harbor Laboratory Press,
New York, 101–107.
Robertson, C.E., Harris, J., Spear, J.R., and Pace, N.R. (2005)
Phylogenetic diversity and ecology of environmental archaea.
Curr. Opin. Microbiol. 8:638–642.
Rogers, K.L. and Amend, J.P. (2005) Archaeal diversity and
geochemical energy yields in a geothermal well on Vulcano
Island, Italy. Geobiology 3:319–332.
Schloss, P.D. and Handelsman, J. (2005) Introducing DOTUR, a
computer program for defining operational taxonomic units
and estimating species richness. Appl. Environ. Microbiol. 71:
1501–1506.
Shimizu, S., Akiyama, M., Ishijima, Y., Hama, K., Kunimaru, T.,
and Naganuma, T. (2006) Molecular characterization of
213
microbial communities in fault-bordered aquifers in the
Miocene formation of northernmost Japan. Geobiology 4:203–
213.
Sogin, M.L., Morrison, H.G., Huber, J.A., Welch, D.M.,
Huse, S.M., Neal, P.R., Arrieta, J.M., and Herndl, G.J.
(2006) Microbial diversity in the deep sea and the underexplored ‘‘rare biosphere.’’ Proc. Natl. Acad. Sci. U.S.A.
103:12115–12120.
Spear, J.R., Walker, J.J., McCollom, T.M., and Pace, N.R. (2005)
Hydrogen and bioenergetics in the Yellowstone geothermal
ecosystem. Proc. Natl. Acad. Sci. U.S.A. 102:2555–2560.
Spearman, C.E. (1904) The proof and measurement of association between two things. Am. J. Psychol. 15:72–101.
Squyres, S.W., Reynolds, R.T., Cassen, P.M., and Peale, S.J.
(1983) Liquid water and active resurfacing on Europa. Nature
301:225–226.
Stamatakis, A. (2006) RAxML-VI-HPC: maximum likelihoodbased phylogenetic analyses with thousands of taxa and mixed
models. Bioinformatics 22:2688–2690.
Stamatakis, A., Hoover, P., and Rougemont, J. (2008) A rapid
bootstrap algorithm for the RAxML web servers. Syst. Biol.
57:758–771.
Stone, W.C. (2007) Design and deployment of a 3D autonomous
subterranean submarine exploration vehicle [Proceedings
#UUST07]. In Conference on Unmanned, Untethered Submersable
Technology, Durham, NH, August 20–22.
Van de Peer, Y., Chappelle, S., and de Wachter, R. (1996) A
quantitative map of nucleotide substitution rates in bacterial
rRNA. Nucleic Acids Res. 24:3381–3391.
van den Ende, F.P. and van Gemerden, H. (2003) Sulfide oxidation under oxygen limitation by Thiobacillus thioparus isolated from a marine microbial mat. FEMS Microbiol. Ecol.
13:69–77.
Wagner, M., Roger, A.J., Flax, J.L., Brusseau, G.A., and Stahl,
D.A. (1998) Phylogeny of dissimilatory sulfite reductases
supports an early origin of sulfate respiration. J. Bacteriol.
180:2975–2982.
Wallrabenstein, C., Hauschild, E., and Schink, B. (1995) Syntrophobacter pfennigii sp. nov., new syntrophically propionateoxidizing anaerobe growing in pure culture with propionate
and sulfate. Arch. Microbiol. 5:346–352.
Woese, C., Magrum, L.J., Gupta, R., Siegel, R.B., Stahl, D.A.,
Kop, J., Crawford, N., Brosius, J., Gutell, R., Hogan, J.J., and
Noller H.F. (1980) Secondary structure model for bacterial 16S
ribosomal RNA: phylogenetic, enzymatic and chemical evidence. Nucleic Acids Res. 8:2275–2293.
Address correspondence to:
John R. Spear
Colorado School of Mines
Environmental Science and Engineering
Golden, CO 80401
E-mail: jspear@mines.edu
Submitted 22 April 2009
Accepted 28 December 2009