RESEARCH ARTICLE
Neotropical Andes hot springs harbor diverse and distinct
planktonic microbial communities
pez2,3, Laura C. Bohorquez1,2, Jose
R. Bustos1,2, Carolina
Luisa Delgado-Serrano1,2, Gina Lo
2,3
1,2
1,2
2,3
sar Osorio-Forero , Howard Junca , Sandra Baena & Marıa M. Zambrano1,2
Rubiano , Ce
n CorpoGen, Bogot
Molecular Genetics & Microbial Ecology, Corporacio
a, DC, Colombia; 2Colombian Center for Genomics and Bioinformatics
3
of Extreme Environments – GeBiX, Bogota, DC, Colombia; and Unidad de Saneamiento y Biotecnologıa Ambiental, Departamento de Biologıa,
Pontificia Universidad Javeriana, Bogota, DC, Colombia
1
Correspondence: Marıa Mercedes
n CorpoGen, Carrera 5
Zambrano, Corporacio
No. 66A-34, 110231, Bogota DC, Colombia.
Tel.: (+57) 1 8050106 ext 111;
fax: (+57) 1 3484607;
e-mail: [email protected]
Received 12 December 2013; revised 7
March 2014; accepted 21 March 2014. Final
version published online 29 April 2014.
DOI: 10.1111/1574-6941.12333
MICROBIOLOGY ECOLOGY
Editor: Tillmann Lueders
Keywords
16S rRNA; Andes; bacterial diversity;
pyrotags; terrestrial hot spring.
Abstract
Microbial explorations of hot springs have led to remarkable discoveries and
improved our understanding of life under extreme conditions. The Andean
Mountains harbor diverse habitats, including an extensive chain of geothermal
heated water sources. In this study, we describe and compare the planktonic
microbial communities present in five high-mountain hot springs with distinct
geochemical characteristics, at varying altitudes and geographical locations in
the Colombian Andes. The diversity and structure of the microbial communities were assessed by pyrosequencing the V5 - V6 region of the 16S rRNA gene.
The planktonic communities varied in terms of diversity indexes and were
dominated by the bacterial phyla Proteobacteria, Aquificae, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, and Thermotogae, with site-specific bacterial
taxa also observed in some cases. Statistical analyses showed that these microbial communities were distinct from one another and that they clustered in a
manner consistent with physicochemical parameters of the environment sampled. Multivariate analysis suggested that pH and sulfate were among the main
variables influencing population structure and diversity. The results show that
despite their geographical proximity and some shared geochemical characteristics, there were few shared operational taxonomic units (OTUs) and that community structure was influenced mainly by environmental factors that have
resulted in different microbial populations.
Introduction
Terrestrial hot spring ecosystems can be characterized by
extremes of pH and temperature that result in niches
with distinct microbial populations. Both the taxonomic
characterization of these communities and the identification and study of specific organisms provide insights into
the survival strategies and metabolic processes required
for growth in these aquatic habitats (Rzonca & SchulzeMakuch, 2003). Culture-independent studies based on
16S rRNA gene analysis have revealed that spring microbial communities can be very diverse and may harbor
both unique and extremophilic microorganisms as well as
ubiquitous microorganisms, such as Proteobacteria and
phototrophic bacteria (i.e. Chlorobi, Cyanobacteria, Chloroflexi) (Huang et al., 2011). In an effort to determine
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
variables that affect community composition, several
studies have also correlated hot spring microbial assemblages with environmental factors such as pH, temperature, salinity, and dissolved hydrogen sulfide levels
(Skirnisdottir et al., 2000; Whitaker et al., 2003; Purcell
et al., 2007; Lau et al., 2009; Sayeh et al., 2010). In other
studies, geographical isolation was found to affect thermophile populations from both distant locations (Papke
et al., 2003; Whitaker et al., 2003) and sites closer to one
another (Takacs-Vesbach et al., 2008). Thus, both environmental determinants and historical legacies have been
shown to influence microbial distribution (Martiny et al.,
2006; Takacs-Vesbach et al., 2008).
The Colombian tropical Andes Mountains have several
active volcanoes and areas of geothermal activity that are
made evident by the presence of hot springs, particularly
FEMS Microbiol Ecol 89 (2014) 56–66
57
Comparative analysis of Andean hot springs
along the central and eastern mountain ranges. These
springs are found at elevations between 2500 and
5000 masl, and they vary in terms of their geochemistry
and thermal source. There are more than 100 thermal
springs located in geographical proximity to the Nevados
National Natural Park (NNP) in the central mountain
range (Alfaro et al., 2002). In the same geographical
region, but as part of another volcanic system, is a spring
called Parador de Quimbaya, which is associated with an
area of thermal anomaly (Alfaro et al., 2002). On the
eastern Andean mountain range, the Ojo del Diablo hot
spring is part of a system of 22 springs with water temperatures (around 80 °C) and pH (3.6–7.4) that are
higher than those in the Nevados NNP (Baena et al.,
2011). Water at these sites emerges at isolated and undisturbed high-mountain habitats and provides a unique
resource for studying hot spring microbial communities.
Although the physicochemical characteristics and geothermal potential of many of these Andean springs have
been explored (Alfaro et al., 2002), the microbial communities present have only recently begun to be examined. A
survey of one of these springs (El Coquito) in the Nevados
NNP using culture-independent strategies indicated the
presence of mesophilic, thermophilic and acidophilic
microorganisms and a metabolic diversity that includes
both generalists and specialists potentially involved in
cycling of ferrous and sulfur-containing minerals (Bohorquez et al., 2012). An in-depth total metagenomic DNA
sequencing analysis also supported these conclusions
(Jimenez et al., 2012). However, the microbial communities in other springs in the area have not been explored,
and, based on their close geographical location, we
hypothesized that hot springs with similar chemical characteristics would also have similar community structure.
Thus, the aim of this work was to extend our observations
(a)
to include additional Andean hot springs and to compare
the microbial communities by assessing population structures, features unique to each site, and the presence of
shared community members indicative of a core microbiome. Together, these data can enhance our understanding
of how these communities are shaped and how they
function.
Materials and methods
Site description and physicochemical analysis
Water samples were collected from five springs located at
different altitudes and ecosystems in the Colombian central
and eastern Andean mountain ranges in March and April
2008 (Fig. 1a, Table 1). Surface water (5 L) was aseptically
collected in sterile bottles filled to the brim, capped, transported to the laboratory and processed within 18 h (physicochemical analyses and DNA isolation). Temperature and
pH were recorded in situ using a Hach pH meter equipped
with a pH and temperature probe. The analysis of chloride,
total phosphorus, total iron, sulfate, calcium, sodium, magnesium, potassium, nitrate, total solids (TS), total suspended solids (TSS), and total dissolved solids (TDS) were
carried out according to standard methods (Eaton & Franson, 2005). Geographical location and chemical analysis of
the water samples are shown in Table 1.
DNA extraction, PCR amplification, and
pyrosequencing
Total DNA was isolated from the water samples as previously described (Schauer et al., 2000; Bohorquez et al.,
2012) and stored at 20 °C prior to amplification. Primers 807F (GGATTAGATACCCBRGTAGTC) and 1050R
(b)
Fig. 1. Location and characteristics of sites sampled. (a) Map of the sites sampled in the Colombian neotropical Andes; the white circle indicates
the three close sites of the NNP (A4, A5 and A6). (b) Ordination biplot of principal component analysis (PCA) for environmental variables. Spring
samples are shown as circles, and environmental variables are designated with arrows.
FEMS Microbiol Ecol 89 (2014) 56–66
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
L. Delgado-Serrano et al.
38 848
0.48
1320
21.2
216
175
1037
19 250
12 375
2397
2.66
13.3
72.4
256
1052
58
2561
≤ 0.19
320
122
134
13.5
2280
0.89
9.25
55.3
0.2
0.01
5103
68
2500
6.9
56.6
0.51
36.5
2.66
30
4665
25.8
0.25
139
35
4363
3.12
8.27
0.1
56.6
2.7
29
3973
04°50 14″N
75°320 53.4″W
04°520 27″N
75°150 51,4″W
04°540 32,8″N
75°180 19″W
04°540 19.7″N
75°200 5.9″W
5450 33.29″N
7360 49.89″W
2683
58
6.5
345
0.21
2.66
16S rRNA gene sequence analysis
Superparamo
El Gualı
Ojo del
Diablo
A6
P3
Andean Forest
Superparamo
El Calvario
A4
Superparamo
A5
Andean Forest
Parador de
Quimbaya
El Coquito
802
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
*Fe2+ and Fe3+.
TDS, total dissolved solids.
TDS
1003
45.2
0.29
35.6
41.3
73
10.9
279
N-NO3
K+
Mg2+
Na+
Ca2+
SO42
Hot Spring
Sample
ID
(AGYTGDCGACRRCCRTGCA) were used to amplify the
V5-V6 region of the 16S rRNA gene of bacteria and archaea, as described previously (Bohorquez et al., 2012).
Briefly, PCR amplifications were performed in a 25 lL
reaction volume with 20 ng of metagenomic DNA and
0.75 lM of each primer, followed by a second PCR using
primers 16S807F-b15 and 16S1050R-b5 that include the
same V5-V6 sequences, adaptor for pyrosequencing
(GCCTCCCTCGCGCCATCAG), a two base pair spacer
and a 8 nt barcode (http://pyro.cme.msu.edu/pyro/pyro_8base_tagset.txt) (Bohorquez et al., 2012). Sequencing
was carried out using primer A on the Roche GS FLX
and GS FLX Titanium systems (EnGenCore, University of
South Carolina). Pyrosequencing reads have been submitted to the NCBI Sequence Read Archive under Accession
Number SRA052192 (http://www.ncbi.nlm.nih.gov/sra/?
term=SRA052192).
0
Total
Fe*
Total
PO43
Cl
GPS coordinates
Altitude
(m)
Temperature
(°C)
pH
Concentration (mg L1)
Ecosystem
(altitudinal
section)
Table 1. Geochemical characteristics of Andean hot springs
1235
58
Pyrosequencing reads were analyzed using QIIME v1.6.0
(Caporaso et al., 2010). Sequences were filtered based on
quality, removing reads with low quality scores (< 25)
evaluated by a slide window of 40 nt, with ambiguous
characters, and with a length < 80 nt after trimming the
barcode and primers. The remaining sequences were denoised (Reeder & Knight, 2010) and checked using QIIME’s
ChimeraSlayer (Haas et al., 2011). After separating reads
based on barcodes, filtered datasets were resampled to
4400 reads per sample using the statistical package R
(http://www.r-project.org/) to eliminate effects due to differences in the number of sequences analyzed. Sequences
were aligned using MOTHUR (Schloss et al., 2009), and
diversity and richness indexes were obtained with the furthest neighbor algorithm. UCLUST (Edgar, 2010) was used
to obtain operational taxonomic units (OTUs) with a
97% pairwise identity threshold, and the RDP-na€ıve
Bayesian
classifier
(http://rdp.cme.msu.edu/classifier/
classifier.jsp) (Wang et al., 2007) was used for taxonomic
classification with a 50% confidence level (Claesson et al.,
2009). The phylogenetic overlap at 97% identity was calculated using the core microbiome script in QIIME. The
software package for Statistical Analysis of Metagenomic
Profiles, STAMP, was used to compare taxonomic profiles
of each pair of samples at different taxonomic levels by
running differences between proportions, Fisher0 s exact
and chi-square tests (Parks & Beiko, 2010). For tree-based
analyses, single representative sequences for each OTU
were aligned using PYNAST, and a phylogenetic tree was
built using FASTTREE (Price et al., 2009), which served as
input for UniFrac (Lozupone & Knight, 2005). The unweighted UniFrac metric was calculated for cluster and P
test analyses.
FEMS Microbiol Ecol 89 (2014) 56–66
59
Comparative analysis of Andean hot springs
Multivariate analysis
To determine which environmental variables best
explained community patterns, a multivariate principal
component analysis (PCA) of physicochemical characteristics was carried out with the following variables: pH,
temperature, chloride, total phosphorus, iron, sulfate, calcium, nitrate, potassium, sodium, magnesium, TDS, and
altitude. All variables except for pH were ln+1 transformed to better conform to normality. The canonical
correspondence analysis (CCA) was used to evaluate the
relationship between environmental variables and microbial community at phylum, class, and order levels, generating the most efficient model from the initial set of 13
explanatory variables used in separate CCA runs. The
Monte Carlo Test (999 permutations, a = 0.05) and forward selection were used to determine the marginal and
conditional effects of predictor variables on the ordination. Multivariate analyses (PCA and CCA) were carried
out using CANOCO 4.5 software (Microcomputer Power,
Inc., Ithaca, NY), as recommended (ter Braak & Smilauer,
1998). To evaluate the correlation and statistical significance between biotic similarity and environmental factors,
a Mantel test was performed using the zt program (Bonnet & Peer, 2002). To construct the abiotic similarity
matrices, all raw values (e.g. values of pH, temperature,
distance) were standardized and used to calculate 1 – the
Euclidean distance for each pair, as previously described
(Pagaling et al., 2009). The biotic similarity matrices were
assessed by means of Jaccard indexes calculated from Jaccard distance values at 97% identity obtained using QIIME.
Correlations between diversity and environmental factors
were analyzed by lineal and quadratic regressions using
SPSS 19.0 statistical software (SPSS Inc., Chicago IL).
Results
Abiotic characterization of the hot springs
Five different springs were sampled in the Andean Mountains, one on the eastern mountain range [Ojo del Diablo
(P3)] and the remaining four on the central range, three
located close to one another in the Nevados NNP, El Coquito (A4), El Calvario (A5), El Guali (A6), and one further west, Parador de Quimbaya (802) (Fig. 1a). These
springs differed in their chemical composition (Table 1)
and water temperature (29–68 °C), which in all cases was
higher than the average ambient temperature of the
region (≤ 9 °C) and allows them to be classified as hot
springs (Rzonca & Schulze-Makuch, 2003). Hot springs
A4, A5 and A6, with an acidic pH (2.7–3.1) and low
chloride and high sulfate concentrations, are steam-heated
acidic sulfate waters that result from the vapor-phase separation due to boiling geothermal fluid and the oxidation
of H2S (Alfaro et al., 2002). Hot spring 802 differed from
springs A4, A5, and A6 because of its high pH and low
mineral content. P3 had the highest temperature (68 °C),
pH, TDS, and sulfate, which is probably due to the presence of an evaporated salt deposit located near the area.
The physicochemical data were analyzed by carrying
out a PCA (Fig. 1b). The first two axes explained 95.9%
of the total variance and separated samples according to
temperature, pH, and ion concentration. The first axis
separated acidic springs A4, A5, and A6 from springs P3
and 802 explaining most of the variance (66.5%), which
was determined by pH, TDS, and minerals (Cl, Fe,
Ca2+, Na+, K+ and Mg2+). The second axis (29.4% of the
total variance) was related to altitude, temperature, and
sulfate concentrations.
Microbial community composition and
diversity
The structure of the microbial community in each spring
was analyzed using 454 pyrosequencing of the V5 - V6
region of the 16S rRNA gene. The number of sequence
reads obtained per sample varied from 4453 to 17 251.
To compare the five samples, we randomly resampled
each dataset to work with 4400 sequences for each site
and calculated diversity and richness indexes at 97%
identity (Table 2). The number of OTUs varied broadly
from 91 (spring P3) to 585 (spring A5), and richness esti-
Table 2. Diversity Analysis at 0.03 genetic distance obtained from 4400-sequence subsamples using DOTUR
Richness estimators
Diversity
Coverage
Sample
Total
sequences
OTU
SCHAO
SACE
Shannon-Wiener
Index
Simpson
Index (D)
CGOOD
CACE
802
A4
A5
A6
P3
5250
4453
17 251
5947
8584
510
236
585
108
91
1071.12
383
988.56
143.43
201
1664.47
491.76
1163.46
142.66
270.46
3.89
3.32
4.76
2.86
1.67
0.07
0.10
0.03
0.12
0.37
0.93
0.98
0.94
0.99
0.99
0.67
0.77
0.78
0.85
0.75
FEMS Microbiol Ecol 89 (2014) 56–66
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Published by John Wiley & Sons Ltd. All rights reserved
L. Delgado-Serrano et al.
60
Fig. 2. Rarefaction curves for microbial diversity in the five hot
springs evaluated. OTUS were calculated at a 97% cutoff.
mators SCHAO and SACE showed that estimates for all
cases were higher than the observed number of OTUs.
Coverage estimates determined using CGood and CACE also
varied among samples, all showing Good’s coverage above
93 (Table 2). A rarefaction analysis showed that in some
cases, as for samples P3 and A6 with lower overall comparative diversity, the curves were reaching a plateau
indicative of low diversity when compared with the other
springs inspected and thus of being closer to estimating
the planktonic prokaryotic diversity present in the samples. While more sampling would still be required
for samples like A5, due to its higher diversity, the
(a)
description of the overall community composition and
abundant OTUs could be sufficient given the relatively
high coverage (Fig. 2, Table 2).
The community structure for each site was determined
by doing a taxonomic classification of the sequences using
the RDP classifier with a 50% confidence threshold, as
has been recommended for the assessment of short
sequence reads (Claesson et al., 2009). The majority of
the sequences were classified as Bacteria (98.2%), and
there was an overall predominance of Proteobacteria
(Beta-, Alpha-, and Gammaproteobacteria), although other
phyla were also prevalent: Aquificae, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, and Thermotogae. Within
the Cyanobacteria, the majority of the sequences were
associated with chloroplast ribosomal DNA, indicating
the presence of microeukaryotes, as reported previously
for A4 (Bohorquez et al., 2012). Also consistent with this
same analysis of site A4, there were few archaeal
sequences (1.8%), which were classified within the phyla
Euryarchaeota (order Thermoplasmatales) and Crenarchaeota (unclassified Crenarchaeota). There were also
sequences that could not be assigned to known taxa using
the RDP database (A4 = 30.7%; 802 = 11.6%; A5 = 8%;
A6 = 6.8% and P3 = 1.9%) and a list of phyla with
uncertain taxonomic placement (incertae sedis), as shown
in Fig. 3a.
Rank abundance curves generated for each resampled
dataset indicated that, despite the high accumulation of
sequences in few single OTUs, a large number of reads
corresponded to rare OTUs represented by few or single
sequences (data not shown), consistent with previous
reports (Sogin et al., 2006). Overall, 7–30% of the
OTUs were represented by more than 10 sequences,
33–43% of the OTUs had 2–10 sequences, and the
(b)
Fig. 3. Relative abundance of bacterial taxa in the five Andean hot springs studied. The percentage of each taxon is shown for each of the
sampled sites at the level of (a) phylum and (b) class for Proteobacteria.
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FEMS Microbiol Ecol 89 (2014) 56–66
Comparative analysis of Andean hot springs
remaining 30–59% were represented by unique sequences.
The most abundant OTU in each sample was also identified, and for two of the springs (802 and A5), it corresponded to an unclassified Betaproteobacteria that
represented 19% and 8% of the sequences, respectively.
For the other samples, the most abundant OTU was affiliated to an uncultured bacterium for A6 and A4 (26%
and 28% of the sequences, respectively) and the genus
Hydrogenobacter belonging to Aquificae for P3 (57% of
the sequences) (Supporting information, Table S1).
Differences in the microbial composition of these hot
springs were more evident at deeper taxonomic levels.
Springs A4 and A5, which are located close to each other,
and 802, which is more distant (Fig. 1a), shared some of
the abundant groups at high taxonomic levels, such as
phylum and class, but their taxonomic profiles differed at
the order and family levels (data not shown). For example, at class level, there was a predominance of Beta- and
Alphaproteobacteria for the three springs, but 802 was
dominated by the order Gallionellales (Betaproteobacteria)
while there was a predominance of Rhodospirillales (Alphaproteobacteria) for A5 and Burkholderiales (Betaproteobacteria) for A4. The remaining A6 and P3 hot springs
differed at all phylogenetic levels. A6 had unclassified
Thermotogae as the predominant bacteria. The clear dominant group at order level in hot spring P3, which had
the lowest diversity index (Table 2) and fewest taxonomic
groups, was Aquificales, known as thermophilic sulfuroxidizing bacteria. Among the few Archaea identified in
this study, Crenarchaeota related to the order Cenarchaeales were retrieved from the springs A4, A5, and 802 and
unclassified Crenarchaeota from A6 and the hottest spring
P3.
These spring communities were further examined using
three approaches. Pairwise comparisons among datasets
using a STAMP analysis indicated differences in proportions
that were significant among the samples at the phylum
level (P-values < 0.05) and showed that certain phylogenetic groups were characteristic of a particular hot spring
community, even if they were not predominant in the
dataset. For example, the WPS-2 phylum was highly
abundant, yet not predominant, in hot spring A4 when
compared to the other hot springs. Also, 802 had the
highest number of significantly different phyla, whereas
A6 and P3 had only a few different groups. Phylogenetic
differences between hot springs were next examined using
UniFrac, which showed that the microbial communities
were distinct from one another (P-values < 0.001). The
acidic springs A4 and A6 clustered together, as did 802
and P3, and all were separated from the community in
hot spring A5 (Fig. 4). Finally, OTUs were compared
across datasets to detect a core microbial community in
these hot springs. However, there was no OTU common
FEMS Microbiol Ecol 89 (2014) 56–66
61
Fig. 4. Hierarchical clustering analysis using Unifrac. The line below
indicates substitution per site.
to all samples at the sequencing depth of our study. Consistent with the UniFrac results, A4 and A6 hot springs
had the highest phylogenetic overlap, sharing 50 OTUs.
When extending the comparison to include springs A4,
A5, and A6, only 26 shared OTUs were identified. The
last two springs, P3 and 802, shared 15 OTUs.
Community structure and diversity in relation
to environmental factors
The relationship between microbial community composition and environmental variables was evaluated using two
different approaches: a CCA and a Mantel test. For the
CCA analysis, the model generated after different runs
with 13 explanatory variables showed significance only
when using pH and sulfate as the environmental characteristics (Monte Carlo test P < 0.05). The variation in
microbial composition among the springs could be
explained by pH (57% at phylum level) and by SO42
(43% and 34% at the phylum and class level, respectively)
(Fig. S1). Again, A4, A5, and A6 grouped together as
occurred in the PCA, probably due to the low pH.
Springs P3 and 802 were separated as they differed in the
concentrations of SO42 and because of their high pH.
The Mantel test showed that geographical distance was a
significant factor in influencing microbial community
composition at 97% identity, as well as contemporary
environmental factors such as pH, temperature, and sulfate and chloride concentrations (P-values < 0.05) (Table
S2). In general, pH and sulfate showed the greatest influence on the structure of the communities present in these
five hot springs, as determined by both CCA and Mantel
test. However, only pH showed a correlation with the
diversity Shannon index (quadratic regression with a r2
value of 0.98 and a P-value of 0.019), where hot springs
with the lower and higher pHs had the lowest diversity
values.
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
62
Discussion
Microbial diversity in acidic hot springs
This work extends a recent survey of the microbial diversity
in hot spring A4 (El Coquito) by including three additional,
geographically close springs (A5, A6, 802), and a more distant spring (P3) located in a separate mountain range. The
spring communities were all characterized by the predominance of Bacteria over Archaea, which is consistent with
previous FISH analysis of site A4 using archaeal and bacterial probes (Bohorquez et al., 2012). However, the primer
set used to amplify the V5-V6 16S rRNA gene region could
also have missed novel sequences, many of them Archaea,
or those present in very low abundance in these environmental samples. The predominance of bacteria is, however,
consistent with reports for hot springs at high altitudes
(Yim et al., 2006; Huang et al., 2011), but differs from
other extreme environments (Chapelle et al., 2002). All
sites contained Archaea from the phylum Crenarchaeota,
which are thermophilic, sulfur-dependent chemolithoautotrophic organisms that are usually found in hot springs
where they can grow on H2, S, or H2S using the electron acceptors O2, NO3, S, or Fe3+ (Song et al., 2010). Although
the lack of greater taxonomic resolution within the phylum
Crenarchaeota did not allow in-depth identification of the
genera present, the differences in water chemistry and pH
at each hot spring probably produce different forms of oxidized and reduced sulfur and iron compounds and varying
energy source concentrations that could be used by these
chemolithoautotrophic microorganisms.
Within bacteria, there was a predominance of Proteobacteria, particularly of Betaproteobacteria, and a large number
of unclassified Bacteria (ranging from 30.7% for A4 to
1.9% for P3; see Fig. 3), as has been reported for other
acidic thermal springs (Wilson et al., 2008). This latter
result suggests that many of the microorganisms found in
these habitats could be different from those previously
identified in other springs and environments (Baker et al.,
2001; Jackson et al., 2001; Demergasso et al., 2010). Overall, these Andean spring microbial communities harbored
diverse taxonomic groups and an abundance of mixotrophs
with respect to other groups (i.e. organotrophs, autotrophs) that could indicate an advantage in oligotrophic
environments where they can more efficiently use limited
resources for growth (Tittel et al., 2003). As previously
noted for site A4, the communities in the more acidic
springs include both mesophilic and thermophilic microorganisms and are in general similar to those found in other
hot and acidic environments (Bohorquez et al., 2012). In
these acidic hot springs that are rich in sulfur and iron
compounds, and where there is scant evidence for photo-
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
L. Delgado-Serrano et al.
trophic primary production, mixotrophic metabolism is
probably used to enable chemolithotrophic microorganisms to oxidize reduced forms of sulfur and iron as energy
sources. The reduced gases that could originate from the
volcanic environments near acidic hot springs could also be
an energy source for the microbial community.
The springs differed in water physicochemical characteristics and in diversity indexes and were distinguished
by the presence of specific taxonomic groups and the
absence of shared community members. The most diverse
spring was A5, which had warm water temperature
(35 °C) and was acidic (3.1), although its pH was not as
low as in other springs studied (sites A4 and A6,
Table 1). Site P3, which had the least diversity and the
lowest richness estimates, had the highest temperature
(68 °C) and pH (6.9), but also had high concentrations
of chlorides and sulfate, which could affect diversity. Specific bacterial taxa characterized each of the springs sampled. For example, hot spring P3 was dominated by
Aquificales of the genus Hydrogenobacter, which includes
thermophilic, chemolithotrophic, and oxidizing aerobic
species that have an optimum growth temperature of
65 °C (Stohr et al., 2001) and have been found in the Izu
peninsula (Japan), and in hot springs in Italy, Japan, and
China (Zeytun et al., 2011; Hou et al., 2013). Acidic
spring A5 was dominated by members of the mixotrophic
acidophilic genus Acidiphilium of the order Rhodospirillales, organisms that are widely distributed in extremely
acid, heavy metal-rich environments (Hao et al., 2010),
but have also been reported as predominant in acidic hot
springs with lower temperatures (~ 33 °C) in the Caribbean island of Montserrat and in an acidic mine drainage
in China (Burton & Norris, 2000; Hao et al., 2010). These
microorganisms couple organic carbon oxidation to dissimilatory reduction in ferric iron (Fe3+) that is more soluble at low pH than at near-neutral pH (Johnson et al.,
2012). Acidiphilium spp. can also carry out photosynthesis
as a mixotrophic strategy useful for surviving in oligotrophic and acidic environments (Wakao et al., 1996).
Hot spring A4 was dominated by Thiomonas spp., which
are ubiquitous and widely studied in extreme environments. This genus includes facultative chemolithoautotrophs able to grow on inorganic sulfur, arsenite, and
iron (Battaglia-Brunet et al., 2002; Johnson et al., 2005),
over a wide range of temperatures (Battaglia-Brunet et al.,
2006; Kelly et al., 2007; Arsene-Ploetze et al., 2010).
However, arsenite, which is usually associated with volcanic environments, has not been reported in these springs
(Alfaro et al., 2002). Strains of the genus Thiomonas have
been isolated from different environments such as uranium mines, sewage sludge, sediments, and water from
hot springs (Huber & Stetter, 1990; Vesteinsdottir et al.,
FEMS Microbiol Ecol 89 (2014) 56–66
63
Comparative analysis of Andean hot springs
2011). A high number of the sequences from hot spring
A6 were affiliated to the phyla Thermotogae, which could
not be classified at deeper phylogenetic levels, and the
Firmicutes. The Firmicutes sequences were related to the
order Clostridiales, which contains thermophilic and
strictly anaerobic organisms that have been reported in
hot springs (Slobodkin et al., 1997; Bouanane-Darenfed
et al., 2011; Fraj et al., 2013; Lozupone et al., 2012).
Sequences dominant in hot spring 802 belonged to the
order Gallionellales, which have been found in groundwater seep, freshwater, and the rhizosphere of wetland plants
(Krepski et al., 2012; Saini & Chan, 2013). Finally, the
order Thermoplasmatales of the Euryarchaeota was found
only in springs A5, A6, and P3 and includes acidophiles,
some of which are thermophilic, such as the members of
the Thermoplasmata (Auguet & Casamayor, 2013)
reported in hypersaline environments and other hot
springs (Yasuda et al., 1995; Schouten et al., 2007;
Oueriaghli et al., 2013).
In addition to the differences in physicochemical properties and the presence of particular phylogenetic groups,
there was also an absence of a core microbial community
across sites. Even springs close to one another (A4, A5,
A6 and 802) or those that clustered based on phylogenetic profiles (A4 and A6) shared only very few OTUs.
Greater depth of sequencing might detect groups present
at extremely low abundance in these water systems and
thus lead to more overlap among biomes, as suggested
for ocean waters (Gibbons et al., 2013). This might not
be the case for all samples, however, as in some cases rarefaction curves seemed to reach a plateau (such as for
sites A6, P3; Fig. 2) and suggest that additional sampling
might not necessarily detect many more taxa. The presence of abundant members in each sample, the absence
of a core microbiome shared among springs, and the
presence of hot spring-specific OTUs also indicate a
microbial endemism and communities shaped essentially
by local environmental variables.
Environmental factors determine microbial
composition
Analyses undertaken to identify parameters that could
affect the composition of the communities in these five
hot springs indicated that water chemical composition,
mainly pH and sulfate, and possibly geographical distance
at a small scale were responsible for population structure
(see Fig. S1, Table S2). Statistical STAMP and UniFrac
analyses showed that the microbial communities in these
hot springs are unique, even though some springs, such
as A4 and A6, were more closely related to one another
by both PCA (Fig. 1b) and hierarchical clustering
(Fig. 4), indicating similar environmental conditions and
FEMS Microbiol Ecol 89 (2014) 56–66
phylogenetic profiles. The communities in hot springs P3
and 802 also clustered together while A5 differed with
respect to the others. Previous studies have shown that
factors such as temperature, salinity, pH, and sediment
mineral chemistry can affect microbial structure, diversity,
and richness (Lindstrom et al., 2005; Fierer & Jackson,
2006; Mathur et al., 2007; Herlemann et al., 2011; Tobler
& Benning, 2011). For example, Fierer and Jackson
(2006) reported that differences in richness and diversity
of soil ecosystem types could be explained by pH, with
diversity increasing close to neutral pH. In this case, pH
was also important, but the highest diversity was found
in an acidic spring (pH 3.1) rather than in springs with
pH closer to 7. Thus, additional factors, such as metals
and salts that contribute to the unique features of each
pool, also influence diversity. This was clearly the case for
site P3, which was the least diverse but differed from others in its taxonomic profile probably due to the high content of TDS, presence of minerals, and more extreme
conditions of temperature. Each pool is therefore a distinct habitat with microbial communities characterized by
particular phylogenetic groups, some of which occur at
great frequency and could represent adaptation to a given
restrictive environment.
Although many questions remain unanswered, our
results indicate that site-specific environmental properties,
in this case pH and sulfate, select for and drive change
within these high-mountain thermal planktonic prokaryotic communities and have a stronger effect on these
aquatic assemblages than factors such as geographical distance or isolation. This would result in diverse spring
communities with distinct microbiomes, despite their
geographical proximity. However, particular underlying
similarities in overall taxon composition, such as predominance of Bacteria over Archaea, as well as the presence of
some shared community members, could also be due to
seeding by an early core community, especially in the
three closely related springs located in the same mountain
range (A4, A5 and A6), that gave rise to the different
populations present today at each site. These results highlight the need to understand microbial community
dynamics and genomic variability of community members, such as those that are abundant in one spring but
represent a minority in another one, to more fully
understand community function and their importance for
maintenance of high-mountain Andean ecosystems.
Acknowledgements
We would like to thank Luis Miguel Alvarez for help with
sampling, Jose Salvador Monta~
na for sampling and
processing DNA, Claudia Alfaro for providing hot spring
geochemical information and Martha Lucia Cepeda for her
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
64
collaboration in the development of this manuscript. This
work was financed by Colciencias (project no. 6570-39219990 and contract no. 573-2012) and was performed under
MAVDT contract no. 15, 2008 for access to genetic
resources and UAESPNN Research permit no. DTNO-N20/2007.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Fig. S1. Canonical Correspondence Analysis (CCA)
between the five hot spring microbial communities at
phylum level and environmental variables.
Table S1. Summary of the most represented OTUs from
each sampling site.
Table S2. Simple Mantel test for the microbial community at 97% identity.
FEMS Microbiol Ecol 89 (2014) 56–66