Download Molecular ecology of microbial mats

MINIREVIEW
Molecular ecology of microbial mats
Henk Bolhuis, Mariana Silvia Cretoiu & Lucas J. Stal
Department of Marine Microbiology, Royal Netherlands Institute for Sea Research, NIOZ, Yerseke, The Netherlands
Correspondence: Henk Bolhuis, Department
of Marine Microbiology, Royal Netherlands
Institute for Sea Research, NIOZ,
Korringaweg 7, NL 4401 NT Yerseke, The
Netherlands. Tel.: +31-113-577478;
fax: +31-113-573616;
e-mail: [email protected]
Received 21 May 2014; revised 27 July 2014;
accepted 5 August 2014. Final version
published online 28 August 2014.
DOI: 10.1111/1574-6941.12408
Editor: Gerard Muyzer
MICROBIOLOGY ECOLOGY
Keywords
Cyanobacteria; microbial mat; metagenomics;
hyperthermal; hypersaline; coastal.
Abstract
Phototrophic microbial mats are ideal model systems for ecological and evolutionary analysis of highly diverse microbial communities. Microbial mats are
small-scale, nearly closed, and self-sustaining benthic ecosystems that comprise
the major element cycles, trophic levels, and food webs. The steep and fluctuating physicochemical microgradients, that are the result of the ever changing
environmental conditions and of the microorganisms’ own activities, give rise
to a plethora of potential niches resulting in the formation of one of the most
diverse microbial ecosystems known to date. For several decades, microbial
mats have been studied extensively and more recently molecular biological
techniques have been introduced that allowed assessing and investigating the
diversity and functioning of these systems. These investigations also involved
metagenomics analyses using high-throughput DNA and RNA sequencing.
Here, we summarize some of the latest developments in metagenomic analysis
of three representative phototrophic microbial mat types (coastal, hot spring,
and hypersaline). We also present a comparison of the available metagenomic
data sets from mats emphasizing the major differences between them as well as
elucidating the overlap in overall community composition.
Introduction
Microbial mats are vertically stratified communities of
functional groups of microorganisms embedded in an
organic matrix that can also contain various amounts of
minerals such as silicates and carbonates (Stal, 2012).
Microbial mats are benthic communities that grow on a
solid substrate (e.g. sand, rock, and other sediments) and
the vast majority is autotrophic, that is utilize inorganic
carbon as carbon source. This review focuses on phototrophic microbial mats that develop in illuminated environments, which are in majority build by the oxygenic
phototrophic Cyanobacteria, sometimes aided by phototrophic microbial Eukarya (e.g. diatoms).
Microbial mats are often considered as analogs of stromatolites, the fossil remains of which date back to almost
3.5 billion years and, hence, represent the oldest ecosystem we know (Margulis et al., 1980). Precambrian stromatolites, that is laminated rock, were formed by
microbial mats in shallow marine environments and lithified through calcification or silicification. The laminated
structure reflects the successive development of microbial
mats and in certain cases may have followed sea level rise.
FEMS Microbiol Ecol 90 (2014) 335–350
Microbial mats are also important for terraforming by
stabilizing the sediment surface and increasing the sediment erosion threshold. For instance, coastal microbial
mats affect coastal morphodynamics and may serve as a
natural barrier against rising sea levels (Yallop et al.,
1994).
The typical multilayered structure of microbial mats
(‘vertical stratification’) originates from the physicochemical gradients that are generated and maintained by the
activities of the component microorganisms. These physicochemical gradients provide microenvironments for different functional groups of microorganisms, that is
groups that exhibit a certain physiology with which they
fulfill a specific function (van Gemerden, 1993). For
example, in phototrophic microbial mats Cyanobacteria
and phototrophic Eukarya fulfill largely the same function
of harvesting light as the source of energy, splitting water
as the source of electrons and using these to fix CO2.
These are used to synthesize organic matter (‘primary
production’) for growth and for the production of nonstructural components such as extracellular polymers
(EPS) (De Philippis & Vincenzini, 1998). EPS form the
matrix in which the organisms are embedded and serve
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H. Bolhuis et al.
336
as glue with which a cohesive structure is formed that
renders stability to the mat and the sediment surface
(Grant & Gust, 1987).
The organic matter formed through primary production is the basis of the microbial food web. This organic
matter becomes available to other microorganisms by a
variety of processes (microbial loop) (Pomeroy et al.,
2007). In the dark, Cyanobacteria and algae respire their
endogenous carbon reserves thereby depleting the mat of
oxygen. These organisms continue to degrade their carbon reserves under anoxic conditions by fermentation
resulting in the production of low-molecular organic
acids and alcohols (Stal & Moezelaar, 1997). These fermentation products are further oxidized by methanogenic
bacteria and sulfate-reducing bacteria, often in a syntrophic mode with other microorganisms. Sulfate-reducing bacteria outcompete methanogens in marine and
hypersaline microbial mats because of the high concentration of sulfate in the seawater, but are important in lowsulfate microbial mats. Sulfate-reducing bacteria produce
sulfide, which is oxidized back to sulfate by sulfur-oxidizing bacteria. Chemoautotrophic bacteria oxidize sulfide
aerobically while anoxygenic photoautotrophic bacteria
oxidize sulfide anaerobically in the light (van Gemerden,
1993). The latter forms a purple layer underneath the
Cyanobacteria. Colorless sulfur bacteria do not form such
a distinct layer and are found throughout the mat and
probably take advantage of the vertical migrations of the
oxygenated layer (Visscher et al., 1992). Also sulfatereducing bacteria do not form a distinct layer, although
different species may be found along the vertical gradient,
depending on their oxygen tolerance (Canfield & Des
Marais, 1991; Risatti et al., 1994).
Grazing has not been extensively studied in microbial
mats per se. It does probably not play an important role in
microbial mats and therefore does not contribute substantially to carbon and nutrient cycling. When grazing meioand macrofauna would be present they would destroy the
mat (Fenchel, 1998). It is therefore generally thought that
microbial mats only develop in environments that largely
exclude grazing organisms (i.e. extreme environments).
When inundated, coastal microbial mats may periodically
experience grazing by nematodes (Feazel et al., 2008),
snails, or even fish. Eukarya, including metazoa, are present
in such microbial mats, but their role as grazers remains to
be seen (Bolhuis et al., 2013; Edgcomb et al., 2014).
Although not much is known about viruses (bacteriophages) in microbial mats, they are present and most
likely they are important factors for the recycling of
carbon and nutrients and probably also contribute to
genetic exchange and bacterial evolution within the mat
(Br€
ussow et al., 2004). In a comparison between different
ecosystems, a submerged marine cyanobacterial mat was
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found to have the highest phage density of all tested ecosystems (Hennes & Suttle, 1995). Investigations of Clustered Regularly Interspaced Palindromic Repeats
(CRISPRs) and cas genes in hot spring microbial mat Cyanobacteria (Synechococcus) hinted to a fast coevolution
of the host and viral genome (Heidelberg et al., 2009).
Microbiologists and geologists have studied microbial
mats intensively for several decades (e.g. Stal, 2012). However, the initial studies were hampered by the lack of
appropriate methods and tools that would allow the investigation of microbial processes in the often less than a millimeter thick layers of these systems. The first breakthrough
was the application of microelectrodes and microlight sensors in the study of microbial mats (Jørgensen et al., 1983;
Lassen & Jørgensen, 1994). These sensors allowed measurements at the tens-of-micrometer scale and revealed detailed
high-resolution spatial and temporal information on light
(including its spectral distribution in the mat), oxygen, sulfide, pH, redox, photosynthesis, and sulfate reduction.
However, much remained unknown about the organisms
and their activities that were behind these biogeochemical
processes. Until the application of molecular genetic techniques, information about the microorganisms in microbial mats was limited to isolation and cultivation and
microscopy. Molecular biology revealed the enormous
diversity of the microbial community and their metabolic
pathways and opened whole new avenues for the study of
microbial mats, which is witnessed by a wealth of scientific
reports that have appeared in recent years. The rRNA clone
libraries and DGGE techniques, that were initially used,
have now been replaced by meta-omics and high-throughput sequencing (HTS) and with the availability of new bioinformatics tools their analysis will lead to a revolutionary
different understanding of the diversity, ecology, and
evolution of microbial mats.
In this minireview, we show how molecular genetics has
revolutionized the study of microbial mats, discuss some
problematic issues with the application of molecular biology, review the progress achieved, and provide an outlook
on what we can expect in the near future. As an example,
we selected three extreme environments where modern
microbial mats develop and examined what is known
about microbial diversity in intertidal, hypersaline, and
hyperthermal microbial mats and compare these three
systems with respect to the environments they thrive in.
Sampling and molecular analysis of
microbial mats
Nucleic acid extraction
Microbial mats require their own approach and strategies
with respect to sampling and molecular analysis. Nucleic
FEMS Microbiol Ecol 90 (2014) 335–350
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Molecular ecology of microbial mats
acid extraction is particularly problematic because of the
presence of a dense EPS/sediment matrix and precipitates
of calcite and halite (Dupraz & Visscher, 2005). Whether
to use commercially available nucleic acid extraction kits
or classical phenol/chloroform-based methods depends on
the geochemical nature and physical properties of the
samples and requires optimization for each mat type. An
additional problem with nucleic acid extraction is that
the efficiency of cell lysis strongly varies among different
microorganisms. In particular, filamentous Cyanobacteria
that are heavily encapsulated by EPS are known to be
difficult to lyse and may require a combination of
mechanical (bead beating) enzymatic (proteases, lysozyme, or polysaccharide degrading enzymes) and chemical
lysis (guanidine isothiocyanate, sodium dodecyl sulfate,
NaOH). Moreover, even when lysis is successful, nucleic
acids may become trapped in EPS and inaccessible for,
for example PCR and sequencing. Therefore, with any
chosen method of nucleic acid extraction one should realize that several groups of microorganisms might be
missed in the final molecular data set.
Extracellular DNA
The EPS matrix of biofilms including microbial mats may
contain high amounts of extracellular DNA (Vlassov
et al., 2007). This extracellular DNA appears to be stable
in sediments and protected from nucleases (Lorenz &
Wackernagel, 1994). Some authors estimated that of the
DNA recovered from marine sediments up to 90% might
represent extracellular nucleic acid (Dell’Anno & Danovaro, 2005). In that case, RNA would give a better idea of
the actual active microbial species composition and their
metabolic diversity assuming that extracellular RNA is
unstable.
Covering the spatial and temporal
heterogeneity
An additional challenge is the spatial heterogeneity of
microbial mats (Armitage et al., 2012; Bolhuis et al.,
2013). Microbial mats are heterogeneous at the microand macroscale. Heterogeneity is typical for almost any
ecosystem and is generated by alternating stable states
(van de Koppel et al., 2001). In microbial mats physicochemical gradients of light, temperature, salinity, oxygen,
carbon, sulfur, and nitrogenous compounds will affect the
microbial community composition at the microscale. In
particular, in coastal microbial mats, larger scale heterogeneity is generated by the tides, precipitation, vegetation,
bioturbation, and other factors. This makes it basically
impossible to analyze the full extent of microbial diversity
of coastal mats that may stretch over several square kiloFEMS Microbiol Ecol 90 (2014) 335–350
meters (Villanueva et al., 2004, 2007; Bolhuis et al.,
2013). The problem of the heterogeneity of microbial
mats must also be taken into consideration when analyzing the metatranscriptome. Moreover, in the higher and
lower latitudes seasonality is also important for the composition and activity of the mat community. These mats
may experience a yearly cycle of growth, climax, and
destruction in addition to erratic events. Sampling at one
time point and at one position will therefore only provide
a ‘snapshot’ of the mat’s actual community composition
and function and cannot be reproduced and any extrapolation of such data can be questioned. Currently, the best
approach appears to be to collect several randomly chosen samples (the number depends on the structure and
heterogeneity of the mat) in a chosen plot and to resample the same site at different time points during a 24-h
day and during different seasons. However, this may easily result in the analysis of hundreds of samples (e.g.
three mat types with 10 spatial points per mat type, sampled over six time points in a 24-h period, repeated over
four seasons result in 720 samples for only DNA analysis
and the double amount if the RNA fraction is also targeted). Although separate analysis of samples is preferred
as it allows thorough statistical analysis and insight in the
heterogeneity of the system this may quickly become unaffordable for most laboratories. Depending on the goal,
available tools, and (financial) resources, subsamples can
either be pooled to cover spatial or temporal heterogeneity or analyzed separately. It should also be noted that
mats in intertidal areas often develop in one direction
and that it is unlikely that a previous state will return.
Molecular analysis in the omics era
Molecular techniques have greatly advanced the analysis
of microbial diversity in divergent ecosystems. Although a
number of different genes have been used for phylogenetic studies, the analysis of the 16S and 18S ribosomal
RNA (rRNA) genes of, respectively, Bacteria/Archaea and
microbial Eukarya have revolutionized microbial ecology
and enhanced our knowledge of the abundance, diversity,
and function of these microorganisms. With the exception of direct sequencing techniques and analysis of single
isolates, most phylogenetic studies in microbial communities relied on PCR based amplification of the genes of
interest followed by direct sequencing or by a combination of cloning and sequencing. The sequences are compared with databases to identify their closest match with
those of cultivated or uncultivated microorganisms.
The current standard in the study of microbial diversity involves next generation HTS techniques. These
methods generate between 1 million (454 pyrosequencing) and 600 million (Illumina) independent small DNA
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338
fragments (reads). Each of these techniques has its specific advantages and disadvantages with respect to read
length versus error rates (Harris et al., 2013). For reproducible and comparable results, special care has to be
given to library construction conditions (Ross et al.,
2013) and to obtaining high-base quality data (especially
for high GC regions) and increasing read lengths. For
amplicon sequencing the choice of primers is crucial and
the scientific community would benefit from focusing on
one fixed region of the 16S or 18S rRNA gene so that
databases consist of comparable data sets (Chakravorty
et al., 2007; Ghyselinck et al., 2013). Several PCR steps
can be omitted when whole environmental genome
sequencing becomes available for affordable prices, yielding sufficient phylogenetic information on 16S or 18S
rRNA genes and protein coding sequences to uncover the
dominant and rare biodiversity (Fierer et al., 2012). HTS
has revolutionized microbial ecology allowing access to
the full extent of microbial diversity including rare species
that were missed in traditional clone libraries. Moreover,
gene diversity can be assessed and used to reconstruct
metabolic networks using metatranscriptomics.
Intertidal microbial mats
Globally, intertidal microbial mats are formed on beaches
with low slopes and fine sandy sediments (Stal, 2012).
Intertidal areas are extreme environments in several ways.
Because intertidal areas are irregularly flooded, they experience strong salinity fluctuations (from almost freshwater
after rain to moderate hypersaline conditions upon prolonged desiccation) and large changes in temperature.
These environmental changes and fluctuations follow in
short time frames and could have generated the (micro-)
diversity that characterizes intertidal microbial mats (Bolhuis & Stal, 2011). Unlike hypersaline and hot spring
mats that exclude most Eukarya, intertidal microbial mats
are rich in microeukaryotic organisms (Bolhuis et al.,
2013). Intertidal microbial mats alter their environment
and provide the conditions for higher organisms to colonize the sediment and eventually form salt marshes or
initiate the formation of dunes (Grant & Gust, 1987;
Blanchard et al., 2000; Dupraz & Visscher, 2005; Bolhuis
& Stal, 2011; Armitage et al., 2012). Intertidal mats
cover large coastal surfaces worldwide including the
well-studied beaches of the North Sea barrier islands in
the Netherlands, Germany and Denmark (Stal et al.,
1984; Villbrandt et al., 1990; Severin & Stal, 2008; ).
Microscopy of intertidal mats revealed the dominance of
Cyanobacteria such as Coleofasciculus (Microcoleus) chthonoplastes and Lyngbya aestuarii. Cyanobacteria are the initial builders of coastal mats by producing biomass, EPS
and other organic matter, which form the basis of the
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H. Bolhuis et al.
microbial food web and support a plethora of different
functional groups of microorganisms (Stal et al., 1984;
van Gemerden, 1993; Bebout & Garcia-Pichel, 1995;
Guerrero et al., 2002; Abed et al., 2008; Dijkman et al.,
2010; Severin et al., 2010; Bauersachs et al., 2011; Gobet
et al., 2012).
A millimeter dissection of a tidal microbial mat using
16S amplicon HTS and metagenomic analysis was performed on samples extracted from the Great Sippewisset
salt marsh in the USA (Armitage et al., 2012). The amplified rRNA gene reads were dominated by Cyanobacteria
and purple sulfur bacteria (Chromatiales – Gammaproteobacteria). Cyanobacteria were most abundant in the top
2 mm and still detectable at 20 mm depth. Proteobacteria
other than Chromatiales dominated the 2–20 mm range.
Overall, the Sippewisset mats were taxonomically highly
diverse, and the community compositions differed
depending on the developmental stage of the mat, the
year of sampling, and the mat layer. Phylogenetic richness
and evenness positively covaried with depth while trait
richness tended to decrease with depth. The lower diversity in the top layer may be caused by unfavorable conditions such as UV irradiation, temperature, desiccation,
and erosion by wind and flooding that occur on a daily
base. During the day, the top layer of the mat is likely
composed of stress-tolerant aerobes such as the Cyanobacteria, while at night microaerophiles and UV-sensitive
taxa may temporarily migrate to the surface (Villanueva
et al., 2007).
In an attempt to assess part of the predicted heterogeneity of an intertidal mat, Gobet et al. (2012) compared
the microbial community of the sediment pore water, the
sand grains, and the overlaying seawater. Samples from a
shallow subtidal sand flat at the German island of Sylt in
the North Sea were analyzed using bacterial 16S amplicon
HTS of the c. 60 bp long variable V6 region of the 16S
rRNA gene. This study revealed that pore water contained
< 0.2% of the species that were associated with the sand
grains and the authors concluded that most of the microbial mat community was tightly bound to the sediment
and embedded in EPS. The abundant phyla associated
with the sediment belong to the Proteobacteria, Cyanobacteria, Bacteroidetes, and Acidobacteria. The number of
Cyanobacteria decreased with depth, but this was also the
case with the nonphotosynthetic Bacteroidetes. The
number of Betaproteobacteria increased with depth. Gobet
et al. (2012) showed that there was no change at the level
of phyla and class of the predominant groups with time.
However, when observed at a higher taxonomic resolution (e.g. family, genus, and species), drastic changes in
the bacterial community composition were found. Only
0.55% of the total number of > 27 000 unique OTU’s
were present in all samples at all times. However, it has
FEMS Microbiol Ecol 90 (2014) 335–350
339
Molecular ecology of microbial mats
to be taken into account that at a higher taxonomic resolution the outcome is more sensitive to point mutations
introduced by the PCR steps during pyrosequencing.
Richness estimates for the different samples varied
between 496 and 2993 OTU’s at the 97% cutoff level
underlining the overall high richness measured in coastal
microbial mats (Gobet et al., 2012). Metagenomic study
of an Arctic photosynthetic microbial mat revealed a
similar bacterial community composition dominated by
Proteobacteria, Cyanobacteria, Bacteroidetes, and Actinobacteria despite harsher conditions such as temperatures
ranging far below zero in wintertime and around 1 °C
during summer (Varin et al., 2010).
In our own study (Bolhuis & Stal, 2011), we investigated microbial diversity (both Archaea and Bacteria) of
coastal mats using the 60 bp, V6 variable 16S rRNA gene
amplicon HTS at various levels of spatial and temporal
resolution. Three different mat types were identified
(fresh/brackish water, intermediate and marine) that were
situated along a tidal salinity gradient that were sampled
during three different seasons. The most important outcome of that study was that the coastal mats of the Dutch
barrier island of Schiermonnikoog are among the most
diverse and species rich marine microbial ecosystems
studied so far, which was in agreement with other HTS
studies of similar microbial mats (Ley et al., 2006; Baumgartner et al., 2009; Armitage et al., 2012; Gobet et al.,
2012). Mats from the fresh/brackish water and intermediate zones contained a large proportion of Cyanobacteria,
but this group was not the most abundant as expected
from their visual dominance. Proteobacteria (especially
Alphaproteobacteria) and Bacteroidetes appeared as the
most dominant group in all coastal mat types studied on
the island of Schiermonnikoog. The Cyanobacteria were
present at much lower numbers in the marine zone that
is continuously affected by the tide. Bacteroidetes were
found in higher numbers in the marine tidal zone. The
diversity between the different mat types was far more
pronounced than the changes between the different seasons at one location. Several novel taxonomic levels were
identified ranging from classes to species especially among
the rare types. A subsequent study used DGGE and confirmed the presence of three different mat types by the
conserved clustering of the different community fingerprints in three major clusters (including Eukarya) but
also exposed a considerable (micro-) heterogeneity
between the communities (Bolhuis et al., 2013).
Carbon flux in the active population of intertidal
microbial mats in the Elkhorn Slough estuary in Central
California, USA was studied using metatranscriptomics
(Burow et al., 2013). The active community was strongly
dominated by Cyanobacteria (80–90% of the active population) independent of the time (morning or evening) of
FEMS Microbiol Ecol 90 (2014) 335–350
extraction. However, the contribution of Cyanobacteria to
the DNA fraction was c. 20% of the total community,
which is in agreement with the findings by Bolhuis & Stal
(2011). The Elkhorn Slough mats revealed a surprisingly
low number of Proteobacteria among the active community which was unexpected given the known contribution
of this group of Bacteria to essential processes such as the
sulfur cycle and is in contrast with studies on similar
mats in which Proteobacteria were often found to be the
dominant group. These mats also evolved net H2 under
anoxic conditions in the dark through cyanobacterial
(Microcoleus) fermentation (Burow et al., 2012). Sulfatereducing bacteria (Desulfobacteriales) oxidized part of this
H2 but insufficiently so to leave a net efflux (Burow et al.,
2014). Only Chloroflexi contributed importantly to the
cyanobacterial dominated mRNA pool, which was also
observed in hypersaline mats suggesting an intimate relationship between Cyanobacteria and Chloroflexi (Ley
et al., 2006). Metabolic reconstruction of highly expressed
genes in the metatranscriptome suggests that Cyanobacteria contribute in this relationship by fermenting photosynthate (stored as glycogen) to organic acids and
ethanol, which are taken up as carbon source by Chloroflexi that store it as polyhydroxyalkanoates (Burow et al.,
2013).
The importance of sulfur cycling by sulfide-oxidizing
and sulfate-reducing prokaryotes is well established in
coastal microbial mats (Canfield & Des Marais, 1991; van
Gemerden, 1993; Overmann & van Gemerden, 2000). The
metagenomic data presented above confirm their importance and numerical dominance as most mats are dominated by Proteobacteria important in sulfur cycling,
especially the purple sulfur bacteria belonging to the
Gamma-clade and sulfate-reducing bacteria belonging to
the Delta-clade (Bolhuis & Stal, 2011; Armitage et al.,
2012; Burow et al., 2012; Gobet et al., 2012).
Hypersaline microbial mats
Hypersaline mats are among the best-studied microbial
mat systems. These microbial mats are found worldwide
in natural occurring salt lakes and man-made salterns
used for salt industries. Hypersaline microbial mats are
exposed to salinities up to the crystallization point of
halite (Jørgensen, 1994; Des Marais, 1995) as well as to
high temperatures and to high solar radiation. These factors affect the community composition and metabolic
performance of the mat organisms but apparently do not
prevent the formation of a highly diverse and complex
microbial ecosystem. Hypersaline mats are less affected by
seasonal disruptions as found in coastal mats and continue to grow for several years resulting in multilayered
structures. Whereas Archaea such as the enigmatic Haloª 2014 Federation of European Microbiological Societies.
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H. Bolhuis et al.
340
quadratum walsbyi often dominate the hypersaline water
column (Bolhuis et al., 2004; Legault et al., 2006), hypersaline mats have a distinct bacterial signature (Ley et al.,
2006; Robertson et al., 2009; Armitage et al., 2012; Harris
et al., 2013). Ratios of bacterial/archaeal/eukaryal rRNA
genes of 90%/9%/1% have been found in the Guerrero
Negro mats confirming the bacterial dominance although
with a significant archaeal contribution to the metabolic
activities (Robertson et al., 2009). Also in hypersaline
mats, Cyanobacteria are the dominant primary producers
and are responsible for the production of a thick EPS
matrix that serves among others as protection against
desiccation. Sulfate-reducing bacteria, sulfur-oxidizing
and anoxygenic phototrophic bacteria are vertically stratified according to microgradients of oxygen, sulfide, and
light, as is the case in other types of microbial mats.
Sanger sequencing already provided insight in a diverse
cyanobacterial community in hypersaline mats consisting
of unicellular species (e.g. Pleurocapsa, Synechococcus, and
Gloeothece), filamentous nonheterocystous species such as
Coleofasciculus, Oscillatoria, Leptolyngbya, Schizothrix and
Phormidium, and heterocystous species such as Scytonema
and Calothrix (Paerl et al., 2000; Fourcßans et al., 2004;
Abed et al., 2011). The diversity of the hypersaline microbial mats of Guerrero Negro in Baja California Sur, Mexico, was investigated using Sanger sequencing of a
stunning number of 119.000 nearly full-length sequences
and 454 pyrosequencing of 28 000 reads of c. 200 bp
(Harris et al., 2013). The taxonomic information
obtained with both technologies gave congruent results.
The reads were obtained from a depth profile consisting
of 10 layers and revealed a phylogenetic stratification corresponding to light and geochemical gradients. Similar to
coastal mats, hypersaline mats also turned out to be
among the most diverse environments known. Dominant
groups in these hypersaline mats were Cyanobacteria, Bacteroidetes, Proteobacteria, Spirochetes, Chloroflexi, and
Planctomycetes. Among the Proteobacteria especially the
Deltaproteobacteria were abundant and at lower level
Alpha- and Gammaproteobacteria. Several new phylumlevel groups and many previously undetected lower level
taxa were found in the bacterial domain. The hypersaline
mats of Guerrero Negro contained members of more
than 40 of the estimated 100 different bacterial phyla
known, far more than, for example the approximately
eight phyla found in the human gut microbiome (Harris
et al., 2013).
Dissection of a Guerrero Negro mat into 10 successive
1-mm layers revealed a correlation between the genetic
gradient and the physicochemical profile of the mat
(Kunin et al., 2008). Analysis of the metagenome revealed
that proteins had a slight acid-shifted average isoelectric
point when compared with nonhalophilic genomes revealª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
ing an adaptation to increased salinity by enriching proteins with acidic amino acids. Heterotrophy below 3 mm
is important given the twofold increased proteins
involved in the sugar degradation pathways (glycolysis
and oxidative pentose phosphate pathway and uronic acid
degradation).
Cyanobacteria were found up to 50 mm depth but
dominated the top 5 mm. A wide vertical distribution of
the Cyanobacteria has been reported previously (Armitage
et al., 2012) and may be inherent to the ability of matforming Cyanobacteria to migrate along light and chemical gradients (e.g. Richardson & Castenholz, 1987; Bebout
& Garcia-Pichel, 1995; Kruschel & Castenholz, 1998;
Nadeau et al., 1999). Very little is known about vertical
migration of anoxygenic phototrophic bacteria and sulfate-reducing bacteria in hypersaline microbial mats.
Fourcßans et al. (2006) showed that anoxygenic phototrophic bacteria positioned themselves actively by migration in the oxygen and light gradient in a hypersaline
microbial mat. The same group observed also migration
of certain sulfate-reducing bacteria following the daily
shifting oxygen gradients in the same mats, although
other groups appeared to be uniformly distributed (Fourßcans et al., 2008). These authors based their conclusions
on temporal and spatial T-RFLP profiles of anoxygenic
phototrophic bacteria and sulfate-reducing bacteria in the
mat during a day–night cycle, but did not actually
observe migratory behavior of these organisms as they
did for the Cyanobacteria in that mat. HTS has not yet
been applied to investigate the question of migratory
behavior in microbial mats.
HTS based phylogenetic analysis of bacterial species in
a hypersaline microbial mat from the Cuatro Cienegas
Basin, Mexico revealed the abundance of Pseudomonads,
Sphingomonadales, and Sphingobacteria (Bonilla-Rosso
et al., 2012). This hypersaline mat from a red colored
desiccation pond was less diverse than a nearby lower
salinity green mat and had a low evenness in which a few
species already made up for more than 50% of the diversity. Genomic recruitment analysis showed that the cosmopolitan C. chthonoplastes is the most dominant
Cyanobacterium in this hypersaline microbial mat and
likely is the primary producer, which in relative low
numbers maintains high numbers of metabolically versatile heterotrophs. For instance, the dominant Pseudomonas sp. has broad catabolic and transport capabilities and
is also capable of forming biofilms (Davies et al., 1993).
Hot spring microbial mats
Hot springs and their geological and physicochemical features have been investigated since the 19th century. The
interest on their microbial inhabitants started in the
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Molecular ecology of microbial mats
1950s with the isolation of thermophilic bacteria (Marsh
& Larsen, 1953). Currently, there is much interest in hot
spring environments because enzymes of their inhabitants
may possess potential value for biotechnological applications. The hot spring microbial mats are natural model
systems for the study of life under extreme conditions
and for the presumed conditions under which life developed on early earth (Ward et al., 1998).
Hot spring environments often combine high temperature (between 50 and 91 °C) with an acidic pH and a
high sulfide concentration, which restrict the diversity of
life and limit it to Bacteria and Archaea. When compared
to other types of microbial mats, hot spring environments
often lack seasonality and only undergo irregular changes
in light intensities (except those related to the diurnal
cycle), temperature, pH, and sulfide concentrations. The
composition of microbial mats seems to be largely determined by temperature (Miller et al., 2009). The complexity of the community decreases with temperature and
vice versa. At higher temperature primary production is
largely covered by sulfide oxidation while oxygenic and
anoxygenic photosynthesis contribute considerably more
to primary production, which allows for the diversification of the mat community (Everroad et al., 2012).
Photosynthetic hot spring microbial mats can be produced by thermophilic Cyanobacteria (oxygenic mats), by
anoxygenic photosynthetic bacteria (Chloroflexus mats) or
acidophilic anoxygenic phototrophic bacteria (Chlorobium
mats) and by combinations thereof (Bateson et al., 1989;
Ward et al., 1998). Miller et al. (2009) found that either
Cyanobacteria or Chloroflexi dominated hot spring mats,
and these authors conceived that the two groups of
microorganisms compete for a limiting resource. Hot
springs are geographically isolated and therefore represent
dispersal barriers that resulted in the genetic isolation of
the microorganisms that form the microbial mats in these
environments (Miller et al., 2007; Takacs-Vesbach et al.,
2008; Lau et al., 2009).
Microbial mats in the Mushroom and Octopus Springs
in Yellowstone National Park (WY) have been used as
model system for more than three decades to decipher
the structure and function of microbial communities living in these high temperature habitats (Brock, 1978;
Ward et al., 1998, 2006; Ward & Castenholz, 2002; Klatt
et al., 2011, 2013a, b). These alkaline, low-sulfide mats
are dominated by two major bacterial phyla – Cyanobacteria and Chloroflexi – and additional abundant species of
the phyla Acidobacteria and Chlorobi (Klatt et al., 2013b).
The community composition, including the different ecotypes, changes while following the gradients of light and
chemical conditions without changing the functional
organization of the mat (Ramsing et al., 2000; Ward
et al., 2006). Notably, these authors did not observe
FEMS Microbiol Ecol 90 (2014) 335–350
341
migration of the (unicellular) organisms along the physicochemical gradients in the mat.
Sulfur-turf hot spring mats in Japan are dominated by
colorless sulfur bacteria of the Aquifex-Hydrogenobacter
group (Yamamoto et al., 1998). The rates of CO2 fixation
by these bacteria were more than one order of magnitude
higher than in Synechococcus sp. dominated hot spring
mats (Kimura et al., 2010). This may be related to the
fact that these organisms use the reductive tricarboxylic
acid cycle for carbon fixation (Hall et al., 2008). In
another type of hot spring microbial mat (alkaline, sulfidic, 65 °C), Kubo et al. (2011) identified three functional
groups of bacteria. Aerobic chemolithotrophic sulfide-oxidizing bacteria (Sulfurihydrogenibium) were situated at
the mat surface where they scavenged O2 allowing activity
of the anaerobic anoxygenic phototroph Chloroflexus and
the sulfate-reducing bacteria of the Thermodesulfobacterium/Thermodesulfatator group. These anaerobic bacteria
used H2 as the electron donor, produced by fermentation
of organic matter (Otaki et al., 2012). The sulfate-reducing bacteria provided sulfide for Sulfurihydrogenibium. No
results of HTS have been published and therefore the
microbial diversity of these hot spring mats is unknown.
Extensive microbial diversity analysis of the Mushroom
and Octopus Springs was performed using Sanger
based metagenomic shotgun sequencing and amplicon
sequencing of the 16S rRNA gene V3/V5 region using
pyrosequencing (Klatt et al., 2011). The unicellular Cyanobacterium Synechococcus sp. dominates the surface and
deep-green layers of mats located in the effluent channels
of the spring with temperatures between 71 and 75 °C (the
upper temperature) and 50 °C. Recruitment analysis
revealed the presence of two distinct populations of
Synechococcus sp. strains A and B’ that were previously
identified by DGGE analysis of the 16S RNA gene (Ward
et al., 2006). Analysis of different mat layers suggested that
different Synechococcus populations adapted to different
light conditions, temperature, and pH, which have steep
vertical gradients in the mats. Overall, the assembled
metagenomic sequences indicated the presence of six dominant chlorophototrophic populations and several novel
community members belonging to the Chlorobiales (Klatt
et al., 2011). Also a novel lineage of Chloroflexi was found
that include the previously described chlorophototroph
Candidatus Chloracidobacterium thermophilum (Bryant
et al., 2007). The distribution of microorganisms other
than Cyanobacteria also followed the vertical gradients in
the mats. The abundance of the anoxygenic phototroph
Roseiflexus increased with mat depth, while variations in
the number of Candidatus Chloracidobacterium appear to
be related to the temperature gradient (62–66 °C).
Liu et al. (2011) studied metatranscriptomic sequences
of Mushroom Spring from four samples taken at different
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
342
time points during a day–night cycle. Analysis of the 16S
rRNA gene fraction confirmed the dominance of four
bacterial taxa: Cyanobacteria, Chloroflexi, Chlorobi and Acidobacteria. Large changes in relative mRNA percentages
among different time points were observed for Cyanobacteria and Acidobacteria. The mRNAs assigned to Cyanobacteria were significantly less abundant than their
average rRNA content at sunset, and increased significantly in the morning when the mat was fully illuminated. The mRNAs affiliated to Acidobacteria were highly
abundant at sunset and in high-intensity light in the
morning and significantly lower at sunrise and low-intensity light. Earlier reports on the expression of Synechococcus spp. genes involved in photosynthesis and nitrogen
fixation (Steunou et al., 2008) during a day–night cycle
were confirmed.
Transcriptome analysis of Mushroom hot spring mat
samples taken at an hourly interval during a 24-h period
revealed that chlorophototrophic members of the Chloroflexi transcribe several photosynthesis related genes during
the night (Klatt et al., 2013a). These include genes encoding the type 2 photosynthetic reaction centers (pufLM),
genes encoding the chlorosome envelope and genes
involved in bacteriochlorophyll biosynthesis. Klatt et al.
(2013a) proposed that the Chloroflexi grew by fermenting
the cyanobacterial produced glycogen and synthesize bacteriochlorophyll and components of the photosynthetic
apparatus as well as polyhydroxyalkanoates and wax esters
that are used as carbon source during daytime. Whether
protein synthesis for the expressed genes is also initiated
in the dark or that the mRNA remains stable until transcription is still unknown.
Bird’s eye view on mat diversity
The recent studies using next generation sequencing techniques confirm the major building blocks of microbial
mats. Distinct functional groups find their optimal niche
in a narrow region along the geochemical gradients of a
mat leaving a colorful visible layering in the sand. These
groups and their function were already identified some
decades ago using for that time conventional methods
(Stal et al., 1985; van Gemerden, 1993). Today, HTS has
become the method of choice and in this fast moving
field of research the new techniques and technologies
continue to produce more DNA and RNA sequence data
with higher precision, longer read lengths, and greater
ease of producing long contigs or even full genomes can
be assembled from metagenomic data. The high taxonomic resolution with which the present day techniques
can assess microbial diversity warranted a reappraisal of
descriptive studies, but now provides a near complete
insight in the full diversity of microbial ecosystems.
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
H. Bolhuis et al.
Studying microbial mats with HTS techniques led to a
number of interesting discoveries. One of the major findings was the enormous species richness and diversity
especially in coastal and moderate saline microbial mats,
which are now considered to be among the most diverse
microbial ecosystems on earth. Mats in hot springs and
in hypersaline crystallizer ponds are characterized by a
much lower diversity, apparently because each of these
systems approach the environmental limits of which life
is possible. Microbial mats are dominated by bacterial
species, even in the most extreme environments, that are
otherwise dominated by Archaea (Casamayor et al.,
2002). Finally, the visually abundant Cyanobacteria are
often not numerically the most dominant species but they
remain the major group of primary producers that fuel
the ecosystem.
One of the recurring questions in microbial ecology is
how an ecosystem is capable of maintaining such high
species diversity, especially with respect to the rare biosphere tail of species distribution (Pedr
os-Ali
o, 2006,
2007). As niche theory predicts, areas of high resource
heterogeneity will support a large diversity of taxa
(Macarthur & Levins, 1967). Microbial mats harbor an
enormous geomorphological and biogeochemical heterogeneity with steep biogeochemical gradients along a
water-sediment-air interface that result in a large number
of potential ecological niches for microorganisms. Moreover, coastal microbial mats are, in contrast to the more
constant conditions in hypersaline and hot spring mats,
also exposed to strong fluctuations in environmental factors that generate ecological niches in a temporal manner.
It comes therefore as no surprise that coastal microbial
mats appear to be among the most diverse marine microbial ecosystem. Equal high diversities are found in hypersaline mats but only at salinities well below saturation.
The plethora in potential niches can be occupied by different functional groups of microorganisms from various
phylogenetic lineages (Bonilla-Rosso et al., 2012). Indeed,
microbial mats contain various unrelated species that
share the same trophic level or metabolic function (e.g.
the different types of sulfate-reducing and sulfide-oxidizing bacteria) but are able to colonize the mats and coexist
due to the large number of potential niches (Burke et al.,
2011). However, Armitage et al. (2012) argue that the
layering of different phyla in microbial mats is indicative
for phylogenetic clustering in these layers. These authors
propose as explanation ‘habitat filtering’, which predicts
that dominant species exhibit similar traits (Maire et al.,
2012). Habitat filtering and niche differentiation are not
necessarily exclusive and may both contribute to the
observed diversity. General ecological principles also predict that more extreme environments are less diverse
(Frontier, 1985). This certainly applies most prominently
FEMS Microbiol Ecol 90 (2014) 335–350
Molecular ecology of microbial mats
for Eukarya in aquatic hypersaline and thermal ecosystems but also for Bacteria and Archaea with only a few
dominant species remaining at extreme salinities (Benlloch et al., 2002; Casamayor et al., 2002). This trend is
also observed in thermal mats and hypersaline mats from
crystallizer ponds that have a much lower diversity and
species richness than moderate halophilic and intertidal
mats (Fig. 1). However, while the diversity at the higher
taxonomic levels (phyla, class, order) is low in extreme
environments, there appears to be a hidden higher ‘microdiversity’ of coexisting, closely related clones of microorganisms as was shown for coexisting unicellular
Cyanobacteria Synechococcus ecotypes (Melendrez et al.,
2011). High microdiversity reflects the large number of
available niches in a microbial mat that are occupied by a
limited number of species but at a large number of varieties (ecotypes).
Bacteria dominate the microbial mats that we discussed
here, while Archaea and Eukarya represent only a small
fraction of these mats. Archaea make up between 1% and
20% of microbial mat communities (Sievert et al., 2000;
L
opez-L
opez et al., 2013). This number reflects more or
less the abundance of Archaea in most ecosystems. For
example, the global ocean survey metagenomic analysis of
surface water yielded < 3% archaeal sequences, in soils
the estimates vary between 1% and 5%, whereas 20% has
been estimated for deeper marine waters DeLong & Pace,
2001; Yooseph et al., 2007). In hypersaline waters, the archaeal fraction can make up to 80% of the total biomass
(Benlloch et al., 2002). Remarkably, these large numbers
of Archaea are not found in hypersaline microbial mats
in the same ponds (Ley et al., 2006; Armitage et al., 2012;
Harris et al., 2013). Archaea that are present in microbial
mats are predominantly halophilic Archaea (haloarchaea)
and methanogens. Haloarchaea are well adapted to desiccation stress and are mainly involved in decomposition of
organic matter and are found in hypersaline mats and in
coastal mats after an extended period of high temperatures and draught (Bolhuis & Stal, 2011). Methanogenesis
may be important in microbial mats although net methane production is not likely because of the presence of
aerobic (Bacteria) and anaerobic (Archaea) methanotrophs (Ward, 1978; Potter et al., 2009). Due to the dominance of the sulfur cycle in many microbial mats,
methanogenic Archaea may occupy only the niche of
using noncompetitive substrates such as methylamines,
which cannot be used by sulfate-reducing bacteria (Kelley
et al., 2012).
Comparison of metagenome sequencing data confirms
that hypersaline and coastal microbial mats reveal a common phylogenetic pattern with respect to the different
phyla (Fig. 1). Cluster analysis of the phyla distribution
in the different mat samples shows high similarity
FEMS Microbiol Ecol 90 (2014) 335–350
343
between the freshwater hot spring mats and hypersaline
mats (Fig. 1a). Within the saline cluster, the hypersaline
red mat sample stands out from the rest as the result of a
lower diversity and domination (75%) by Proteobacteria.
While it has been previously assumed that microbial
(Cyanobacteria) mats are numerically dominated by
Cyanobacteria, molecular analysis consistently showed that
in fact Proteobacteria (especially the Alpha-, Gamma-, and
Delta-lineages) and Bacteroidetes are the dominant microorganisms (Fig. 1b). Proteobacteria are important for sulfur cycling, comprising the purple sulfur bacteria
(Gamma-clade: e.g. Chromatiales) and sulfate-reducing
bacteria (Delta-clade: e.g. Desulfobacterales and Desulfovibrionales). Alphaproteobacteria comprising the purple nonsulfur bacteria of the order Rhodobacterales are
abundantly present in coastal microbial mats, particularly
Rhodobacter spp. They are versatile photoheterotrophic
organisms that can adopt different trophic strategies and
therefore occupy various niches in the mats (Garrity
et al., 2005). Bacteroidetes are also mainly heterotrophic
organisms that are active in multiple layers in the mat
where they are important for carbon cycling (Harris
et al., 2013). Bacteroidetes are specialized in the decomposition of high-molecular-mass dissolved organic matter in
marine ecosystems (O’Sullivan et al., 2006). With an
average contribution to the microbial population density
of 10–20%, the Cyanobacteria may not be the most abundant group but they are undoubtedly the most prominent
primary producers. Filamentous Cyanobacteria are very
large when compared to most other Bacteria. Hence, in
terms of biomass Cyanobacteria are the dominant component of cyanobacterial mats (Dijkman et al., 2010).
Cyanobacteria are the pioneering organisms that colonize,
build and establish the microbial mat and are primarily
responsible for the flow of energy and elements. The
requirement of Cyanobacteria for light, N2 and CO2 positions them at the mat surface. As this position comes
with the toll of dehydration and exposure to UV, potential predators, and viral attack, Cyanobacteria protect
themselves by excreting excess fixed carbon as EPS. In
hypersaline and coastal mats, Firmicutes occur in similar
numbers as Cyanobacteria (Bolhuis & Stal, 2011; Harris
et al., 2013; L
opez-L
opez et al., 2013). Firmicutes
comprise among others Bacilli and Clostridiales and are
known as decomposers of organic matter and form heatand desiccation-resistant endospores that allow them to
survive conditions that prevent growth. Chloroflexi, Chlorobi, Acidobacteria, and Actinobacteria are present in lower
numbers in coastal and hypersaline mats, but may fulfill
an important role in carbon and sulfur cycling.
Hot spring mats are different and less diverse compared with hypersaline and tidal mats (Fig. 1). Terrestrial
hot springs are mostly low salinity systems. Salinity is one
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
H. Bolhuis et al.
344
(a)
(b)
(c)
Fig. 1. Comparison of the microbial diversity in hyperthermal, saline, and coastal microbial mats. (a) Chao index of the estimated richness
calculated at the genus level. (b) Stacked column graph representing the relative distribution of the dominant phyla in the different mats. For
visibility, this graph consists of the 12 most abundant phyla that together make up 95% of the total diversity. (c) Cluster analysis of the microbial
diversity at the genus level. The data were retrieved from MG-RAST (http://metagenomics.anl.gov) and consisted of metagenomic data sets. Data
sets used were Hot_mus: combined data from 4443745.3, 4443746.3 and 4443747.3 and 4443762.3 (Mushroom Springs, Yellowstone, Core A,
B, D and F), Hot_octo: 4443749.3 and 4443750.3 (Octopus Springs, Yellowstone, Core F and R), Salt_red: 4442467.3 (Red Mat Cuatro Cienegas
Coahuila, Mexico), Salt_green: 4441363.3 (Green Mat Pozas Azules Cuatro Cienegas Coahuila, Mexico), Salt_Guer: combined samples
4440964.3 (Guerrero Negro 0–1 mm) – 4440963.3 (Guerrero Negro 1–2 mm) – 4440965.3 (Guerrero Negro 2–3 mm) – 4440966.3 (Guerrero
Negro 3–4 mm) – 4440967.3 (Guerrero Negro 4–5 mm) – 4440969.3 (Guerrero Negro 5–6 mm) – 4440970.3 (Guerrero Negro 6–10 mm),
Coast_Tidal: 4548349.3 (Schiermonnikoog, the Netherlands, Tidal mat), and Coast_int: 4548350.3 (Schiermonnikoog, the Netherlands,
Intermediate mat). The data sets from Guerrero Negro were combined to cover the average depth of a microbial mat. The data sets from
Mushroom springs and Octopus Springs consist of the combined data from different cores.
of the major drivers of microbial diversity in coastal mats
(Bolhuis et al., 2013) and, hence, may also explain the
difference between hot spring mats and those thriving in
saline environments. The microbial community in various
hot spring mats forms a cluster separated from saline
mats (Fig. 1a). In addition to salinity, the low concentraª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
tions of sulfate in these hot springs are unfavorable for
sulfate-reducing bacteria and may explain the low
number of Deltaproteobacteria. Dillon et al. (2007) looked
at the dsrAB genes encoding for dissimilatory sulfite
reductase, a protein essential in the sulfate reduction
pathway and showed that a highly active sulfate-reducing
FEMS Microbiol Ecol 90 (2014) 335–350
Molecular ecology of microbial mats
community in the Yellowstone hot springs was composed
of only a few dsrAB genotypes that were especially active
near the surface during the switch from net photosynthetic to net respiratory conditions at the end of the day.
Hot spring mats maintain a similar functional layering of
photosynthetic Cyanobacteria combined with sulfur-oxidizing Chlorobi (green sulfur bacteria) and filamentous
anoxygenic phototrophic Chloroflexi. In particular, in the
sulfidic hot springs, sulfide-oxidizing Chloroflexi are present (Bryant et al., 2007, 2012; Klatt et al., 2013b).
Whereas in tidal and hypersaline mats, the sulfide production by sulfate-reducing bacteria is a consequence of
the high concentration of sulfate in natural seawater, the
sulfur cycle in the low-sulfide alkaline hot spring mats is
in most cases driven by sulfide from geothermal origin or
sulfide formed by sulfate-reducing bacteria in deeper layers in the sediment (Dillon et al., 2007; Smith et al.,
2010). Sulfide-oxidizing bacteria produce sulfate, which
drives the sulfate-reducing bacteria. Although Proteobacteria and Bacteroidetes are not among the dominant groups
in hot spring microbial mats, the gammaproteobacterial
genus Chromatium (Thermochromatium) is reported in
the top 10 in these systems (Klatt et al., 2013b).
The high number of sequences obtained per sample
gives insight in the rare biosphere, comprising species
that are represented in very low numbers. However, due
to the short read lengths and PCR error rates that still
haunt proper analysis of DNA sequences, it is difficult to
position rare genotypes and to attribute functions to
them. It has been postulated that the rare biosphere consists of microorganisms that have escaped predation or
viral lysis and may serve as a genetic pool that supplies
the community when conditions change. The importance
of the rare biosphere should not be underestimated
because it comprises a large proportion of the microbial
community as indicated by the long distribution tails in
rank-abundance curves (Pedr
os-Ali
o, 2007). Gobet et al.
(2012) studied the potential impact of the rare biodiversity by applying multivariate cutoff level analysis of HTS
data combined with geochemical data. Their analysis predicted that removing 50% of the rare biodiversity had a
major effect on the community structure but experimental proof is required to validate this prediction.
Future perspectives
Due to the tremendous HTS efforts during the past decade, the phylogenetic diversity of microbial communities
can be described to the near full extent. However, a large
fraction of the obtained sequences belong to uncultivated
species and the ecosystem function of these populations
can only be inferred from cultivated relatives. It is clear
that such inferences are highly uncertain. As a rule,
FEMS Microbiol Ecol 90 (2014) 335–350
345
microorganisms possess a versatile physiology and exhibit
remarkably different lifestyles. For example, the aerobic,
oxygenic phototrophic Cyanobacteria can adopt an anaerobic heterotrophic lifestyle by fermenting intracellular
organic carbon storage to small molecular weight organic
compounds that serve as substrate for other microorganisms in the microbial mat. Hence, in the closely packed
matrix of the mat these fermentation products realize a
flow of substrates to a range of terminal oxidizers such as
sulfate-reducing bacteria and Chloroflexi (Lee et al.,
2014).
Metagenomic analysis forms an important tool to
understand the true genetic and potential metabolic
diversity, which should be applied to microbial ecology.
To overcome the problems of assembly of short reads it
is advisable to produce and sequence large insert libraries
(e.g. fosmids/cosmids/BAC’s) or to use novel HTS techniques that generate long sequence read lengths (PacBio
or Nanopore technology). This would improve the assembly, annotation and identification of the metagenome and
facilitate a more detailed analysis.
Once a genetic backbone has been established, experiments can be designed that will give answers to ecological
relevant questions such as how the single components
interact and communicate to form a microbial mat as a
single entity and how it evolves. Systems biology aims to
fulfill the long coined promise to translate genetic diversity into function. This is accomplished by analyzing the
individual components as well as the combined processes
and interactions of an ecosystem in a holistic approach
(Karsenti et al., 2011). Combining chemical, biogeochemical, ecological, and genetic data using various techniques
will provide a broad picture of how individual organisms
operate, how they affect their ecosystem and how other
biological and abiotic processes respond to their activities.
A more accurate prediction can then be made about ecosystem response to fluctuating environmental conditions
and potential consequences of, for example, global
change. It also requires a multidisciplinary approach and
the integration of data from different techniques and
experimentation to understand each shackle in the chain
of events that occurs in a microbial mat. The full complexity of microbial mats is probably too large to comprehend with the current techniques and available
bioinformatics tools. One approach is to carry out experiments with simplified microbial mat communities. There
are two possible approaches. Synthetic microbial mat
communities can be created from a select number of
well-characterized and fully sequenced key species representing different functional groups. The degree of complexity of such synthetic microbial mats can be increased
depending on the research question. Another approach to
decrease the complexity is to generate minimal microbial
ª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
H. Bolhuis et al.
346
mats by diluting the natural community to a level that
still contains the basic characteristics of a microbial mat
(e.g. primary production by photoautotrophs, and intact
carbon, sulfur and nitrogen cycles). Obviously, these minimal microbial mat communities lack the rare biodiversity
but therefore might provide insight in its role, which is
still debated (Pedr
os-Ali
o, 2007; Chen-Harris et al., 2013;
Morgan et al., 2013). Artificial microbial mats that are
maintained and monitored for several years offer an ideal
model system to study the influence of environmental
factors on community composition and metabolic pathways. Under laboratory conditions, the mats can be
exposed to different levels of community complexity, different temperatures, light intensities, tidal cycles, or desiccation stress. Systems biology requires mathematical
modeling of the (metagenomics) data to help understanding the function of the microbial mat and the interactions
between its individual components and to predict the
effect of forcing on the system (Decker et al., 2005; Marx,
2012, 2013).
An additional challenge lies in the putative discrepancy
between present and active populations, which becomes
clear when analyzing RNA transcripts rather than DNA.
Microorganisms that appear to be dominant from 16S
rRNA gene analysis may be negligible when analyzing the
16S rRNA gene molecules. Transcriptomics provides
insight in the actual transcribed genes but also in a yet
unknown number of small RNA transcripts involved in
regulation (Gierga et al., 2012). Understanding the role
and action of these small regulatory RNAs is currently
restricted to single isolates but would greatly benefit from
synthetic microbial communities in which the total
amount of data can still be understood. Another factor
that complicates the picture is that RNA transcripts do
not necessarily translate into the respective of protein,
nor does it say anything about the actual activity of this
protein. A cascade of post-translational modifications and
the need for additional protein subunits and cofactors
makes it nearly impossible to predict the actual activity
and function and warrants the need of additional analyses
at the protein production and activity level such as proteomics and metabolomics. Eventually, there is still much
to learn about microbial ecosystems. The microbial systems ecology approach encompassing the enormous
amount of data through HTS and simultaneously evolving bioinformatics will be the way forward understanding
microbial mat communities as living entities.
Acknowledgements
The authors acknowledge support of the European Commission 7th Framework Programme project MaCuMBA
(Marine Microorganisms: Cultivation Methods for Improvª 2014 Federation of European Microbiological Societies.
Published by John Wiley & Sons Ltd. All rights reserved
ing their Biotechnological Applications) contract no.
311957.
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