Post on 19-Jan-2021
A year in the life of a thrombolite: comparativemetatranscriptomics reveals dynamic metabolicchanges over diel and seasonal cycles
Artemis S. Louyakis,1 Hadrien Gourl�e,1,2
Giorgio Casaburi,1 Rachelle M. E. Bonjawo,1
Alexandrea A. Duscher1 and Jamie S. Foster1*1Department of Microbiology and Cell Science,
University of Florida, Space Life Sciences Lab,
Merritt Island, FL, USA.2Department of Animal Breeding and Genetics,
Global Bioinformatics Centre, Swedish University of
Agricultural Sciences, Uppsala, Sweden.
Summary
Microbialites are one of the oldest known ecosys-
tems on Earth and the coordinated metabolisms and
activities of these mineral-depositing communities
have had a profound impact on the habitability of the
planet. Despite efforts to understand the diversity
and metabolic potential of these systems, there has
not been a systematic molecular analysis of the tran-
scriptional changes that occur within a living
microbialite over time. In this study, we generated
metatranscriptomic libraries from actively growing
thrombolites, a type of microbialite, throughout diel
and seasonal cycles and observed dynamic shifts in
the population and metabolic transcriptional activity.
The most transcribed genes in all seasons were asso-
ciated with photosynthesis, but only transcripts
associated with photosystem II exhibited diel cycling.
Photosystem I transcripts were constitutively
expressed at all time points including midnight and
sunrise. Transcripts associated with nitrogen fixa-
tion, methanogenesis and dissimilatory sulfate
reduction exhibited diel cycling, and variability
between seasons. Networking analysis of the meta-
transcriptomes showed correlated expression
patterns helping to elucidate how metabolic interac-
tions are coordinated within the thrombolite
community. These findings have identified distinctive
temporal patterns within the thrombolites and will
serve an important foundation to understand the
mechanisms by which these communities form and
respond to changes in their environment.
Introduction
Microbially induced mineralization is a critical process that
has helped shape Earth’s geological landscape (Dupraz
et al., 2009). This process occurs via the metabolic activi-
ties of lithifying microbial communities that facilitate the
precipitation and/or the entrapment of inorganic materials,
which together form depositional structures known as
microbialites (Burne and Moore, 1987; Reid et al., 2000;
Dupraz and Visscher, 2005; Dupraz et al., 2009). The abil-
ity of these microbialite-forming communities to alter their
biological and geological environments has enabled these
ecosystems to adapt to a wide range of environmental con-
ditions over the course of Earth’s history (Walter, 1994;
Grotzinger and Knoll, 1999). Microbialites are located
throughout the globe in a wide range of habitats (e.g.,
lacustrine, marine and hypersaline) and their ecological
complexity is just now beginning to be revealed through
advancements in high-throughput sequencing of the taxo-
nomic and functional gene diversity (Breitbart et al., 2009;
Khodadad and Foster, 2012; Petrash et al., 2012;
Bernhard et al., 2011; Mobberley et al., 2013; Russell
et al., 2014; Valdespino-Castillo et al., 2014; Sagha€ı et al.,
2015; Casaburi et al., 2016; Cerqueda-Garcia and Falcon,
2016; Chagas et al., 2016; Ruvindy et al., 2016; Warden
et al., 2016; White et al., 2016; Alcantara-Hern�andez et al.,
2017; Louyakis et al., 2017).
There are two major categories of microbialites, includ-
ing stromatolites, with well-laminated internal structures
(Walter, 1994; Reid et al., 2000) and thrombolites, which
have irregular clotted fabrics (Aitken, 1967; Kennard and
James, 1986). Although there has been extensive
research on the metabolisms and organisms associated
with formation of stromatolites (Visscher et al., 2000;
Visscher et al., 1998; Stolz et al., 2001; Burns et al., 2004;
Papineau et al., 2005; Havemann and Foster, 2008; Allen
et al., 2009; Baumgartner et al., 2006; Foster et al., 2009;
Foster and Green, 2011; Goh et al., 2011; Berelson et al.,
Received 9 September, 2017; revised 12 December, 2017;accepted 12 December, 2017. *For correspondence. E-mail jfos-ter@ufl.edu; Tel. 321-525-1047.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd
Environmental Microbiology (2018) 20(2), 842–861 doi:10.1111/1462-2920.14029
2013; Foster and Green, 2011; Khodadad and Foster,
2012; Pepe-Ranney et al., 2012; Mata et al., 2012; Casa-
buri et al., 2016; Ruvindy et al., 2016; Suosaari et al.,
2016), far fewer studies have examined thrombolite-
forming communities (Desnues et al., 2008; Planavsky
et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012;
Edgcomb et al., 2013; Mobberley et al., 2013; Gleeson
et al., 2015; Warden et al., 2016; Louyakis et al., 2017).
One ecosystem, where both microbialite types co-occur
and form calcium carbonate build-ups, is the island of
Highborne Cay, The Bahamas (Myshrall et al., 2010). In
these carbonate-based microbialites, it is the community
composition coupled with the type and rate of the metabo-
lism that alters the microenvironment, thereby creating
conditions that either drive the alkalinity towards precipita-
tion or dissolution of minerals (for review Dupraz et al.,
2009). For example, some metabolisms (e.g., oxic and
anoxygenic photosynthesis, and some forms of sulfate
reduction) increase the local pH thereby promoting condi-
tions for mineral deposition, whereas other metabolisms
(e.g., aerobic respiration, dissimilatory nitrate reduction
and sulfide-oxidation) can result in a net loss of carbonate
(Dupraz and Visscher, 2005; Visscher and Stolz, 2005). In
addition to these intrinsic factors driving local alkalinity, the
precipitation of carbonate is also dependent on the pres-
ence of exopolymeric substances (EPS). EPS material
creates an organic matrix within the microbialite-forming
mat community and serves as integral nucleation sites
(Braissant et al., 2007; Dupraz et al., 2009; Planavsky
et al., 2009). The negatively charged acidic groups within
the EPS matrix bind free Ca21, and through modification
or degradation by heterotrophic microbes, the Ca21 is
released. The local increase in free Ca21 and alkalinity
results in biologically induced mineralization within the mat
communities. However, the specific metabolic interactions
and underlying molecular pathways of these metabolisms
have not been fully elucidated in microbialites.
Despite similarities in the overall processes of biologi-
cally induced mineralization, Bahamian stromatolites
undergo an iterative growth process through microbial mat
cycling and environmental controls (Visscher et al., 2000;
Bowlin et al., 2012), whereas thrombolites form as a result
of the activity of bundles of calcified cyanobacteria fila-
ments, primarily the taxa Dichothrix spp. of the family
Rivulariaceae (Planavsky et al., 2009; Louyakis et al.,
2017). On the surface of the Bahamian thrombolite plat-
forms (Fig. 1A), there are numerous nodular mat
formations called ‘button’ mats (Fig. 1B) (Myshrall et al.,
2010; Mobberley et al., 2013) that are comprised of verti-
cally orientated Dichothrix spp. filaments (Fig. 1C). The
filaments serve as ‘hot-spots’ for carbonate deposition
(Fig. 1D) and previous isotopic analyses have revealed the
precipitate is derived from photosynthetic activity (Planav-
sky et al., 2009; Louyakis et al., 2017).
Although the taxonomic complexity and metabolic poten-
tial of thrombolite-forming communities is now emerging,
the transcriptional activities are not well known. Only a sin-
gle metatranscriptome analysis of a living microbialite has
been completed (Mobberley et al., 2015), and, although it
targeted the Bahamian thrombolites, it was limited to a sin-
gle time-point (Mobberley et al., 2015). The previous RNA-
Seq study revealed that, at noon, there was pronounced
transcriptional activity that formed a distinctive vertical
0
5
10
15
20
25
30
35
0 2 4 6 8
10 12 14 16
October November December January February March April May June July August September
16
14
12
10
8
6
4
2
0
Day
light
(h)
Temp Max
Temp Min
35
30
25
20
15
10
5
0
Temperature (˚C
)
October
Novem
ber
Decem
ber
January
February
March
April
May
June
JulyA
ugust
Septembe r
E
DC
BA
Fig. 1. Thrombolite of Highborne Cay, The Bahamas.
A. Thrombolite platforms located in the intertidal zone. Red box
reflected area magnified in (B). Bar, 1 m.
B. Thrombolite ‘button’-forming mats found on the surface of the
platforms. Green box represents area magnified in (C). Bar, 2 cm.
C. Cross section of ‘button’ mat depicting the extensive field of
vertically orientated Dichothrix filaments. Blue box reflects area
magnified in (D). Bar, 3 mm.
D. Light micrograph of Dichothrix bundles showing extensive
carbonate precipitation along the filaments. Bar, 1 mm.
E. Maximum and minimum air temperatures for the duration of
experiment from October 2013 thru September 2014. Grey area
reflects the hours of daylight during the same time period. Asterisks
indicate months in which the thrombolite-forming mats were
sampled. [Colour figure can be viewed at wileyonlinelibrary.com]
A year in the life of a thrombolite 843
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
gradient of metabolisms within the thrombolite-forming
mats (Mobberley et al., 2015), however, how these activi-
ties change over the diel cycle and throughout the year is
not yet known. In this study, we provide the first transcrip-
tional analysis of a lithifying microbial mat system
throughout the diel cycle replicated over the course of a
year. A comparative metatranscriptomic approach was
used to elucidate the activity of the microbial consortium
associated with the thrombolite of Highborne Cay. Overall,
this study provides important snapshots of the thrombolites
throughout the day and year to more fully characterize the
microbiome of these lithifying systems, as well as under-
stand how these communities interact and coordinate their
activities to drive the formation of these structures in an
ever-changing environment.
Results and discussion
To examine how the metabolic activities of thrombolite-
forming mats of Highborne Cay change over time, meta-
transcriptomic libraries were generated throughout the diel
cycle and replicated over the course of one year. The
thrombolite-forming button-mats from Site 4 (Andres and
Reid, 2006) were sampled every 6 h, for two consecutive
days, in the months of October (2013), March (2014) and
August (2014). The diel time points were chosen to reflect
the peak of photosynthetic (12:00) and respiration (00:00)
activities of the button-mat communities (Myshrall et al.,
2010) as well as the light/dark transitions at approximately
sunrise (06:00) and sunset (18:00). Although day length
did fluctuate between the different months (Fig. 1E), the
objective was to ensure a consistent sampling regime
between the treatments. The months were chosen as they
reflect the major seasonal transitions in The Bahamas, fall
(October), winter/early spring (March) and summer
(August) and previous studies have ascertained key envi-
ronmental changes (e.g., temperature, burial events,
photosynthetic active radiation) between these Bahamian
seasons (Bowlin et al., 2012). At the time of collection, the
water temperature was 288C in October 2013, 248C in
March 2014 and 308C in August 2014. The full dynamic
ranges of both water and air temperatures throughout
October 2013 to September 2014 are presented in
Fig. 1E.
In total, 24 libraries were constructed for four time-points
over three seasons with two biological replicates for each.
More than 1 billion reads were recovered and after quality
filtering, 687 million rRNA and 305 million mRNA reads
were retained (Table 1). Sequence data from all seasons
and time-points was assembled using Trinity v2.2.0 (Garber
et al., 2011). A total of 890 921 contigs were recovered with
reads mapped to them. Contigs were annotated to 13 983
KEGG Orthologies (KOs; Supporting Information Table
S1). Genes were considered differentially expressed (DE)
Table 1. Metatranscriptome data summary for four timepoints over three seasons sampled from Highborne Cay thrombolite-forming mats.
Sample Month Timepoint # of reads # of post-QC readsa % rRNA % remaining % alignedb
oct06_1 October 6:00 39609094 33689382 80.4 19.6 59.4
oct12_1 October 12:00 77945514 66083045 71.6 28.4 60.4
oct18_1 October 18:00 71433480 59414453 70.9 29.1 55.4
oct00_1 October 0:00 39379596 31584081 79.7 20.3 55.2
oct06_2 October 6:00 35044080 29260586 71.0 29.0 46.0
oct12_2 October 12:00 75711876 65456261 56.2 43.8 46.8
oct18_2 October 18:00 46851856 41098015 71.7 28.3 46.3
oct00_2 October 0:00 75785140 65230103 76.2 23.8 46.2
mar06_1 March 6:00 42286936 37108069 84.0 16.0 61.9
mar12_1 March 12:00 43962956 36332970 53.5 46.5 42.9
mar18_1 March 18:00 18040772 15057539 55.1 44.9 44.6
mar00_1 March 0:00 30358562 26183784 56.7 43.3 47.2
mar06_2 March 6:00 50796354 42258844 54.8 45.2 44.7
mar12_2 March 12:00 34809204 29685603 73.6 26.4 45.6
mar18_2 March 18:00 47205018 40876339 44.1 55.9 47.3
mar00_2 March 0:00 78846408 68104175 55.5 44.5 46.4
aug06_1 August 6:00 38970248 27423407 73.9 26.1 38.3
aug12_1 August 12:00 31367092 25207290 86.9 13.1 56.1
aug18_1 August 18:00 63575558 46390757 69.7 30.3 41.3
aug00_1 August 0:00 46398488 31001238 71.3 28.7 32.1
aug06_2 August 6:00 51792988 42670313 74.7 25.3 53.8
aug12_2 August 12:00 55154298 46063430 75.8 24.2 59.5
aug18_2 August 18:00 55090470 45178031 81.3 18.7 68.6
aug00_2 August 0:00 49895344 41203480 82.5 17.5 64.0
a. Quality trimming completed using Trimmomatic within the Trinity pipeline.b. Percents are non-rRNA portion of raw reads aligned to Trinity assembly using Bowtie2.
844 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
with p-values�0.01 through a NoiSEQBio DE analysis.
Mapping of reads to assembled contigs resulted in low
overlap between diel time points and seasons. The paucity
of sequenced genomes and metagenomes from these sys-
tems does represent a limitation with regard to annotation
and assembly. Additionally, we also acknowledge that only
a single year is represented in this study and that there
may be year-to-year variation within the Highborne Cay
ecosystem. Despite this potential caveat, the differences
between the time points do suggest that the transcriptional
activities within mat community are dynamic throughout the
day and year.
Two replicate metatranscriptomes were generated for
each time point and reproducibility was assessed using
Pearson and Spearman’s rank correlation coefficients.
Pearson correlations for biological replicates ranged from
0.061 to 0.793, with a mean of 0.487 and all p-value
<10211, and Spearman’s rank correlation ranged from
0.296 to 0.506, with a mean of 0.446 and all p-value
<10211 (Supporting Information Table S2). The standard
Pearson correlation for technical replicates of a transcrip-
tome is� 0.9, while no standard has yet been devised for
biological replicates (Conesa et al., 2016). Although efforts
were made to collect samples from across the thrombolite
platform in Site 4 (Fig. 1A), the lower correlation values
likely reflect some spatial heterogeneity within the
thrombolite-forming community. Despite the potential het-
erogeneity, distinctive, reproducible trends were observed
throughout the thrombolite-forming community over time.
Taxonomic dynamics within thrombolite-forming buttonmat community
During library construction, the samples underwent rRNA
depletion to help improve mRNA recovery. To test whether
rRNA depletion would have a biasing effect on the recov-
ered taxonomic data, a parallel study was completed using
a previously published dataset from samples collected and
handled identically with the exception that one was rRNA
depleted and the other was not (NCBI Bioproject ID:
PRJNA261361) (Mobberley et al., 2015). A Kruskal-Wallis
test (p>0.319) showed no significant difference between
rRNA depleted and total RNA samples suggesting the
depletion step did not significantly alter the relative taxo-
nomic abundances. Additionally, Pearson and Spearman
tests showed strong linear correlation (0.965 and 0.892
correlation coefficients, respectively, p-values< 10216),
also suggesting that depletion has no distinguishable effect
on community structure.
The metatranscriptome data was mined for 16S rRNA
transcripts to compare relative abundances of bacteria and
archaea within the community over diel and seasonal
cycles. The use of rRNA transcript levels as a proxy for
microbial activity does have limitations due to differences
in growth rates, life strategies and environmental condi-
tions (Blazewicz et al., 2013). However, rRNA-based
analyses can yield important insight into the community
dynamics and provide qualitative information about those
taxa that have the potential for protein synthesis and how
they fluctuate over time. Together, analyses identified a
total of 23 bacterial phyla, three archaeal phyla and an
additional 26 candidate phyla that exhibited enriched rRNA
transcript expression within the mats over the observed
diel and seasonal cycles. A cladogram and heat map
depicting the differential expression of the 16S rRNA
genes for each of the time points revealed extensive
changes in transcript abundance throughout the day and
between seasons with the node size reflecting the total
16S rRNA transcript number for a given taxa (Fig. 2). Of
the many phyla represented within the thrombolite
communities the most diversely represented within the
metatranscriptomes were the Proteobacteria, Bacteroi-
detes and Cyanobacteria regardless of season (Fig. 2). A
detailed list of those taxa with the highest levels of 16S
rRNA transcripts through the diel cycle and seasons are
presented in Table 2, Supporting Information Fig. S2 and
Table S3. Although Proteobacteria (specifically Gammapro-
teobacteria, Alphaproteobacteria and Deltaproteobacteria)
populations represented the most diverse assemblage of
taxa, the individual taxa that were the most enriched
within the thrombolite-forming mat transcriptomes
were the Cyanobacteria (Table 2, Supporting Information
Table S3).
Regardless of season and time of day, the cyanobacte-
rium with the highest level of transcripts within the
community shared sequence similarity with the Xenococ-
caceae, a family of coccoid cyanobacteria within the Order
Pleurocapsales. This result was visually confirmed with
light- and epifluorescence microscopy, where coccoid cya-
nobacteria were observed in high numbers in the upper cm
of the thrombolite-forming mats (data not shown). This
taxon, along with Pseudoanabaenaceae, which were also
highly represented within the metatranscriptome (Table 2),
are abundant in other lithifying mat systems, such as
Shark Bay, Western Australia (Goh et al., 2009; Suosaari
et al., 2016), Lake Alchichica, Mexico (Ka�zmierczak et al.,
2011) and in Lake Van, Turkey (Kazmierczak et al., 2009).
Coccoid cyanobacteria have been shown to have impor-
tant roles in lithifying systems, as carbonate precipitation
initiates near clusters of coccoid cyanobacteria in some
hypersaline mats (Dupraz et al., 2004). Additionally,
numerous microfossils, dating back to the Archean, have
been interpreted as calcified, colonial coccoid cyanobacte-
ria (Kazmierczak and Altermann, 2002; Altermann et al.,
2006) suggesting their importance in biologically induced
mineralization within the thrombolites.
Unsurprisingly, another highly represented cyanobacte-
rial taxa within the recovered metatranscriptome was the
A year in the life of a thrombolite 845
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
family Rivulariaceae, which harbors the filamentous
Dichothrix spp., known to serve as hot spots for carbon-
ate precipitation within the thrombolites (Planavsky
et al., 2009; Louyakis et al., 2017). Much like the coc-
coid cyanobacteria, the filamentous strains exhibited
very high numbers of transcript levels regardless of sea-
son. Although the cyanobacteria transcript levels peaked
at noon, high levels of 16S rRNA transcripts were
observed at night (Table 2). The accumulation or main-
tenance of rRNA content during the dark periods has
been observed in numerous cultured cyanobacterial
studies (Lepp and Schmidt, 1998; Zinser et al., 2009;
Beck et al., 2014) and may provide the cyanobacteria
with a competitive advantage to respond quickly during
the onset of the light period in the thrombolite
communities.
BA
C
Archaea
Archaea
Bacteria
Bacteria
γ
α
α
γ
δ δ
γ
α
δ
α
γ
γγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγγ
ααααααααααααααααααα
δδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδδ
Archaea
Bacteriaα
γ
δ
Fig. 2. Composition of thrombolite active taxa as visualized with GraPhlAn. Cladograms represent metatranscriptome bacterial and archaealdiversity for each season,
A. October 2013, (B) March 2014 and (C) August 2014. Cladogram was constructed using total abundances for the seasons with size of
nodes increasing with abundance and colored by phylum. The innermost node (white) represents domain, then phylum, class, order, family,
genus and species on the outermost node. Outer rings represent relative abundance heatmaps for each time-point, innermost is 06:00 (red),
then 12:00 (green), 18:00 (orange) and 00:00 (blue) on the outside. [Colour figure can be viewed at wileyonlinelibrary.com]
846 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Tab
le2.
Rela
tive
abundance
of
recove
red
16S
rRN
Atr
anscri
pts
(means
show
n)
within
thro
mbolit
es
ove
rdie
land
seasonalcycle
s.
Tota
lTra
nscripts
per
Mill
ion
Octo
ber
06:0
0O
cto
ber
12:0
0O
cto
ber
18:0
0O
cto
ber
00:0
0P
hylu
mC
lass
Ord
er
Fam
ily
471786
100352
129120
149798
92516
Cyanobacte
ria
Oscill
ato
riop
hycid
eae
Chro
ococcale
sX
enococcaceae
422906
124770
69827
87993
140317
Cyanobacte
ria
Nosto
cophycid
eae
Stigonem
ata
les
Riv
ula
riaceae
398288
141842
52996
58360
145090
Cyanobacte
ria
Nosto
cophycid
eae
Nosto
cale
sN
osto
caceae
270337
94635
35655
41652
98394
Cyanobacte
ria
Synechococcophycid
eae
246991
84135
35088
41728
86040
Cyanobacte
ria
Nosto
cophycid
eae
Nosto
cale
s
242077
73127
38795
50385
79769
Cyanobacte
ria
Synechococcophycid
eae
Pseudanabaenale
sP
seudanabaenacea
e
188047
35170
59275
60406
33197
Pro
teobacte
ria
Alp
hapro
teobacte
ria
Rhodobacte
rale
sR
hodobacte
raceae
184577
44170
33857
72400
34150
Cyanobacte
ria
Oscill
ato
riop
hycid
eae
Chro
ococcale
s
160582
43984
30519
36718
49362
Cyanobacte
ria
76399
8070
18973
39575
9781
Pro
teobacte
ria
Deltapro
teobacte
ria
Myxococcale
s
67910
16515
27914
8729
14752
Pro
teobacte
ria
Alp
hapro
teobacte
ria
Rhiz
obia
les
Hyphom
icro
bia
ceae
60652
11838
19604
17986
11225
Pro
teobacte
ria
Alp
hapro
teobacte
ria
44223
3066
9659
28937
2561
Cyanobacte
ria
Oscill
ato
riop
hycid
eae
Oscill
ato
riale
sP
horm
idia
ceae
43677
6986
12938
16185
7569
Chlo
roflexi
Anaero
lineae
SB
R1031
A4b
41721
6142
15348
11002
9229
Bacte
roid
ete
s[S
apro
spirae]
[Sapro
spirale
s]
Sapro
spiraceae
38867
11817
11684
7627
7739
Cyanobacte
ria
Synechococcophycid
eae
Pseudanabaenale
sP
seudanabaenacea
e
34210
8815
8697
9152
7546
Pla
ncto
mycete
sP
hycis
phaera
eP
hycis
phaera
les
28062
6092
8043
7688
6239
Pla
ncto
mycete
sP
lancto
mycetia
Pirellu
lale
sP
irellu
laceae
26449
4659
8707
8012
5071
Pro
teobacte
ria
Deltapro
teobacte
ria
Myxococcale
sO
M27
Tota
lTra
nscripts
per
Mill
ion
Marc
h06:0
0M
arc
h12:0
0M
arc
h18:0
0M
arc
h00:0
0P
hylu
mC
lass
Ord
er
Fam
ily
699229
161877
193626
171318
172407
Cyanobacte
ria
Oscill
ato
rio
phycid
eae
Chro
ococcale
sX
enococcaceae
205100
46561
48116
58289
52134
Pro
teobacte
ria
Alp
hapro
teobacte
ria
Rhodobacte
rale
sR
hodobacte
raceae
190576
38152
56897
48108
47419
Cyanobacte
ria
Oscill
ato
rio
phycid
eae
Chro
ococcale
s
187291
66563
55305
42924
22498
Cyanobacte
ria
Nosto
cophycid
eae
Stigonem
ata
les
Riv
ula
riaceae
153678
58452
44890
33011
17324
Cyanobacte
ria
Nosto
cophycid
eae
Nosto
cale
sN
osto
caceae
137779
44032
42180
28172
23396
Cyanobacte
ria
Synechococcophycid
eae
Pseudanabaena
les
Pseudanabaenacea
e
110081
42697
31578
23601
12204
Cyanobacte
ria
Synechococcophycid
eae
108551
21519
34193
25054
27784
Pro
teobacte
ria
Deltapro
teobacte
ria
Myxococcale
s
103295
40250
28379
22655
12011
Cyanobacte
ria
Nosto
cophycid
eae
Nosto
cale
s
96388
19946
23715
30490
22237
Pro
teobacte
ria
Alp
hapro
teobacte
ria
77839
12260
17150
28055
20373
Pla
ncto
mycete
sP
lancto
mycetia
Pirellu
lale
sP
irellu
laceae
74885
25602
20515
19265
9502
Cyanobacte
ria
65301
16617
17808
15958
14918
Cyanobacte
ria
Synechococcophycid
eae
Pseudanabaena
les
Pseudanabaenacea
e
63135
17221
12648
15682
17582
Pro
teobacte
ria
Alp
hapro
teobacte
ria
Rhiz
obia
les
Hyphom
icro
bia
ceae
53011
12636
11410
11411
17554
Cyanobacte
ria
Oscill
ato
rio
phycid
eae
Chro
ococcale
sC
yanobacte
riaceae
48757
12677
11162
9677
15241
Chlo
roflexi
Anaero
lineae
SB
R1031
A4b
48450
10228
10674
11782
15765
Bacte
roid
ete
s[S
apro
spirae]
[Sapro
spirale
s]
Sapro
spiraceae
47608
12837
12355
10712
11703
Pro
teobacte
ria
Deltapro
teobacte
ria
Myxococcale
sO
M27
47133
9663
13716
13186
10569
Pla
ncto
mycete
sP
hycis
phaera
eP
hycis
phaera
les
A year in the life of a thrombolite 847
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Other highly represented taxa within the thrombolite
metatranscriptomes included the alphaproteobacterial
families Rhodobacteraceae and Rhizobiales, deltaproteo-
bacterial order Mycoccocales and the Planctomycetes
order Phycisphaerales, which are common in a wide range
marine ecosystems and are thought to play important roles
in biochemical cycling (Baker et al., 2015; Simon et al.,
2017). These taxa exhibited relatively consistent expres-
sion of rRNA throughout the diel cycle and over the course
of the year (Table 2, Supporting Information Table S3).
Those enriched taxa that did exhibit seasonal changes
in 16S rRNA expression were the Bacteroidetes, Chloro-
flexi and Firmicutes. In the Bacteroidetes, the families
Saprospiraceae, Cytophagaceae and the cluster BME43
exhibited increased relative abundance in March and Octo-
ber. Heterotrophic Saprospiraceae are known to hydrolyse
complex carbon compounds and may be contributing to
the degradation of exopolymeric substances (Fernandez-
Gomez et al., 2013; McIlroy and Nielson, 2014). The deg-
radation of EPS is an essential process to release calcium
ions during the precipitation of calcium carbonate (Dupraz
et al., 2009) and have been reported in a wide range of lith-
ifying hypersaline mat systems (Mobberley et al., 2012;
Schneider et al., 2013). In the warmer month of August,
however, there was an overall decrease in the representa-
tion of Bacteroidetes, with the exception of the family
Flammeovirgaceae, which has been shown to exhibit ther-
motolerance (Tang et al., 2016) and form associations with
cyanobacteria in marine ecosystems, such as corals (Den
Uyl et al., 2016).
There were also seasonal changes in the orders of the
Chloroflexi and Firmicutes (Table 2). Among the Chloro-
flexi, the anoxic non-phototrophic Anaerolineae (Class
SBR1031) were enriched in March and October, but lower
in August, whereas the photoheterotrophic Chloroflexales
(Candidate family Chlorothrixaceae) were enriched in
August. These differences in the relative abundance may
simply reflect differences in light levels throughout the
year. In the Firmicutes, the families Bacillaceae and Plano-
coccaceae were also enriched at sunset and midnight in
August (Table 2) and have been isolated from a wide range
of extreme environments including salterns, permafrost,
hyperalkaline lakes and microbial mats (Reddy et al.,
2002; Sorensen et al., 2005). Members of both families
have been shown to have a high tolerance to elevated UV
and salt conditions (Ordonez et al., 2009; Paul et al., 2015;
See-Too et al., 2017) and the increased relative abun-
dance in August may reflect resistance to elevated UV
radiation and desiccation stress in the thrombolites during
the summer.
Although Bacteria dominated the metatranscriptome at
all time points and seasons, Archaea transcripts were
recovered with similarity to three phyla, the methanogenic
Euryarchaeota, ammonia-oxidizing Crenarchaeota and theTab
le2.
cont.
Tota
lTra
nscri
pts
per
Mill
ion
August
06:0
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ugust
12:0
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ugust
18:0
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ugust
00:0
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hylu
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lass
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er
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ily
603314
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nobacte
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ococcale
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enococcaceae
436844
144003
201465
53328
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Cya
nobacte
ria
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ato
riop
hycid
eae
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ococcale
s
328293
14328
2687
213623
97656
Fir
mic
ute
sB
acill
iB
acill
ale
sB
acill
aceae
326312
11124
2960
223642
88587
Pro
teobacte
ria
Gam
mapro
teobacte
ria
Ente
robacte
riale
sE
nte
robacte
riaceae
236338
44024
13856
40771
137687
Cya
nobacte
ria
Nosto
cophycid
eae
Stigonem
ata
les
Riv
ula
riaceae
193247
56177
69714
20780
46576
Cya
nobacte
ria
Synechococcophycid
eae
Pseudanabaenale
sP
seudanabaen
aceae
137630
62496
48752
8005
18376
Cya
nobacte
ria
Synechococcophycid
eae
Pseudanabaenale
sP
seudanabaen
aceae
130254
17075
7593
38773
66813
Cya
nobacte
ria
Nosto
cophycid
eae
Nosto
cale
sN
osto
caceae
81215
23799
18666
7107
31642
Cya
nobacte
ria
77846
23864
18918
19758
15307
Pla
ncto
mycete
sP
hycis
phaera
eP
hycis
phaera
les
72014
9553
5003
18148
39310
Cya
nobacte
ria
Nosto
cophycid
eae
Nosto
cale
s
68374
26915
27687
3530
10241
Pro
teobacte
ria
Alp
hapro
teobacte
ria
68177
25647
19291
9740
13499
Pro
teobacte
ria
Deltapro
teobacte
ria
Myxococcale
s
57704
20834
19602
1564
15704
Pro
teobacte
ria
Alp
hapro
teobacte
ria
Rhodobacte
rale
sR
hodobacte
race
ae
49418
7442
3577
12960
25437
Cya
nobacte
ria
Synechococcophycid
eae
44860
7166
3694
25487
8512
Chlo
roflexi
Chlo
roflexi
Chlo
roflexale
s[C
hlo
roth
rixaceae]
44710
15155
21369
4785
3401
Cya
nobacte
ria
Oscill
ato
riop
hycid
eae
Chro
ococcale
sC
yanobacte
riaceae
43108
1404
355
27268
14081
Fir
mic
ute
sB
acill
iB
acill
ale
sP
lanococcaceae
30239
8098
4768
13410
3963
Bacte
roid
ete
sC
yto
phagia
Cyto
phagale
sF
lam
meovirgaceae
30142
9551
7905
3376
9311
Pla
ncto
mycete
sP
lancto
mycetia
Pirellu
lale
sP
irellu
laceae
848 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
newly proposed archaeal phyla Parvarchaeota (Rinke
et al., 2013) (Supporting Information Table S3). Archaea
had very low transcriptional activity in all seasons com-
pared to Bacteria, but were highest in August, comprising
0.138% of recovered rRNA transcripts. Of the three phyla,
the Parvarchaeota were the most abundant subgroup and
the orders YLA114 and WCHD3–30 have been observed
in other microbial mat systems including the hypersaline
smooth mats of Shark Bay, Western Australia (Wong et al.,
2017) and geothermal spring microbialite-forming mats in
Romania (Coman et al., 2015), but their role within these
systems is currently unknown.
Metabolic activity of thrombolite-forming community
Functional genes grouped by KEGG Orthology (KO) classi-
fications were compared using principle component
analysis (PCA, Euclidian distance) based on normalized
and batch corrected using the package RNA-Seq by Expec-
tation Maximization (RSEM) estimated counts (Fig. 3; Li
and Dewey, 2011). When all functional genes were consid-
ered, PCA plots showed no significant clustering based on
time of day (Fig. 3A) or season (Fig. 3B), suggesting that
the majority of functional genes within the thrombolites are
constitutively expressed. This assessment was confirmed
when the number of unique gene counts grouped by KOs
were compared, as depicted in the Venn diagrams (Fig. 3C
and D), revealing that the majority of unique genes were
expressed at all time points (KO 5 4898) and during all
three seasons (KO 5 6013). There were, however, unique
gene counts associated with each diel time point with the
highest number recovered from the midnight samples
(KO 5 773) followed by noon (KO 5 556). With respect to
the seasons, March (KO 5 1063) had the highest number of
unique gene counts followed by August (KO 5 703), sug-
gesting that some pathways within the thrombolite-forming
communities do differentially respond to changes in their
environment over diel and seasonal cycles.
To begin to elucidate those pathways that might be dif-
ferentially expressed over time, several key metabolisms
were examined that have been typically associated with
the promotion or dissolution of carbonate (Visscher and
Stolz, 2005; Dupraz et al., 2009). These metabolisms
include photosynthesis, nitrogen fixation, denitrification,
methanogenesis and dissimilatory sulfate reduction
(Fig. 4). The data were normalized for within sample
counts (transcripts per million, TPM), across samples
(trimmed mean of M-values, TMM) and batch corrected,
allowing for comparisons between times and seasons.
These normalized data were used for all downstream anal-
yses. The specific genes used for these metabolic
comparisons are listed in Supporting Information Table S4.
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Fig. 3. Relationships between seasonsand diel time-points.
Principle component analysis of
thrombolite metatranscriptomes based on
distribution of KEGG Orthology colored by
(A) time or (B) season. Venn diagrams
displaying unique genes counts grouped
by KEGG Orthology (C) time comparisons
between seasons and (D) comparisons
within each season. [Colour figure can be
viewed at wileyonlinelibrary.com]
A year in the life of a thrombolite 849
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Expression of photosynthetic pathways during diel andseasonal cycles
For photosynthesis, the transcripts were differentiated based
on Photosystem I (PSI) and II (PSII) and showed pro-
nounced differences (Fig. 4). Transcripts associated with
PSII genes exhibited a pronounced diel cycle with expres-
sion increasing rapidly after sunrise and peaking at noon,
mirroring many previous studies in both cyanobacterial cul-
tures and non-lithifying microbial mat communities (Fig. 4;
Steunou et al., 2008; Ludwig and Bryant, 2011). For exam-
ple, in cyanobacteria, the expression of psbA gene, which
encodes for the D1 reaction centre protein, has long been
known to respond to the quality and quantity of light (Kulkarni
and Golden, 1994). Genes for PSII (e.g., psbA, psbC, psbD,
psbF and psbE) and photosynthesis antenna proteins (e.g.,
apcD) were some of the most highly expressed transcripts
at noon regardless of the season (Fig. 5). Although PSII
associated gene expression levels drop precipitously at sun-
set and midnight, PSII-associated transcripts were still
present in high numbers (Fig. 4). At night, psbA transcripts
have been shown to be more stable with a half-life of approx-
imately 7 h and play a role in regulating D1 production at the
translational level (Mohamed and Jansson, 1991). Addition-
ally, some diazotrophic coccoid cyanobacteria isoforms of
psbA are transcribed at night to form nonfunctional D1 pro-
teins thereby preventing oxygen evolution during the
nitrogen fixation period (Wegener et al., 2015).
In contrast, transcripts associated with PSI showed no
diel cycling and maintained a relatively high, but constant,
level of expression even at night. Several studies using cul-
tured cyanobacteria have shown that under high light
intensity almost all of the PSI genes are downregulated
(Hihara et al., 2001; Ludwig and Bryant, 2011) to lower the
susceptibility of the cells to damage (Hihara et al., 1998),
giving the appearance of no diel rhythmn. Cyanobacteria
often regulate their PSII/PSI ratios to rapidly adjust to ever
changing cellular redox states and maintain a balance
between CO2 fixation and generation of reductants to
avoid excess production of reactive oxygen and nitrogen
species, which can cause photooxidative damage to the
cells (Ludwig and Bryant, 2011).
Cyanobacteria have also evolved efficient mechanisms
to acquire the inorganic carbon needed for photosynthesis.
Genes associated with carbon concentrating mechanisms
(CCM) were identified in the thrombolite-forming communi-
ties and some exhibited differential diel expression. The
complete family of genes encoding CCM proteins
(ccmKLMNOP) were recovered but exhibited no differential
expression between the treatments, suggesting no pro-
nounced diel or seasonal cycling. Genes associated with
NADPH dehydrogenase (e.g., ndhD, ndhM, ndhN) were
enriched at noon in all seasons (Fig. 5) and, in cultures,
have been shown to be associated with CO2 uptake and
are upregulated under high light and low CO2 conditions
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Fig. 4. Diel and seasonal cycling of major pathway gene expression presenting combined normalized abundances for all the genes in eachmetabolism.
October (green, dotted line); March (red, dotted circles); August (blue, solid line). [Colour figure can be viewed at wileyonlinelibrary.com]
850 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
(Hihara et al., 2001). Although not typically present in
marine cyanobacteria, transcripts associated with the high
affinity bicarbonate transport system cmpABCD peaked at
dawn (Badger and Price, 2003), albeit at low expression
levels. A low affinity SulP-type bicarbonate transporter,
bicA, exhibited higher expression levels peaking at 06:00
with the highest expression in March (Fig. 5). This trans-
porter has been identified across a range of marine
cyanobacteria and BicA has been shown to act indepen-
dently in the uptake of bicarbonate, simplifying the process
of carbon concentration within the cyanobacteria carboxy-
some (Price et al., 2004). The increased expression of
bicA at dawn has been observed in several Microcystis
strains as well as non-lithifying mats (Jensen et al., 2011;
Sandrini et al., 2016), and may be light-dependent (Price
et al., 2010). Further examination of these contigs may
reveal novel homologs of the bicA gene within the
thrombolite-forming communities.
In addition to oxygenic photosynthesis, anoxygenic pho-
tosynthesis can locally increase the pH thereby creating a
microenvironment that is favorable to promoting carbonate
precipitation. Evidence for diel cycling of anoxygenic photo-
synthesis was also observed as genes encoding the
photosynthetic reaction centre (puf operon) and bacterio-
chlorophyll biosynthesis (bchC, bchG), which are common
to a diverse group of anoxygenic phototrophs from the Pro-
teobacteria and Chloroflexi, were primarily expressed at
midnight (Fig. 5). Specifically, pufL and pufM, which
encode for the light and medium polypeptide genes of the
reaction centre respectively, as well as the pufC gene,
which encodes the cytochrome c subunit of the complex,
were significantly expressed at midnight regardless of sea-
son (Fig. 5). A similar trend of the enrichment of transcripts
associated with anoxygenic phototrophy at night has been
observed in both cultured Roseobacters (Yurkov and
Beatty, 1998) and in coastal marsh systems (Gifford et al.,
2014). Evidence with cultured strains indicates that the
transcription of the puf operon is light sensitive, but not
oxygen sensitive (Nishimura et al., 1996) suggesting the
differential expression of the puf operon genes at night is a
strategy to avoid photo-induced damage within the
thrombolites.
Nitrogen cycling within thrombolite-forming mats
With regard to nitrogen cycling within the thrombolite-
forming mats, metatranscriptome analysis revealed a pro-
nounced diel cycling of nitrogenase transcripts suggesting
that nitrogen fixation is the primary N source for the throm-
bolite communities. In August and October, the overall
expression of the Mo-nitrogenase structural genes (nifD,
nifH, nifK) peaked at 18:00, whereas in March, these
genes peaked at midnight (Fig. 4). Additionally, several
transcripts encoding nitrogenase FeMo cofactors (nifB)
and stabilizing proteins (nifW) were highly differentially
expressed at midnight regardless of season (Fig. 5). These
differences in nitrogenase reductase expression corre-
spond to non-lithifying cyanobacterial mats from
Mushroom Spring in Yellowstone and may reflect the
increase accumulation of fixed carbon during August and
October where the days are longer, thereby providing
more available energy for the onset of nitrogenase tran-
scription (Steunou et al., 2008). In the thrombolites, the
majority of the nitrogenase transcripts were produced by
coccoid (Order Chroococcales) and filamentous (Order
Nostocales) cyanobacteria regardless of season, although
there were differences in the diel peak of transcripts. The
nitrogenase transcripts derived from the coccoid cyano-
bacteria were enriched at both 18:00 and 00:00, whereas
nif transcripts derived from the filamentous cyanobacteria
peaked primarily at midnight. The H2 generated from the
nitrogen fixation may be recovered by the cyanobacteria
through the presence of a bidirectional hydrogenase (hox)
that is highly expressed at night (Fig. 5).
The metatranscriptomic results indicated that the pri-
mary sink for N in the thrombolite community was
assimilatory nitrate reduction, as transcript levels for deni-
trification (< 700 TPM) and ammonium oxidation (< 300
TPM) were very low at all time points and seasons. It is
possible that the sampling regime did not fully capture diel
transcriptional activity, as transcripts associated with deni-
trification pathways (e.g., nirS, nosZ, norC), although
relatively stable by mRNA standards, typically have a half-
life extending to only 30 min (Hartig and Zumft, 1999).
There was, however, an observable trend of an increase in
transcripts associated with denitrification at 00:00 and
06:00, but it was not statistically significant (Fig. 4; see
Table S4 for complete list). Overall, these results suggest
that denitrification and ammonium oxidation do not repre-
sent primary sinks for N within the thrombolite
communities.
Expression of anaerobic respiration metabolisms
Although photosynthesis and nitrogen fixation processes
dominated the metatranscriptomes, there was evidence of
a diverse array of respiration processes, including metha-
nogenesis and dissimilatory sulfate reduction. For
example, transcripts related to methanogenesis were
recovered from several Archaea and exhibited diel and
seasonal trends (Fig. 2, Supporting Information Table S2).
In August, transcript levels were highest at 00:00, whereas
in October and March the highest number of methanogen-
esis transcripts were recovered at 06:00 (Fig. 4;
Supporting Information Table S4). The majority of
recovered methanogenic Euryarchaeota within the
thrombolite-forming mats appeared to be methylotrophic
(Orders Methanobacterales, Methanosarcinales). For
A year in the life of a thrombolite 851
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Fig. 5. Volcano plots visualizing gene expression changes between 00:00 (up on left) and 12:00 (up on right) or 18:00 (up on left) and 06:00(up on right) for each season, October, March and August.
Points represent unique KEGG Orthologies; log2(FC)< 5 and FDR adjusted p-value< 0.01, green; log2(FC)> 5 and FDR adjusted p-
value< 0.01, orange; log2(FC)> 5 and FDR adjusted p-value< 0.001, red; no significance, black; FC, fold change. [Colour figure can be
viewed at wileyonlinelibrary.com]
852 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
example, transcripts of methyltransferase genes associ-
ated with methanogenesis from methanol (e.g., mtaC) and
methylamines (e.g., mttB, mttC) were recovered, primarily
from August samples and peaked in expression at 06:00
(data not shown). Methylotrophic methanogenesis is more
varied and may enable these taxa to overcome competition
for H2 by other metabolisms (e.g., sulfate reduction). Addi-
tionally, numerous transcripts associated with aceticlastic
methanogenesis were also recovered (e.g., acsCD, ppa,
ackA) but these transcripts showed no significant differen-
tial expression patterns between time points or seasons
(data not shown). These results suggest that the methano-
gens can use a range of compounds as substrates,
whereas electrons can come from H2 or through methyl
disproportionation, enabling greater metabolic versatility
within the thrombolite-forming community.
Another key metabolism observed within the mat that
plays an important role in carbonate precipitation is sulfate
Fig. 6. Gene expression network using Bray-Curtis dissimilarity (cutoff: 0.45).
Each circle is a KEGG orthologue (KO) and lines connect similar expression patterns (i.e., shorter lines indicate a closer connection). Only
KOs associated with Energy Metabolism pathways are displayed. [Colour figure can be viewed at wileyonlinelibrary.com]
A year in the life of a thrombolite 853
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
reduction (Dupraz and Visscher, 2005). In the adjacent
stromatolites at Highborne Cay, there is extensive biogeo-
chemical evidence to suggest sulfate reduction increases
localized alkalinity, thereby promoting carbonate precipita-
tion (Visscher et al., 2000; Baumgartner et al., 2009).
Microelectrode profiling in the thrombolites have shown
that the distribution of sulfide is detectable but its abun-
dance changes at different depths (Louyakis et al., 2017).
Numerous taxa typically associated with sulfate reduction
have been recovered from the thrombolites (Myshrall
et al., 2010; Mobberley et al., 2012; Louyakis et al., 2017),
but their transcriptional activity throughout the diel cycle is
not yet known.
One of the key steps in sulfate reduction is the direct
reduction of sulfite to sulfide by dissimilatory sulfite reduc-
tase (dsrAB) (Rees, 1973). However, previous
metagenomic analyses recovered very few dsrAB genes
from the thrombolite-forming populations (Mobberley et al.,
2013; Mobberley et al., 2015). Therefore, the transcription
of genes typically associated with this pathway was exam-
ined here (Fig. 4). Transcripts of dsrAB were recovered
from Desulfovibrio, the archaea Archaeoglobus and the
phototrophic sulfur bacterium Allochromatium but exhibited
relatively low read counts (< 700 TPM) with transcript lev-
els peaking at midnight in August. Transcripts for dsrAB
associated genes, including APS reductase (aprAB) and
sulfate adenylyltransferase (sat), were also recovered at a
higher level from a more diverse consortium of taxa, which
also included Desulfovibrionales and Chroococcales, but
these genes can have alternative functions in addition to
sulfate reduction. Interestingly, numerous transcripts were
also recovered for anaerobic sulfite reductases (asrC) orig-
inally described in Salmonella and Clostridium (Hallenbeck
et al., 1989; Czyzewski and Wang, 2012). Additionally,
thiosulfate (phsA) and tetrathionate reductases (ttrB) were
highly expressed within the thrombolites at night (Figs 5
and 6). Together, these results suggest that much like the
stromatolites, dissimilatory sulfate reduction is an impor-
tant component of the sulfur cycle in the thrombolite-
forming community, but is likely a multi-step process with
intermediate sulfur species (e.g., trithionate pathway;
Kobayashi et al., 1969) rather than the direct reduction of
sulfate to sulfide by dissimilatory sulfite reductase (Rees,
1973). These pathways may be highly dependent on the
nature of electron donors and the environmental
conditions.
The oxidation of inorganic sulfur compounds within the
thrombolite-forming mats is less clear, as transcripts asso-
ciated with pathways, such as the SOX pathway, were in
low abundance at all time points. However, transcripts
associated with sulfide:quinone oxidoreductase (sqr) gene,
which can catalyse the oxidation of sulfide to elemental
sulfur in a wide range of phototrophic and chemotrophic
bacteria (Ghosh and Dam, 2009), were observed in higher
numbers at periods of low oxygen concentration (00:00
and 06:00), but exhibited no seasonal differences (data not
shown). These results may suggest a higher prevalence of
anaerobic oxidation of sulfide at night, which has been
observed in the adjacent stromatolites and may promote
carbonate precipitation (Visscher et al., 2000). More
detailed comparative metagenomics and metatranscrip-
tomics are required to identify the full range of sulfur
cycling within the thrombolite-forming communities.
Network analysis of metabolic pathways
To begin to assess the overarching architecture of the tran-
scriptional network within the thrombolites, all transcripts
(regardless of differential expression) encoding genes
associated with metabolism were clustered by KEGG and
mapped with a weighted approach using Phyloseq (Fig. 6)
(McMurdie and Holmes, 2013). The topography of the net-
work indicated two primary clusters with high levels of
connectivity within the transcriptome. In cluster 1, path-
ways associated with sulfur and nitrogen cycling were
highly intertwined. Specifically, transcripts associated with
ammonia oxidation (e.g., amoAB), dissimilatory nitrate
reduction [e.g., nitrous oxide reductase (e.g., nosZ), nitrate
reductase (e.g., narGH) and nitrite reductases (e.g., nifK)],
as well as several nitrite oxidation (e.g., norB) reactions
clustered extensively with transcripts associated with dis-
similatory sulfur reduction genes. These dissimilatory
sulfur reduction transcripts were associated with thiosulfate
reductase (e.g., phsA), tetrathionate reductase (ttrA),
dimethyl sulfoxide (DMSO) reductase (dsmAB), dimethyl
sulfide dehydrogenase (e.g., ddhAC), dimethylsuloniopro-
prionate (DMSP) lyases (e.g., dddL) and an anaerobic
sulfite reductase (asrA). Although this network does not
reflect the differential expression of these genes within the
thrombolites, these results reveal respiration versatility and
suggest that these sulfur and nitrogen pathways may be
tightly coordinated and regulated. Several previous studies
have shown that some sulfide oxidizing bacteria (e.g., Thi-
obacillus spp.) under anaerobic conditions can use nitrate
as the terminal electron acceptor rather than oxygen
(Visscher and Taylor, 1993). For example, the oxidation of
sulfur and thiosulfate can be linked to nitrate and nitrite
reduction in both cultivars (Aminuddin and Nicholas, 1973)
as well as in oceanic oxygen minimum zones (Azhar et al.,
2014), suggesting that in the thrombolite-forming mats the
sulfur and nitrogen cycle are closely coupled.
Interestingly, transcripts associated with dissimilatory
sulfite reductases (dsrA and dsrB), which directly reduce
sulfite to sulfide, were not associated with cluster 1 and
were located within an uncompressed section of the net-
work, suggesting that the expression of these genes are
distinctly regulated. The regulation of dsrAB is highly vari-
able between organisms and in many organisms is not
854 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
dependent on sulfide levels (Grimm et al., 2010). In some,
sulfide-oxidizing taxa expression of dsrAB is increased
under sulfur-oxidizing conditions and under high levels of
reduced sulfur compounds (Chan et al., 2008; Grimm
et al., 2010). However, in several purple and green sulfur
bacteria, as well as some sulfide oxidizers, dsrAB have
been shown to be constitutively expressed (Beller et al.,
2006).
The transcriptional network also revealed a second clus-
ter of transcripts primarily associated with photosynthesis
(PSII and PSI), nitrogen fixation (e.g., nifDK), ammonia
assimilation (e.g., gltBD) and assimilatory sulfur pathways,
such as cysteine biosynthesis (e.g., cysECHIJKM), sug-
gesting these metabolisms exhibit extensive connectivity
within the thrombolite-forming mats. The transcripts asso-
ciated with this cluster are diverse representing a wide
range of taxa associated with these core metabolisms
within the thrombolites. Together, the networking maps
reveal that, with regard to energy metabolism, the
thrombolite-forming mats exhibit tightly coordinated
expression patterns amongst the diverse assemblage of
taxa. Although no taxa-specific clusters are observed
within these metabolic clusters, deeper examination of indi-
vidual gene nodes may help elucidate the potential cross-
talk that occurs between individual taxa and identify how
the community interactions are coordinated. These coordi-
nated activities may serve as important regulators of the
biogeochemical processes that drive the precipitation of
carbonate within the thrombolite-forming communities.
Conclusions
Microbialites represent an important ecosystem to under-
stand the metabolic processes that lead to microbially
induced mineralization. Therefore, examining the ecosys-
tem dynamics of living thrombolites is essential to gain a
more in-depth assessment of these ancient ecosystems.
Our comparative analysis of thrombolite metatranscrip-
tomes provided a new perspective on how microbes
coordinate their metabolic activities across diel and sea-
sonal cycles. First, this study demonstrated that the two
most transcriptionally active taxa within the thrombolites
were the filamentous Rivulariaceae, which have been pre-
viously shown to serve as hot spots for precipitation within
the thrombolites (Planavsky et al., 2009; Louyakis et al.,
2017), and the Xenococcaceae. These results suggest
that these coccoid cyanobacteria may be working syner-
gistically with the Dichothrix spp. to help facilitate the onset
of carbonate precipitation within the thrombolite-forming
mats and this putative relationship will need further
exploration.
Second, our results provide evidence for dynamic tem-
poral patterns of gene expression demonstrating
distinctive diel and seasonal metabolism fluxes that occur
in responses to changing environmental cues. Lastly, the
network mapping of the metabolism transcripts revealed a
high level of connectivity between nitrogen and sulfur
cycling pathways as well as the photosynthesis and assim-
ilatory sulfur pathways, indicating these are among the
most tightly regulated metabolic processes within the
thrombolites. Together, our findings underscore the impor-
tance of elucidating the relative contribution of the different
taxa and metabolisms within thrombolite-forming ecosys-
tems as it will lead to a better understanding of the
underlying interactions and controls required for microbially
induced mineralization.
Experimental procedures
Sample collection
Samples of thrombolite-forming mats were collected in Octo-ber 2013, March 2014 and August 2014 from the island ofHighborne Cay, Exumas, The Bahamas (768490 W, 248430N).
For each season samples were collected for two consecutivesunny days at four time-points: sunrise (06:00), midday(12:00), sunset (18:00) and midnight (00:00). Three replicatebutton formations (Fig. 1B) were collected for each time-pointusing a sterile scalpel and placed immediately in RNAlater
(Life Technologies, Grand Island, NY) before being trans-ported to Space Life Sciences Lab, Merritt Island, FL wherethey were stored at 2808C until processing. Metadata forannual temperature and day lengths were attained fromarchives from Weather Underground (https://goo.gl/qkXGc7).
RNA isolation, purification and cDNA synthesis
Upon thawing, samples were gently pressed and centrifuged
multiple times for removal of RNAlater. A modified PowerBio-film RNA Isolation Kit (Qiagen, Carlsbad, CA, USA) was usedto extract total RNA from �100 mg of sample per reaction.Modifications included adding BFR1, ß-mercaptoethanol
(2.8%) and BFR2 solution directly to the bead tube prior toadding the mat. Additionally, to ensure adequate lysing of cya-nobacterial filaments without compromising the overall qualityof the RNA, the bead tubes underwent homogenization in aBioSpec Mini-Beadbeater-8 in three, 1 min increments with 1
min rests on ice in-between. The bead tubes were then heatedto 658C for 5 min in a water bath and vortexed for 10 min.BFR3 was increased to 200 ll and added to the supernatant.The manufacturer’s protocol was followed for the remainingsteps. An additional DNAse treatment, the Turbo DNA-free Kit
(Ambion, Applied Biosystem Business, CA), was used toremove residual DNA and the samples were purified usingRNA Clean and Concentrator-5 (Zymo Research, Orange,CA, USA).
Nucleic acid concentrations were measured with a Qubit
2.0 Fluorometer (Invitrogen, Life Technologies, Carlsbad, CA,USA). Quality of the RNA was visualized on an Agilent 2100Bioanalyser with an RNA 6000 Nano Chip (Agilent Technolo-gies, Santa Clara, CA, USA). To ensure the RNA extracts didnot contain any enzymatic inhibitors, samples were amplified
using real-time PCR. Universal primers, targeting the 16SrRNA gene (Khodadad et al., 2011), with an iTaq Universal
A year in the life of a thrombolite 855
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
SYBR Green One-Step kit (BioRad, Hercules, CA, USA) were
used on a CFX96 Touch platform (BioRad, Hercules, CA,
USA) as previously described (Casaburi et al., 2017). Overall,
approximately 3–5 extractions for each of the collected throm-
bolite button formations (n 5 3) were pooled to acquire the
minimum 1 lg of RNA needed for each library preparation.
Generation and sequencing of RNA libraries
Recovered RNA first underwent rRNA depletion with Ribo-
Zero Gold rRNA Removal Kit (Epidemiology, Illumina, San
Diego, CA, USA) followed by purification with RNA Clean and
Concentrator-5. Synthesis of cDNA libraries was accom-
plished using ScriptSeq v2 RNA-Seq Library Preparation Kit
(Illumina, San Diego USA) following manufacturers protocol.
Libraries were purified with Axygen AxyPrep Mag kit (Axygen
Biosciences, Union City, CA, USA) and quantified before
sequencing. For all time-points (n 5 4) and seasons (n 5 3),
duplicate libraries were generated for a total of 24 libraries
that were sequenced at the University of Florida’s Interdisci-
plinary Centre for Biotechnology Research using NextSeq
500/550 High Output v2 Kit (paired end, 150 cycles, insert
size 550 bp) on a NextSeq 500 sequencing system (Illumina,
San Diego, CA, USA). Sequences of thrombolite cDNA were
deposited in the NCBI Sequence Read Archive (SRR6006825
though SRR6006848). BioSample accession numbers are
consecutive from SAMN07554631 through SAMN07554654
under BioProject PRJNA305634.
Sequence quality control, assembly, annotation
and mapping
A comprehensive flowchart of the bioinformatic analysis is
described in Supporting Information Fig. S1. Briefly, sequen-
ces were trimmed using Trimmomatic v0.35 with a threshold
of Q5 (Bolger et al., 2014) and rRNA was removed using Sort-
MeRNA, as previously described (Kopylova et al., 2012).
FastQC was used to analyse read quality (Andrews, 2014).
Taxonomic analysis was completed on the quality-filtered 16S
rRNA gene sequences isolated from each sample using
Quantitative Insights Into Microbial Ecology (QIIME v1.9.1)
(Caporaso et al., 2010b). Operational taxonomic units (OTUs)
were assigned to the reads at 97% identity against the Green-
genes database v13.8 (DeSantis et al., 2006) using the
UCLUST method (open reference OTU picking) within QIIME.
Representative reads were aligned with PyNAST (v1.2.2)
(Caporaso et al., 2010a) to the Greengenes Core reference
alignment and a phylogenetic tree was built with FastTree
(v2.1.3) (Price et al., 2013). The resulting biom data was visu-
alized using GraPhlAn v0.9.7 (Asnicar et al., 2015) after
removal of unassigned records. The networking analysis
included only transcripts with> 10 hits to a specific gene and
was completed using the R package Phyloseq v1.19.1
(McMurdie and Holmes, 2013).
The remaining sequences were assembled using Trinity
v2.2.0 with the following parameters: fastq assembly (left read
file contained forward and unpaired reads), minimum contig
length of 75 bp and normalized reads (Garber et al., 2011).
Detonate v1.11 was used to score assembly tools under
altered parameters to facilitate selection for downstream
analysis (Li et al., 2014). Alignment was completed using
Bowtie2 v2.2.9 (Langmead and Salzberg, 2012) and RSEM
v1.2.7 (Li and Dewey, 2011) estimation was used for counts of
sample replicates, as well as pooled samples. RSEM esti-
mates were rounded to nearest integer, length corrected
(transcripts per million method), trimmed mean of M-values
(TMM) adjusted for normalized expression values (EdgeR
v3.16.5) (Robinson et al., 2010; McCarthy et al., 2012), and
batch corrected with ARSyNseq. NOISeq v2.18.0 (Tarazona
et al., 2011; Tarazona et al., 2015) in Bioconductor v3.4 was
used for PCA plotting (Euclidian distance) as well as differen-
tial expression analysis with the following noiseqbio
parameters: norm 5 ‘tmm’, k 5 0.5, r 5 50, filter 5 1,
nclust 5 15, a0per 5 0.9 and random.seed 5 12 200. Venn
diagrams were generated using the R package, VennDiagram
v1.6.17 (Chen and Boutros, 2011). The metatranscriptome
assembly was annotated using Trinotate v3.0.1 using com-
plete pipeline (http://trinotate.github.io). KEGG terms were
associated with their respective pathways using the R pack-
age KEGGREST (Tenenbaum, 2017).
Acknowledgements
The authors would like to thank Kimberly Cranmore for her
technical assistance as well as Drs. Brelan Moritz and Keelnat-
ham Shanmugam for their advice on RNA extraction. ASL was
supported by the NSF Graduate Research Fellowship Pro-
gram. HG was supported by the Developing a European Ameri-
can NGS Network program. Collection permits were provided
through the Ministry of Agriculture, Marine Resources and
Local Government of The Bahamas. The research efforts were
supported by the NASA Exobiology and Evolutionary Biology
program element (NNX12AD64G) awarded to JSF.
Author contributions
ASL and JSF designed the experiments and conducted
the fieldwork; ASL, RMEB and JSF performed the experi-
ments; ASL, HG, GC, AAD and JSF analysed the data;
ASL and JSF wrote the manuscript.
Conflict of Interest
The authors declare no conflict of interest.
References
Aitken, J.D. (1967) Classification and environmental signifi-
cance of cryptalgal limestones and dolomites, with illustra-
tions from the Cambrian and Ordovician of southwestern
Alberta. J Sediment Petrol 37: 1163–1178.Alcantara-Hern�andez, R.J., Valdespino-Castillo, P.M.,
Centeno, C.M., Alcocer, J., Merino-Ibarra, M., and Falc�on,
L.I. (2017) Genetic diversity associated with N-cycle path-
ways in microbialites from Lake Alchichica, Mexico. Aquat
Microb Ecol 78: 121–133.Allen, M.A., Goh, F., Burns, B.P., and Neilan, B.A. (2009)
Bacterial, archaeal and eukaryotic diversity of smooth and
pustular microbial mat communities in the hypersaline
lagoon of Shark Bay. Geobiology 7: 82–96.
856 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Altermann, W., Kazmierczak, J., Oren, A., and Wright, D.T.
(2006) Cyanobacterial calcification and its rock-building
potential during 3.5 billion years of Earth history. Geobiol-
ogy 4: 147–166.
Aminuddin, M., and Nicholas, D.J. (1973) Sulphide oxidation
linked to the reduction of nitrate and nitrite in Thiobacillus
denitrificans. Biochim Biophys Acta 325: 81–93.
Andres, M.S., and Reid, R.P. (2006) Growth morphologies of
modern marine stromatolites: a case study from Highborne
Cay, Bahamas. Sediment Geol 185: 319–328.Andrews, S. (2014) FastQC a quality-control tool for high-
throughput sequence data [WWW document]. URL http://
www. In Bioinformaticsbabraham. ac. uk/projects/fastqc.Asnicar, F., Weingart, G., Tickle, T.L., Huttenhower, C., and
Segata, N. (2015) Compact graphical representation of phylo-
genetic data and metadata with GraPhlAn. PeerJ 3: e1029.Azhar, M.A., Canfield, D.E., Fennel, K., Thamdrup, B., and
Bjerrum, C.J. (2014) A model-based insight into the cou-
pling of nitrogen and sulfur cycles in a coastal upwelling
system. J Geophys Res Biogeosci 119: 264–285.Badger, M.R., and Price, G.D. (2003) CO2 concentrating
mechanisms in cyanobacteria: molecular components, their
diversity and evolution. J Exp Bot 54: 609–622.Baker, B.J., Lazar, C.S., Teske, A.P., and Dick, G.J. (2015)
Genomic resolution of linkages in carbon, nitrogen, and sul-
fur cycling among widespread estuary sediment bacteria.
Microbiome 3: 14.
Baumgartner, L.K., Reid, R.P., Dupraz, C., Decho, A.W.,
Buckley, D.H., Spear, J.R., et al. (2006) Sulfate reducing
bacteria in microbial mats: changing paradigms, new dis-
coveries. Sediment Geol 185: 131–145.Baumgartner, L.K., Spear, J.R., Buckley, D.H., Pace, N.R.,
Reid, R.P., Dupraz, C., and Visscher, P.T. (2009) Microbial
diversity in modern marine stromatolites, Highborne Cay,
Bahamas. Environ Microbiol 11: 2710–2719.Beck, C., Hertel, S., Rediger, A., Lehmann, R., Wiegard, A.,
Kolsch, A., et al. (2014) Daily expression pattern of protein-
encoding genes and small noncoding RNAs in Synechocystis
sp. strain PCC 6803. Appl Environ Microbiol 80: 5195–5206.,.Beller, H.R., Chain, P.S., Letain, T.E., Chakicherla, A.,
Larimer, F.W., Richardson, P.M., et al. (2006) The genome
sequence of the obligately chemolithoautotrophic, faculta-
tively anaerobic bacterium Thiobacillus denitrificans.
J Bacteriol 188: 1473–1488.Berelson, W.M., Corsetti, F.A., Pepe-Ranney, C., Hammond,
D.E., Beaumont, W., and Spear, J.R. (2011) Hot spring sili-
ceous stromatolites from Yellowstone National Park:
assessing growth rate and laminae formation. Geobiology
9: 411–424.Bernhard, J.M., Edgcomb, V.P., Visscher, P.T., McIntyre-
Wressnig, A., Summons, R.E., Bouxsein, M.L., et al. (2013)
Insights into foraminiferal influences on microfabrics of
microbialites at Highborne Cay, Bahamas. Proc Natl Acad
Sci USA 110: 9830–9834.Blazewicz, S.J., Barnard, R.L., Daly, R.A., and Firestone,
M.K. (2013) Evaluating rRNA as an indicator of microbial
activity in environmental communities: limitations and uses.
ISME J 7: 2061–2068.Bolger, A.M., Lohse, M., and Usadel, B. (2014) Trimmomatic:
a flexible trimmer for Illumina sequence data. Bioinformatics
30: 2114–2120.
Bowlin, E.M., Klaus, J., Foster, J.S., Andres, M., Custals, L.,
and Reid, R.P. (2012) Environmental controls on microbial
community cycling in modern marine stromatolites. Sedi-
ment Geol 263–264: 45–55.Braissant, O., Decho, A.W., Dupraz, C., Glunk, C., Przekop,
K.M., and Visscher, P.T. (2007) Exopolymeric substances of
sulfate-reducing bacteria: interactions with calcium at alka-
line pH and implication for formation of carbonate minerals.
Geobiology 5: 401–411.Breitbart, M., Hoare, A., Nitti, A., Siefert, J., Haynes, M.,
Dinsdale, E., et al. (2009) Metagenomic and stable isotopic
analyses of modern freshwater microbialites in Cuatro
Ci�enegas, Mexico. Environ Microbiol 11: 16–34.Burne, R.V., and Moore, L.S. (1987) Microbialites: organosedi-
mentary deposits of benthic microbial communities. Palaios
2: 241–254.Burns, B.P., Goh, F., Allen, M., and Neilan, B.A. (2004) Micro-
bial diversity of extant stromatolites in the hypersaline
marine environment of Shark Bay, Australia. Environ Micro-
biol 6: 1096–1101.
Caporaso, J.G., Bittinger, K., Bushman, F.D., DeSantis, T.Z.,
Andersen, G.L., and Knight, R. (2010a) PyNAST: a flexible
tool for aligning sequences to a template alignment. Bioin-
formatics 26: 266–267.Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K.,
Bushman, F.D., Costello, E.K., et al. (2010b) QIIME allows
analysis of high-throughput community sequencing data.
Nat Methods 7: 335–336.Casaburi, G., Duscher, A.A., Reid, R.P., and Foster, J.S.
(2016) Characterization of the stromatolite microbiome from
Little Darby Island, The Bahamas using predictive and
whole shotgun metagenomic analysis. Environ Microbiol
18: 1452–1469.Casaburi, G., Goncharenko-Foster, I., Duscher, A.A., and
Foster, J.S. (2017) Transcriptomic changes in an animal-
bacterial symbiosis under modeled microgravity conditions.
Sci Rep 7: 46318.
Cerqueda-Garcia, D., and Falcon, L.I. (2016) Metabolic poten-
tial of microbial mats and microbialites: autotrophic capabili-
ties described by an in silico stoichiometric approach from
shared genomic resources. J Bioinform Comput Biol 14:
1650020.Chagas, A.A.P., Webb, G.E., Burne, R.A., and Southam, G.
(2016) Modern lacustrine mcirobialites; towards a synthesis
of aquous and carbonate geochemistry and minerology.
Earth Sci Rev 162: 338–363.
Chan, L.K., Weber, T.S., Morgan-Kiss, R.M., and Hanson, T.E.
(2008) A genomic region required for phototrophic thiosulfate
oxidation in the green sulfur bacterium Chlorobium tepidum
(syn. Chlorobaculum tepidum). Microbiology 154: 818–829.Chen, H., and Boutros, P.C. (2011) VennDiagram: a package
for the generation of highly-customizable Venn and Euler
diagrams in R. BMC Bioinformatics 12: 35.Coman, C., Chiriac, C.M., Robeson, M.S., Ionescu, C.,
Dragos, N., Barbu-Tudoran, L., et al. (2015) Structure, min-
eralogy, and microbial diversity of geothermal spring micro-
bialites associated with a deep oil drilling in Romania. Front
Microbiol 6: 253.Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D.,
Cervera, A., McPherson, A., et al. (2016) A survey of best
practices for RNA-seq data analysis. Genome Biol 17: 13.
A year in the life of a thrombolite 857
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Czyzewski, B.K., and Wang, D.N. (2012) Identification and
characterization of a bacterial hydrosulphide ion channel.
Nature 483: 494–497.Den Uyl, P.A., Richardson, L.L., Jain, S., Dick, G.J., and
Kellogg, C.A. (2016) Unraveling the physiological roles of
the cyanobacterium Geitlerinema sp. BBD and other Black
Band Disease community members through genomic anal-
ysis of a mixed culture. PLoS One 11: e0157953.DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie,
E.L., Keller, K., et al. (2006) Greengenes, a chimera-
checked 16S rRNA gene database and workbench compat-
ible with ARB. Appl Environ Microbiol 72: 5069–5072.Desnues, C.G., Rodriguez-Brito, B., Rayhawk, S., Kelley, S.,
Tran, T., Haynes, M., et al. (2008) Biodiversity and biogeog-
raphy of phages in modern stromatolites and thrombolites.
Nature 452: 340–345.Dupraz, C., and Visscher, P.T. (2005) Microbial lithification in
marine stromatolites and hypersaline mats. Trends Micro-
biol 13: 429–438.
Dupraz, C., Visscher, P.T., Baumgartner, L.K., and Reid, R.P.
(2004) Microbe-mineral interactions: early carbonate precip-
itation in a hypersaline lake (Eleuthera Island, Bahamas).
Sedimentology 51: 1: 21.Dupraz, C., Reid, R.P., Braissant, O., Decho, A.W., Norman,
R.S., and Visscher, P.T. (2009) Processes of carbonate
precipitation in modern microbial mats. Earth Sci Rev 96:
141–162.Edgcomb, V.P., Bernhard, J.M., Beaudoin, D., Pruss, S.,
Welander, P.V., Schubotz, F., et al. (2013) Molecular indica-
tors of microbial diversity in oolitic sands of Highborne Cay,
Bahamas. Geobiology 11: 234–251.Fernandez-Gomez, B., Richter, M., Schuler, M., Pinhassi, J.,
Acinas, S.G., Gonzalez, J.M., and Pedros-Alio, C. (2013)
Ecology of marine Bacteroidetes: a comparative genomics
approach. ISME J 7: 1026–1037.
Foster, J.S., and Green, S.J. (2011) Microbial diversity in mod-
ern stromatolites In Cellular Origin, Life in Extreme Habitats
and Astrobiology: Interactions with Sediments. Seckbach,
J., and Tewari, V. (eds). Springer, pp. 385–405.Foster, J.S., Green, S.J., Ahrendt, S.R., Hetherington, K.L.,
Golubic, S., Reid, R.P., and Bebout, L. (2009) Molecular
and morphological characterization of cyanobacterial diver-
sity in the marine stromatolites of Highborne Cay, Bahamas.
ISME J 3: 573–587.Garber, M., Grabherr, M.G., Guttman, M., and Trapnell, C.
(2011) Computational methods for transcriptome annotation
and quantification using RNA-seq. Nat Methods 8:
469–477.
Ghosh, W., and Dam, B. (2009) Biochemistry and molecular
biology of lithotrophic sulfur oxidation by taxonomically and
ecologically diverse bacteria and archaea. FEMS Microbiol
Rev 33: 999–1043.Gifford, S.M., Sharma, S., and Moran, M.A. (2014) Linking
activity and function to ecosystem dynamics in a coastal
bacterioplankton community. Front Microbiol 5: 185.Gleeson, D.B., Wacey, D., Waite, I., O’donnell, A.G., and
Kilburn, M.R. (2015) Biodiversity of living, non-marine,
thrombolites of Lake Clifton, Western Australia. Geomicro-
biol J 33: 850–859.Goh, F., Allen, M.A., Leuko, S., Kawaguchi, T., Decho, A.W.,
Burns, B.P., and Neilan, B.A. (2009) Determining the
specific microbial populations and their spatial distribution
within the stromatolite ecosystem of Shark Bay. ISME J 3:
383–396.Goh, F., Jeon, Y.J., Barrow, K., Neilan, B.A., and Burns, B.P.
(2011) Osmoadaptive strategies of the archaeon Halococ-
cus hamelinensis isolated from a hypersaline stromatolite
environment. Astrobiology 11: 529–536.
Grimm, F., Dobler, N., and Dahl, C. (2010) Regulation of dsr
genes encoding proteins responsible for the oxidation of
stored sulfur in Allochromatium vinosum. Microbiology 156:
764–773.Grotzinger, J.P., and Knoll, A.H. (1999) Stromatolites in Pre-
cambrian carbonates: evolutionary mileposts or environ-
mental dipsticks?. Annu Rev Earth Planet Sci 27: 313–358.Hallenbeck, P.C., Clark, M.A., and Barrett, E.L. (1989) Char-
acterization of anaerobic sulfite reduction by Salmonella
typhimurium and purification of the anaerobically induced
sulfite reductase. J Bacteriol 171: 3008–3015.Hartig, E., and Zumft, W.G. (1999) Kinetics of nirS expression
(cytochrome cd1 nitrite reductase) in Pseudomonas
stutzeri during the transition from aerobic respiration to
denitrification: evidence for a denitrification-specific nitrate-
and nitrite-responsive regulatory system. J Bacteriol 181:
161–166.Havemann, S.A., and Foster, J.S. (2008) Comparative charac-
terization of the microbial diversities of an artificial microbia-
lite model and a natural stromatolite. Appl Environ Microbiol
74: 7410–7421.Hihara, Y., Sonoike, K., and Ikeuchi, M. (1998) A novel gene,
pmgA, specifically regulates photosystem stoichiometry in
the cyanobacterium Synechocystis species PCC 6803 in
response to high light. Plant Physiol 117: 1205–1216.Hihara, Y., Kamei, A., Kanehisa, M., Kaplan, A., and Ikeuchi,
M. (2001) DNA microarray analysis of cyanobacterial gene
expression during acclimation to high light. Plant Cell 13:
793–806.Jensen, S.I., Steunou, A.S., Bhaya, D., Kuhl, M., and
Grossman, A.R. (2011) In situ dynamics of O2, pH and cya-
nobacterial transcripts associated with CCM, photosynthe-
sis and detoxification of ROS. ISME J 5: 317–328.Kazmierczak, J., and Altermann, W. (2002) Neoarchean bio-
mineralization by benthic cyanobacteria. Science 298:
2351.Kazmierczak, J., Altermann, W., Kremer, B., Kempe, S., and
Eriksson, P.G. (2009) Mass occurrence of benthic coccoid
cyanobacteria and their role in the production of
Neoarchean carbonates of South Africa. Precambrian Res
173: 79–92.
Ka�zmierczak, J., Kempe, S., Kremer, B., L�opez-Garcia, P.,
Moreira, D., and Tavera, R. (2011) Hydrochemistry and
microbialites of the alkaline crater lake Alchichica, Mexico.
Facies 57: 543–570.Kennard, J.M., and James, N.P. (1986) Thrombolites and stro-
matolites: two distinct types of microbial structures. Palaios
1: 492–503.Khodadad, C.L., and Foster, J.S. (2012) Metagenomic and
metabolic profiling of nonlithifying and lithifying stromatolitic
mats of Highborne Cay, The Bahamas. PLos One 7:
e38229.Khodadad, C.L., Zimmerman, A.R., Green, S.J., Uthandi, S.,
and Foster, J.S. (2011) Taxa-specific changes in soild
858 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
microbial composition induced by pyrogenic carbon amend-
ments. Soil Biol Biochem 43: 385–392.Kobayashi, K., Tachibana, S., and Ishimoto, M. (1969)
Intermediary formation of trithionate in sulfite
reduction in a sulfate-reducing bacteirum. J Biochem
(Tokyo) 65: 155.Kopylova, E., Noe, L., and Touzet, H. (2012) SortMeRNA: fast
and accurate filtering of ribosomal RNAs in metatranscrip-
tomic data. Bioinformatics 28: 3211–3217.Kulkarni, R.D., and Golden, S.S. (1994) Adaptation to high
light intensity in Synechococcus sp. strain PCC 7942: regu-
lation of three psbA genes and two forms of the D1 protein.
J Bacteriol 176: 959–965.Langmead, B., and Salzberg, S.L. (2012) Fast gapped-read
alignment with Bowtie 2. Nat Methods 9: 357–359.
Lepp, P.W., and Schmidt, T.M. (1998) Nucleic acid content of
Synechococcus spp. during growth in continuous light and
light/dark cycles. Arch Microbiol 170: 201–207.
Li, B., and Dewey, C.N. (2011) RSEM: accurate transcript
quantification from RNA-Seq data with or without a refer-
ence genome. BMC Bioinformatics 12: 323.Li, B., Fillmore, N., Bai, Y., Collins, M., Thomson, J.A.,
Stewart, R., and Dewey, C.N. (2014) Evaluation of de novo
transcriptome assemblies from RNA-Seq data. Genome
Biol 15: 553.Louyakis, A.S., Mobberley, J.M., Vitek, B.E., Visscher, P.T.,
Hagan, P.D., Reid, R.P., et al. (2017) A study of the micro-
bial spatial heterogeneity of Bahamian thrombolites using
molecular, biochemical, and stable isotope analyses. Astro-
biology 17: 413–430.Ludwig, M., and Bryant, D.A. (2011) Transcription profiling of
the model cyanobacterium Synechococcus sp. strain PCC
7002 by Next-Gen (SOLiD) sequencing of cDNA. Front
Microbiol 2: 41.Mata, S.A., Harwood, C.L., Corsetti, F.A., Stork, N.J., Eilers,
K., Berelson, W., and Spear, J.R. (2012) Influence of gas
production and filament orientation on stromatolite microfa-
bric. Palaios 27: 206–219.McCarthy, D.J., Chen, Y., and Smyth, G.K. (2012) Differential
expression analysis of multifactor RNA-Seq experiments
with respect to biological variation. Nucleic Acids Res 40:
4288–4297.McIlroy, S.J., and Nielson, P.H. (2014) The family saprospira-
ceae. In The Prokaryotes. Berlin, Heidelberg: Springer, pp.
863–889.McMurdie, P.J., and Holmes, S. (2013) phyloseq: an R pack-
age for reproducible interactive analysis and graphics of
microbiome census data. PLos One 8: e61217.Mobberley, J.M., Ortega, M.C., and Foster, J.S. (2012) Com-
parative microbial diversity analyses of modern marine
thrombolitic mats by barcoded pyrosequencing. Environ
Microbiol 14: 82–100.
Mobberley, J.M., Khodadad, C.L., and Foster, J.S. (2013) Met-
abolic potential of lithifying cyanobacteria-dominated throm-
bolitic mats. Photosynth Res 118: 125–140.
Mobberley, J.M., Khodadad, C.L., Visscher, P.T., Reid, R.P.,
Hagan, P., and Foster, J.S. (2015) Inner workings of throm-
bolites: spatial gradients of metabolic activity as revealed by
metatranscriptome profiling. Sci Rep 5: 12601.Mohamed, A., and Jansson, C. (1991) Photosynthetic elec-
tron transport controls degradation but not production of
psbA transcripts in the cyanobacterium Synechocystis
6803. Plant Mol Biol 16: 891–897.Myshrall, K., Mobberley, J.M., Green, S.J., Visscher, P.T.,
Havemann, S.A., Reid, R.P., and Foster, J.S. (2010) Bio-
geochemical cycling and microbial diversity in the modern
marine thrombolites of Highborne Cay, Bahamas. Geobiol-
ogy 8: 337–354.Nishimura, K., Shimada, H., Ohta, H., Masuda, T., Shioi, Y.,
and Takamiya, K. (1996) Expression of the puf operon in an
aerobic photosynthetic bacterium, Roseobacter denitrifi-
cans. Plant Cell Physiol 37: 153–159.Ordonez, O.F., Flores, M.R., Dib, J.R., Paz, A., and Farias,
M.E. (2009) Extremophile culture collection from Andean
lakes: extreme pristine environments that host a wide diver-
sity of microorganisms with tolerance to UV radiation.
Microb Ecol 58: 461–473.Papineau, D., Walker, J.J., Mojzsis, S.J., and Pace, N.R.
(2005) Composition and structure of microbial communities
from stromatolites of Hamelin Pool in Shark Bay, Western
Australia. Appl Environ Microbiol 71: 4822–4832.
Paul, D., Kumbhare, S.V., Mhatre, S.S., Chowdhury, S.P.,
Shetty, S.A., Marathe, N.P., et al. (2015) Exploration of
microbial diversity and community structure of Lonar Lake:
the only hypersaline meteorite crater lake within basalt rock.
Front Microbiol 6: 1553.Pepe-Ranney, C., Berelson, W.M., Corsetti, F.A., Treants, M.,
and Spear, J.R. (2012) Cyanobacterial construction of hot
spring siliceous stromatolites in Yellowstone National Park.
Environ Microbiol 14: 1182–1197.
Petrash, D.A., Gingras, M.K., Lalonde, S.V., Orange, F.,
Pecoits, E., and Konhauser, K.O. (2012) Dynamic controls
on accretion and lithification of modern gypsum-dominated
thrombolites, Los Roques, Venezuela. Sediment Geol
245–246: 29–47.Planavsky, N., Reid, R.P., Andres, M., Visscher, P.T., Myshrall,
K.L., and Lyons, T.W. (2009) Formation and diagenesis of
modern marine calcified cyanobacteria. Geobiology 7: 566–
576.Price, G.D., Woodger, F.J., Badger, M.R., Howitt, S.M., and
Tucker, L. (2004) Identification of a SulP-type bicarbonate
transporter in marine cyanobacteria. Proc Natl Acad Sci
USA 101: 18228–18233.Price, M.N., Dehal, P.S., Arkin, A.P., and Poon, A.F.Y. (2010)
FastTree 2–approximately maximum-likelihood trees for
large alignments. PLos One 5: e9490.Price, G.D., Pengelly, J.J., Forster, B., Du, J., Whitney, S.M.,
von Caemmerer, S., et al. (2013) The cyanobacterial CCM
as a source of genes for improving photosynthetic CO2 fixa-
tion in crop species. J Exp Bot 64: 753–768.Reddy, G.S., Prakash, J.S., Vairamani, M., Prabhakar, S.,
Matsumoto, G.I., and Shivaji, S. (2002) Planococcus ant-
arcticus and Planococcus psychrophilus spp. nov. isolated
from cyanobacterial mat samples collected from ponds in
Antarctica. Extremophiles 6: 253–261.
Rees, C.E. (1973) A steady-state model for sulphur isotope
fractionation in bacterial reduction processes. Geochim Et
Cosmochim Acta 37: 1141–1162.Reid, R.P., Visscher, P.T., Decho, A.W., Stolz, J.F., Bebout,
B.M., Dupraz, C., et al. (2000) The role of microbes in
accretion, lamination and early lithification of modern
marine stromatolites. Nature 406: 989–992.
A year in the life of a thrombolite 859
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
Rinke, C., Schwientek, P., Sczyrba, A., Ivanova, N.N.,
Anderson, I.J., Cheng, J.F., et al. (2013) Insights into the
phylogeny and coding potential of microbial dark matter.
Nature 499: 431–437.
Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010)
edgeR: a Bioconductor package for differential expression
analysis of digital gene expression data. Bioinformatics 26:
139–140.Russell, J.A., Brady, A.L., Cardman, Z., Slater, G.F., Lim,
D.S., and Biddle, J.F. (2014) Prokaryote populations of
extant microbialites along a depth gradient in Pavilion Lake,
British Columbia, Canada. Geobiology 12: 250–264.Ruvindy, R., White, R.A., III, Neilan, B.A., and Burns, B.P.
(2016) Unravelling core microbial metabolisms in the hyper-
saline microbial mats of Shark Bay using high-throughput
metagenomics. ISME J 10: 183–196.Sagha€ı, A., Zivanovic, Y., Zeyen, N., Moreira, D., Benzerara,
K., Deschamps, P., et al. (2015) Metagenome-based diver-
sity analyses suggest a significant contribution of non-
cyanobacterial lineages to carbonate precipitation in mod-
ern microbialites. Front Microbiol 6: 797.Sandrini, G., Tann, R.P., Schuurmans, J.M., van Beusekom,
S.A., Matthijs, H.C., and Huisman, J. (2016) Diel variation in
gene expression of the CO2-concentrating mechanism dur-
ing a harmful cyanobacterial bloom. Front Microbiol 7: 551.Schneider, D., Arp, G., Reimer, A., Reitner, J., Daniel, R., and
Balcazar, J.L. (2013) Phylogenetic analysis of a
microbialite-forming microbial mat from a hypersaline lake
of the Kiritimati atoll, Central Pacific. PLoS One 8: e66662.See-Too, W.-S., Ee, R., Madhaiyan, M., Kwon, S.-W., Tan,
J.Y., Lim, Y.L., et al. (2017) Planococcus versutus sp. nov.,
isolated from soil. Int J Syst Evol Microbiol 67: 94–950.Simon, M., Scheuner, C., Meier-Kolthoff, J.P., Brinkhoff, T.,
Wagner-Dobler, I., Ulbrich, M., et al. (2017) Phylogenomics
of Rhodobacteraceae reveals evolutionary adaptation to
marine and non-marine habitats. ISME J 11: 1483–1499.Sorensen, K.B., Canfield, D.E., Teske, A.P., and Oren, A.
(2005) Community composition of a hypersaline endoeva-
poritic microbial mat. Appl Environ Microbiol 71: 7352–
7365.Steunou, A.S., Jensen, S.I., Brecht, E., Becraft, E.D.,
Bateson, M.M., Kilian, O., et al. (2008) Regulation of nif
gene expression and the energetics of N2 fixation over the
diel cycle in a hot spring microbial mat. ISME J 2: 364–378.Stolz, J.F., Feinstein, T.N., Salsi, J., Visscher, P.T., and Reid,
R.P. (2001) TEM analysis of microbial mediated sedimenta-
tion and lithification in modern marine stromatolites. Amer
Mineral 86: 826–833.
Suosaari, E.P., Reid, R.P., Playford, P.E., Foster, J.S., Stolz,
J.F., Casaburi, G., et al. (2016) New multi-scale perspec-
tives on the stromatolites of Shark Bay, Western Australia.
Sci Rep 6: 20557.Tang, M., Chen, C., Li, J., Xiang, W., Wu, H., Wu, J., et al.
(2016) Fabivirga thermotolerans gen. nov., sp. nov., a novel
marine bacterium isolated from culture broth of a marine
cyanobacterium. Int J Syst Evol Microbiol 66: 1095–1099.Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A., and
Conesa, A. (2011) Differential expression in RNA-seq: a
matter of depth. Genome Res 21: 2213–2223.Tarazona, S., Furio-Tari, P., Turra, D., Pietro, A.D., Nueda,
M.J., Ferrer, A., and Conesa, A. (2015) Data quality aware
analysis of differential expression in RNA-seq with NOISeq
R/Bioc package. Nucleic Acids Res 43: e140.Tenenbaum, D. (2017) KEGGREST: Client-side REST access
to KEGG. In R package version 1161.Valdespino-Castillo, P.M., Alcantara-Hernandez, R.J., Alcocer,
J., Merino-Ibarra, M., Macek, M., and Falcon, L.I. (2014)
Alkaline phosphatases in microbialites and bacterioplankton
from Alchichica soda lake, Mexico. FEMS Microbiol Ecol
90: 504–519.Visscher, P.T., and Stolz, J.F. (2005) Microbial mats as bio-
reactors: populations, processes and products. Palaeo-
geogr Palaeoclimatol Palaeoecol 219: 87–100.Visscher, P.T., and Taylor, B.F. (1993) A new mechanism for
the aerobic catabolism of dimethyl sulfide. Appl Environ
Microbiol 59: 3784–3789.Visscher, P.T., Reid, R.P., Bebout, B.M., Hoeft, S.E.,
Macintyre, I.G., and Thompson, J.A. (1998) Formation of
lithified micritic laminae in modern marine stromatolites
(Bahamas): the role of sulfur cycling. Amer Mineral 83:
1482–1493.Visscher, P.T., Reid, R.P., and Bebout, B.M. (2000) Microscale
observations of sulfate reduction: correlation of microbial
activity with lithified micritic laminae in modern marine stro-
matolites. Geology 28: 919–922.Walter, M.R. (1994) Stromatolites: the main geological source
of information on the evolution of the early benthos. In Early
Life on Earth Nobel Symposium. Bengston, S. (ed). New
York, NY: Columbia University Press, pp. 270–283.
Warden, J.G., Casaburi, G., Omelon, C.R., Bennett, P.C.,
Breecker, D.O., and Foster, J.S. (2016) Characterization of
microbial mat microbiomes in the modern thrombolite eco-
system of Lake Clifton, Western Australia using shotgun
metagenomics. Front Microbiol 7: 1064.
Wegener, K.M., Nagarajan, A., and Pakrasi, H.B. (2015) An
atypical psbA gene encodes a sentinel D1 protein to form a
physiologically relevant inactive photosystem II complex in
cyanobacteria. J Biol Chem 290: 3764–3774.White, R.A., III, Chan, A.M., Gavelis, G.S., Leander, B.S.,
Brady, A.L., Slater, G.F., et al. (2016) Metagenomic analysis
suggests modern freshwater microbialites harbor a distinct
core microbial community. Front Microbiol 6: 1531.Wong, H.L., Visscher, P.T., White Iii, R.A., Smith, D.L.,
Patterson, M.M., and Burns, B.P. (2017) Dynamics of
archaea at fine spatial scales in Shark Bay mat micro-
biomes. Sci Rep 7: 46160.Yurkov, V.V., and Beatty, J.T. (1998) Aerobic anoxygenic pho-
totrophic bacteria. Microbiol Mol Biol Rev 62: 695–724.Zinser, E.R., Lindell, D., Johnson, Z.I., Futschik, M.E.,
Steglich, C., Coleman, M.L., et al. (2009) Choreography of
the transcriptome, photophysiology, and cell cycle of a mini-
mal photoautotroph, prochlorococcus. PLoS One 4: e5135.
Supporting information
Additional Supporting Information may be found in the
online version of this article at the publisher’s web-site:
Fig. S1. Methodology flowchart outlining the experimental
parameters and bioinformatics tools used in the analysis of
the metatranscriptome data.
Fig. S2. Taxonomic histograms for each sample to family
level or higher if unable to classify to family. Legend
860 A. S. Louyakis et al.
VC 2017 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 20, 842–861
identifies thirty most abundant taxa over all libraries and
lists in order of bar plot from top to bottom. Note, two colors
of Chroococcales reflect both ‘unclassified’ and ‘other’
designations.Table S1. Summary of Trinity assembly statisticsTable S2. Correlation coefficients for sample pairs; repli-
cates outlined in bold, Pearson correlation on bottom (blue
scale) and Spearman correlation on top (green scale).
Table S3. Relative abundance of active taxa within throm-
bolites over diel and seasonal cycles.Table S4. Genes used to generate metabolism profiles inFigure 4.Table S5. Expressed genes in thrombolites with normalizedcounts over diel and seasonal cycles and number of ortho-
logs detected in Trinity assembly.
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