Introduction
Long chain alkyl diols (LCDs) are lipids that consist of a linear alkyl chain
with 22–38 carbons, hydroxylated at both the terminal carbon atom and at an
intermediate position, and usually saturated or monounsaturated. LCDs were
identified for the first time in Black Sea sediments (de Leeuw et al., 1981)
and have subsequently been found with widespread occurrence in both suspended
particulate matter (SPM) and sediments from both coastal and off-shore sites
throughout the world ocean (Jiang et al., 1994; Versteegh et al., 1997;
Rampen et al., 2014b). LCDs can be preserved in marine sediments for long
periods of time and their distribution can reflect the environmental
conditions at the time they were produced.
The most abundant LCDs in seawater are the saturated C28 and
C30 1,13-diols, C28 and C30 1,14-diols, and
C30 and C32 1,15-diols (Rampen et al., 2014b), which are
all likely produced by phytoplankton. However, the marine biological sources
of LCDs are still not fully clear because, in contrast with the widespread
occurrence of LCDs in the sediment, few marine taxa have been shown to
contain these lipids. Eustigmatophyceae contain C30 1,13-,
C30 1,15-, and C32 1,15-diols (Volkman et al., 1992;
Rampen et al., 2014a), but they comprise mostly freshwater species, and only
a few rare marine representatives from the genus Nannochloropsis are
known (Andersen et al., 1998; Fawley and Fawley, 2007). Furthermore, the
distribution of LCDs in the marine environment does not match that of LCDs of
marine Eustigmatophyceae (Volkman et al., 1992; Rampen et al., 2012). Species
of the diatom genus Proboscia and the dictyochophycean
Apedinella radians contain C28-32 1,14-diols (Sinninghe
Damsté et al., 2003; Rampen et al., 2009, 2011), with the former
accounting for significant proportions of marine biomass mostly in upwelling
regions (Moita et al., 2003; Lassiter et al., 2006), whereas the latter has
been occasionally observed in estuarine environments (Seoane et al., 2005;
Bergesch et al., 2008). Few other marine species from classes genetically
related to diatoms and Eustigmatophyceae have been recently shown to produce
LCDs (Table S1 in the Supplement). All the known LCD-producing phytoplankters
belong to the eukaryotic supergroup Heterokontophyta, a division which
includes, among others, diatoms and brown seaweeds. The widespread occurrence
of LCDs in the marine environment, despite the restricted abundance and
distribution of known marine LCD producers, suggests that these compounds may be
produced by unknown phytoplankton species. In addition, LCD in the marine
environment might also derive from vegetal debris of terrestrial or riverine
origin. For example, C30-36 diols functionalized at the 1- and the
ω18 or ω20 positions have previously been reported to occur
in ferns (Jetter and Riederer, 1999; Speelman et al., 2009; Mao et al., 2017)
and suggested to be part of the leaf cuticular waxes. Similarly,
C26-32 diols have been occasionally detected in other plants (Buschhaus
et al., 2013). This suggests that vegetal debris may in principle also source
LCDs in seawater.
Several indices, based on ratios between the different diols, have been
proposed for the reconstruction of past environmental conditions. The Diol
Index, reflecting the proportion of C28 and C30
1,14-diols over the sum of C28 and C30 1,14-diols and the
C30 1,15-diol, has been proposed to track ancient upwelling
conditions since the 1,14-diols are believed to be mostly produced by
upwelling diatoms of the genus Proboscia (Rampen et al., 2008).
Another index, the long chain diol index (LDI), which is based on the
proportion of the C30 1,15-diol over the C28 and
C30 1,13-diols, shows a strong correlation with sea surface
temperature (SST) and is used to determine past SST (Rampen et al., 2012;
Plancq et al., 2014; Rodrigo-Gámiz et al., 2015). In addition, since the
C32 1,15-diol is the major component of the LCDs of freshwater
Eustigmatophyceae (Volkman et al., 1992; Rampen et al., 2014a), the
fractional abundance of the C32 1,15-diol has been suggested to be
a marker of riverine input in seawater (de Bar et al., 2016; Lattaud et al.,
2017a, b). Other markers for riverine inputs in seawater are the
C30-36 1,ω20-diols which are produced by the freshwater
fern Azolla (Speelman et al., 2009; Mao et al., 2017). However,
application of these proxies in the marine realm remains uncertain. For
example, the growth of Proboscia spp. is typically promoted under
low concentrations of dissolved silica, whereas other diatoms dominate the
upwelling area under higher silica concentrations (Koning et al., 2001),
making the Diol Index ineffective in predicting upwelling conditions when
communities are dominated by other diatoms. In addition, the sources of the
marine C28-32 1,13- and 1,15-diols are unknown, complicating the
application of the LDI as a proxy.
A way of assessing the sources of biomarker lipids is to compare the
abundance of lipids in environmental samples with the composition of the
microbial community, as determined by genetic methods. For example,
Villanueva et al. (2014) analysed both LCDs and eustigmatophycean 18S rRNA
gene sequences in a tropical freshwater lake and found five clades of
uncultured Eustigmatophyceae in the top 25 m of the water column of the
lake, where LCDs were also abundant. Abundance determination by quantitative
polymerase chain reaction (qPCR) highlighted that the number of
eustigmatophycean 18S rRNA gene copies peaked at the same depth as the LCDs,
suggesting that Eustigmatophyceae are a primary source for LCDs in
freshwater (Villanueva et al., 2014). However, one of the limitations of
this approach is that it relies on specific eustigmatophycean primers
designed based on the sequences available in the genetic databases, which
could be biased and not target all the existing LCD biological sources. To
compensate for this limitation high throughput amplicon sequencing of the
18S rRNA gene allows the exploration of the total marine microbial
communities in great detail (Stoeck et al., 2009; Logares et al., 2012;
Christaki et al., 2014; Balzano et al., 2015; de Vargas et al., 2015;
Massana et al., 2015). The combination of these analyses with lipid
composition may potentially assist in identifying the main LCD producers in
marine settings.
HCC cruise track in the western tropical North Atlantic Ocean,
physical seawater properties, and biological parameters. (a) Map of
the sampling stations. Spatial distribution of (b) temperature,
(c) salinity, the concentration of (d) Chl a,
(e) organic carbon concentrations, and the abundance of
photosynthetic (f) picoeukaryotes and (g) nanoeukaryotes.
Temperature, salinity, as well as the concentrations of Chl a and
organic carbon have also been published by Bale et al. (2018). Data were
plotted using ODV software using kriging for interpolation between data
points (Schlitzer, 2002). Dots represent the depth at which SPM was
collected.
In the present study, we quantitatively analysed the composition and
abundance of LCDs in suspended particulate matter (SPM) collected along the
tropical North Atlantic (Fig. 1a) at different depths in the photic zone
(surface, deep chlorophyll maximum (DCM), and bottom of the wind mixed layer
(BWML); see also Bale et al., 2018). The 18S rRNA gene abundance and
composition of the SPM was also analysed by quantitative PCR (qPCR) and high
throughput amplicon sequencing to infer the taxonomic composition and to
compare the abundance of the different taxa with that of the LCDs, in order
to identify the potential marine biological sources of LCDs.
Material and methods
Cruise transect, ancillary data, and SPM collection
Samples were taken during the Heterocystous Cyanobacteria Cruise (HCC)
(64PE393), which took place from 24 August to 21 September 2014 along a
transect on the tropical North Atlantic Ocean (see Bale et al., 2018, for
details). The transect was from Mindelo (Cape Verde) to a location about
500 km from the Amazon River mouth and then westwards along the coast
towards Barbados (Fig. 1a). Temperature, salinity, and nutrient data have previously been reported
in Bale et al. (2018).
Seawater was collected from two or three depths at each station to measure
the concentration of chlorophyll a (Chl a) and the abundances of
photosynthetic picoeukaryotes and nanoeukaryotes. Seawater was collected
during the up cast using Niskin bottles mounted on a CTD frame. The sampling
depths were determined based on the evaluation of the vertical profiles of
temperature, salinity, and chlorophyll fluorescence after the down cast of
the CTD deployment. The depth of the BWML and the DCM were determined based
on the lowest position of the mixed layer and the depth at which the highest
values of chlorophyll fluorescence were observed. For Chl a
determination, seawater was collected from the Niskin bottles and filtered
through 0.7 µm pore-size glass-fibre (Whatman GF/F) filters, followed by frozen storage. Chl a
was extracted with methanol buffered with 0.5 M ammonium acetate,
homogenized for 15 s, and analysed by high-performance liquid
chromatography.
Photosynthetic picoeukaryotes and nanoeukaryotes were enumerated by flow
cytometry according to the protocol of Marie et al. (2005). In short,
1 mL samples were counted fresh using a Becton-Dickinson FACSCalibur
(Erembodegem, Belgium) flow cytometer equipped with an air-cooled Argon laser
(488 nm, 15 mW). Phytoplankton were discriminated based on
their chlorophyll autofluorescence and scatter signature. Cyanobacteria,
i.e. Synechococcus and Prochlorococcus, were not included
in the current study. Size fractionation was performed by gravity filtration
with >3 µm average cell diameter phytoplankton groups
classified as nanoeukaryotic and those with <3 µm average cell
diameter as picoeukaryotic phytoplankton.
Three McLane in situ pumps (McLane Laboratories Inc., Falmouth) were used to
collect SPM from the water column for the analysis of both lipids and
microbial communities. As with the collection of seawater with Niskin bottles
for Chl a and flow cytometry analyses, the in situ pumps were
deployed at the surface (3–5 m depth), the BWML, and the DCM
(Table S2). Between 100 and 400 L of seawater was pumped and the SPM
was collected on pre-combusted 0.7 µm GF/F filters (Pall
Corporation, Washington) and immediately frozen at -80∘C.
For the determination of the organic carbon concentrations, SPM was freeze
dried and analysis was carried out using a Flash 2000 series Elemental
Analyzer (Thermo Scientific) equipped with a thermal conductivity detector.
Lipid extraction and analyses of LCDs
Lipids were extracted from the GF/F filters as described previously (Lattaud
et al., 2017b). Briefly, 1/4 of the filters were dried using a LyoQuest
(Telstart, Life Sciences) freeze-dryer and lipids were extracted using base
and acid hydrolysis. The base hydrolysis was achieved with 12 mL of a
1 M KOH in methanol solution by refluxing for 1 h.
Subsequently, the pH was adjusted to 4 with 2 M
HCl:CH3OH (1:1, v/v) and the extract was transferred
into a separatory funnel. The residues were further extracted once with
CH3OH:H2O (1:1, v/v), twice with CH3OH,
and three times with dichloromethane (DCM). The extracts were combined in the
separatory funnel and bidistilled water (6 mL) was added. The
combined solutions were mixed, shaken, and separated into a
CH3OH:H2O and a DCM phase; the DCM phase was removed
and collected in a centrifuge tube. The aqueous layer was re-extracted twice
with 3 mL DCM. The pooled DCM layers were dried over a sodium sulfate
column and the DCM was evaporated under a stream of nitrogen. The extract was
then acid hydrolysed with 2 mL of 1.5 M HCl in
CH3OH solution under reflux for 2 h. The pH was adjusted to
4 by adding 2 M KOH:CH3OH; 2 mL of DCM and
2 mL of bidistilled water were added to the hydrolysed extract,
mixed, and shaken and, after phase separation, the DCM layer was transferred
into another centrifuge tube. The remaining aqueous layer was washed twice
with 2 mL DCM. The combined DCM layers were dried over a sodium
sulfate column, the DCM was evaporated under a stream of nitrogen, and a
C22 5,17-diol was added to the extract as an internal standard. The
extract was separated on an activated aluminium oxide column into three
fractions using the following solvents: hexane:DCM (9:1,
v/v), hexane:DCM (1:1, v/v), and
DCM:CH3OH (1:1, v/v). The latter (polar) fraction
containing the diols was dried under a gentle nitrogen stream. Diols were
derivatized by silylating an aliquot of the polar fraction with 10 µL N,O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) and 10 µL
pyridine, heating for 30 min at 60 ∘C and adding
30 µL of ethyl acetate. The analysis of diols was performed by gas
chromatography-mass spectrometry (GC-MS) using an Agilent 7990B GC gas
chromatograph, equipped with a fused silica capillary column (25m×320µm) coated with CP Sil-5 (film thickness
0.12 µm), coupled to an Agilent 5977A MSD mass spectrometer. The
temperature regime for the oven was the same as that used by Lattaud et
al. (2017b): held at 70 ∘C for 1 min, increased to
130 ∘C at a rate of 20 ∘Cmin-1, increased
to 320 ∘C at a rate of 4 ∘Cmin-1, held at
320 ∘C for 25 min. The flow was held constant at
2 mLmin-1. The MS source temperature was held at
250 ∘C and the MS quadrupole at 150 ∘C. The
diols were identified and quantified via single ion monitoring (SIM) of the
m/z=299.3 (C28 1,14-diol), 313.3 (C28 1,13-diol,
C30 1,15-diol), 327.3 (C30 1,14-diol), and 341.3
(C30 1,13-diol, C32 1,15-diol) ions (Versteegh et al.,
1997; Rampen et al., 2012). Surface samples, which contained the highest
concentrations of LCDs, were also analysed by full scan to evaluate the
presence of other eustigmatophycean biomarkers such as long chain alkenols
and long chain hydroxy fatty acids. Absolute concentrations were calculated
using the peak area of the internal standard as a reference.
DNA extraction, PCR, qPCR, and 18S rRNA gene sequencing
On ice a small portion of the GF/F filters, corresponding to 1 / 16 of
their initial size, hence containing SPM from ca. 25 L of seawater,
was cut into small pieces using sterile scissors and tweezers. Filter pieces
were then transferred into 2 mL microtubes and the DNA was extracted
using a MOBIO powersoil DNA isolation kit (Qiagen) following manufacturer
instructions. We amplified the hypervariable V4 region of the 18S rRNA which
is considered the best genetic marker for the identification of microbial
eukaryotes (Logares et al., 2012; Massana et al., 2015). The V4 is located in
a central region (565–584 to 964–981 bp for Saccharomyces cerevisiae) of the 18S rRNA and it was amplified from the genomic DNA by PCR
using the universal eukaryotic primers TAReuk454FWD1
(5′-CCAGCASCYGCGGTAATTCC-3′) and TAReuk454REV3
(5′-ACTTTCGTTCTTGATYRA-3′) (Stoeck et al., 2010). Primers were modified
for multiplex sequencing on a Roche 454 GS FLX system: a 454-adapter A
(CCATCTCATCCCTGCGTGTCTCCGACTCAG), a key (TCAG), and a 10 bp sample-specific
Multiple Identifier (MID, Table S3) were bound to the 5′ end of the forward
primer, whereas a 454-adapter 2 (CCTATCCCCTGTGTGCCTTGGCAGTCTCAG) and a unique
MID (CGTGTCA) were bound to the 5′ end of the reverse primer for all the
samples. The PCR mixture included 25 µL Phusion Flash
High-Fidelity PCR Master Mix (ThermoFisher Scientific) 19.1 µL
deionized water, 1.5 µL dimethyl sulfoxide, 1.7 µL from
each primer, and 25 ng genomic DNA, and the V4 region was amplified
using the same thermal cycling as described by Logares et al. (2012).
Amplicons were visualized on a 1 % agarose gel, V4 bands were excised and
subsequently purified using a QIAquick Gel Extraction Kit (Qiagen), and DNA
concentration was measured by Qubit fluorometric quantitation (ThermoFisher
Scientific). For each sequencing run, 20 samples were pooled in equimolar
amounts and sequenced using a 454 GS-FLX Plus (Macrogen Korea). Some samples
yielded a low number of reads and were re-sequenced; overall 77 samples were
sequenced in five sequencing runs.
To determine the concentration of total 18S rRNA genes within the seawater
sampled, we carried out qPCR using the same primers and the same cycling
conditions as described above. qPCR analysis was performed on a Biorad
CFX96TM Real-Time System/C1000 Thermal cycler equipped with CFX
Manager™ Software. Abundance of 18S rRNA gene
sequences was determined with the same primer pair
(TAReuk454FWD1/TAReuk454REV3) used for the 18S rRNA gene diversity analysis.
Each reaction contained 12.5 µL MasterMix phusion, 8.25 µL deionized nuclease-free water, 0.75 µL DMSO, 1 µL
from each primer and 0.5 µL Sybr green, and 1 µL of DNA
template. Reactions were performed in iCycler iQTM 96-well plates (Bio-Rad).
A mixture of V4 18S rRNA gene amplicons obtained as described above was used
to prepare standard solutions. All qPCR reactions were performed in
triplicate with standard curves from 6.4×103 to 6.4×109
V4 molecules per microlitre. Specificity of the qPCR was verified with
melting curve analyses (50 to 95 ∘C).
Bioinformatic analyses
Bioinformatic analyses were carried out using the python-based bioinformatic
pipeline quantitative insight in microbial ecology (QIIME) (Caporaso et al.,
2010). Overall, we obtained 372 107 raw sequences; reads with a length
comprised between 250 and 500 bp, less than 8 homopolymers, and a phred
quality ≥25 over 50 bp sliding windows were kept for downstream
analyses. Chimeric sequences were then identified by comparison with the
Protist Ribosomal Database 2 (PR2) (Guillou et al., 2013) using the Uchime
algorithm (Edgar et al., 2011) and removed from the dataset along with
singletons (i.e. reads not sharing 100 % identity with at least one
other read).
A total of 238 564 reads remaining after quality filtering were clustered
into 2457 operational taxonomic units (OTUs) based on 95 % sequence
identity using Uclust (Edgar, 2010). Samples containing less than 1000
sequencing reads were removed from the dataset. The taxonomic affiliation of
the OTUs was then inferred by comparison with the PR2 (Guillou et al., 2013)
using BLAST (Altschul et al., 1990) within the QIIME pipeline. Reads from
metazoa and multicellular fungi were removed from the dataset, which finally
contained 1871 OTUs and 184 279 reads. A representative set of sequences
from the OTUs used here has been submitted to the GenBank with the accession
numbers MH913521–MH915389. The abundances of the different taxa in each
sample were estimated by multiplying the percentage of reads by the
concentration of V4 copies measured by qPCR. Taxa containing C28-32
diol producers were extracted from the dataset and plotted using Ocean Data
View (ODV) (Schlitzer, 2002).
Statistical analyses
Linear regression analyses between the concentrations of the different LCDs
were performed to assess whether some of the LCDs were likely to derive from
a common source. To investigate relationships between LCDs and environmental
conditions we calculated the Spearman rank correlation coefficient (r)
using the vegan R package (Dixon, 2003). The environmental data used were
temperature, salinity, TOC, nutrients (nitrate, nitrite, ammonium, phosphate,
and silica), as well the concentration of Chl a and the abundance of
photosynthetic picoeukaryotes and nanoeukaryotes. Samples containing missing
data and outliers were removed from the dataset before the calculations. Both
correlation coefficients and p values were calculated and the latter were
corrected for false discovery rates (Benjamini and Hochberg, 1995).
Correlations were considered significant for p values <0.01.
To investigate the relationships between lipids and microbial taxa, we also
calculated Spearman's rank correlation coefficient between the LCD
concentrations and the abundance of the different taxa at both OTU and class
levels. To this end, taxonomic data were normalized based on the number of V4
copies in the different samples measured by qPCR.
Comparisons at class level provide the advantage of pooling distribution data
from several closely related OTUs, thus reducing the number of zeros (samples
where a given OTU is absent), which complicate statistical analyses of
biological distributions (Legendre and Gallagher, 2001). However, pooling
OTUs at higher taxonomic levels likely leads to combining of species able and
unable to produce LCDs falling into the same taxonomic level. We thus removed
OTUs that were observed in fewer than 19 samples (25 %) and compared the
resulting OTU table with the LCD concentrations. These analyses were
performed using the qiime script
observation_metadata_correlation.py (Caporaso et al., 2010) and
the p values were corrected for false discovery rates (Benjamini and
Hochberg, 1995).
Results
Ancillary data
The HCC cruise sailed across tropical Atlantic waters (Fig. 1a) in late
summer and was targeted at SPM from the photic zone collected at the surface,
the BWML, and the DCM. The extent of the photic zone as well as the depths of
both the BWML and DCM at each station were assessed based on the vertical
profiles of temperature, salinity, and chlorophyll fluorescence. The
temperature of photic zone waters ranged from 15 to 29 ∘C
(Fig. 1b, Table S2), the BWML depth comprised between 9 and 40 m,
whereas the depth of the DCM ranged from 45 to 105 m. Temperatures
varied at the DCM, increasing westwards, whereas they were relatively
constant at the surface and BWML. Salinity varied between 29 and
36.5 gkg-1 (Fig. 1c, Table S2) at the surface, whereas it was
fairly constant in the DCM (36 to 37). The concentration of Chl a
varied from 34 to 470 ngL-1 (Fig. 1d, Table S2), with the lowest
values measured at the surface of the easternmost (1 to 6) and westernmost
(21 to 23) stations and the relatively higher concentrations in surface
waters of the shallowest stations (11 to 13) located above the continental
shelf and about 500 km off the Amazon River mouth (Fig. 1a, Table S2). The
POC concentration ranged from 0.6 to 13 mgL-1 and also peaked at
the surface for the shallowest stations (Fig. 1e, Table S2).
Photosynthetic picoeukaryotes, quantified by flow cytometry, were more
abundant at the DCM compared to the surface and BWML (Fig. 1f). Their
abundance peaked at the DCM of Stations 1 and 2 (>1.5×107 cellL-1), whereas for surface waters the highest values
were measured at Stations 11 to 13. In contrast, photosynthetic
nanoeukaryotes did not vary substantially through the water column and their
abundance peaked at the surface of Station 17, reaching a density of 1.4×105 cellL-1 (Fig. 1g).
Spatial distribution of the concentration of LCDs:
(a) C28 1,13-diol, (b) C28 1,14-diol,
(c) C30 1,13-diol, (d) C30 1,14-diol,
(e) C30 1,15-diol, and
(f) C32 1,15-diol. Data were plotted using ODV software
using kriging for interpolation between data points (Schlitzer, 2002).
Long chain alkyl diols
Six LCDs were detected, the C28 and C30 1,13-diols,
C28 and C30 1,14-diols, and C30 and
C32 1,15-diols (Fig. 2, Table S2). The C30 1,15-diol
dominated all samples, accounting for >95 % of the total LCDs, and its
concentration ranged from 100 to 1600 pgL-1. The concentration
of the C28 1,13-diol ranged from 0 (i.e. undetectable) to
55 pgL-1, whereas the highest concentration measured for the
C28 1,14-diol was 64 pgL-1. The other minor diols were
usually more abundant than the C28 diols, reaching concentrations
of up to 190 pgL-1 for the C30 1,13-diol,
240 pgL-1 for the C30 1,14-diol, and
480 pgL-1 for the C32 1,15-diol (Fig. 2). The
concentration of the C28 1,13-diol peaked in the surface waters of
Station 10, but it was below the detection limit in 19 samples from different
depths and stations (Fig. 2a). The C28 1,14-diol reached its
highest concentrations at the DCM of Station 12 (64 pgL-1) and
at the surface of Station 13 (45 pgL-1) and tended to be more
abundant in the waters of the eastern stations (Fig. 2b). The concentrations
of both C28 1,13- and C28 1,14-diols did not vary
significantly with depth (t-test, p value >0.1), while those of the
C30 1,13-, C30 1,14-, and C30
1,15-diols were higher in the mixed layer (surface and BWML) compared to the
DCM (p value <0.01).
The concentration of the C30 1,13-diol peaked at the surface of
Stations 10 and 14 (Fig. 2c), while that of the C30 1,14-diol reached
its maximum at the BWML of Stations 7 and 8 (Fig. 2d). The highest
concentration of the C30 1,15-diol was measured at the surface of
Station 17 (16 ngL-1, Fig. 2e). The concentration of the C32
1,15-diol peaked in the surface waters of Stations 10 and 14 and at the DCM
of Station 7 (Fig. 2f) and its concentration did not vary significantly with
depth. The concentrations of both the C30 and C32 diols peaked in
the mixed layer of Stations 7–10 and 14–17, which are located in close
proximity to the Amazon Shelf (Fig. 2c–f).
Average fractional abundance of the reads obtained by 18S rRNA
gene sequencing of SPM from the western tropical Atlantic Ocean over the
various classes of eukaryotes. The V4 fragment of the 18S rRNA gene was
sequenced using universal eukaryotic primers. Samples were pooled according
to depth and the average contribution from each group at the different depth
is shown. Error bars represent the standard deviation in the data from the
various stations.
Eukaryotic 18S rRNA gene diversity analysis
Sequencing of the hypervariable V4 region of the 18S rRNA gene of 68 SPM
samples resulted in 238 564 reads with an average of 4987 reads per sample
(Table S2). Reads were clustered based on 95 % sequence identity and,
after removal of reads of Metazoa and multicellular fungi, we obtained 1871
OTUs. Rarefaction analyses indicate that >90% of the genetic
diversity was captured (Fig. S1 in the Supplement), suggesting that no sample
was undersequenced. Most (>90%) reads sequenced here were assigned to
Dinophyceae, Syndiniales, Haptophyta, and Radiolaria (Fig. 3).
Samples were grouped according to the depth layer (surface, BWML, and DCM)
and analysis of similarity (anosim) revealed that the average variance
between samples from different groups was higher than the average variance
between samples from the same group (p value ≈0.001), indicating
that the eukaryotic community was mostly influenced by the water depth rather
than the geographic location. The proportion of reads from Dinophyceae,
Syndiniales, and Haptophyta was slightly higher in the mixed layer compared
to the DCM, whereas Radiolaria and Pelagophyceae tended to be slightly more
abundant in deeper waters (Fig. 3). All samples except surface waters from
Station 12, the BWML from Station 11, and the DCM from Station 22 exhibited
high contributions (>50%) from Dinophyceae and Syndiniales (Fig. S2).
Radiolaria dominated the DCM at Station 22, diatoms were relatively abundant
(≈10–20 %) at the surface of Stations 12–14 and the BWML of
Station 12, and the contribution of diatom reads was <5% for all the
other samples.
Distribution of the 18S rRNA gene reads associated with known LCD
producers.
Taxon
Florenciellales
Heterosigma
Eustigmatophyceae
Proboscia
Total
No. of samplesa
28
2
8
2
35
Surface
12
1
2
0
12
BWMLb
11
0
2
2
13
DCMc
5
1
4
0
10
No. of readsd
99
10
45
3
157
% total
0.04
0.004
0.02
0.001
0.06
Surface
48
4
25
0
77
BWML
41
0
9
3
53
DCM
10
6
11
0
27
a Number of samples where 18S rRNA gene reads from
C28-32 diol producers were found. Overall, 68 samples were screened
for the
presence of 18S rRNA genes affiliated with LCD producers.b Bottom wind mixed layer.c Deep chlorophyll maximum.d Number or proportion of 18S rRNA gene reads associated with
C28-32 diol producers.
18S rRNA gene reads of only four taxa containing known LCD producers were
detected within our dataset: Proboscia spp., Florenciellales,
Heterosigma spp., and Eustigmatophyceae (Table 1). In 33 out of 68
SPM samples we did not detect any 18S rRNA gene read from known LCD
producers, whereas reads from these taxa accounted for <0.1% of the
total 18S rRNA reads in 24 samples, 0.1 % to 0.5 % in 8 samples,
0.5 % to 1 % in 2 samples, and 1.5 % in 1 sample (Station 20,
BWML). The 18S rRNA gene reads from putative LCD producers were mostly
recovered from the mixed layer (Table 1). Florenciellales was the most
abundant taxon among the known LCD producers since it exhibited the highest
number of reads (99) and was present in 28 out of 68 samples. The other taxa
of putative LCD producers were detected only in 8 (Eustigmatophyceae) or 2
(Proboscia sp. and Heterosigma akashiwo) samples (Table 1)
accounting for 3 (Proboscia) to 45
(Eustigmatophyceae) reads. Eustigmatophyceae (mostly affiliated with
Nannochloropsis oculata) were found at the surface for Stations 11,
12, and 13, as well as at the DCM of Station 20 (Fig. 4a).
Spatial distribution of the 18S rRNA gene fragments related to taxa
containing LCD producers at different stations and depth.
(a) Pelagophyceae, (b) Chrysophyceae,
(c) Dictyochophyceae, (d) radial centric diatoms,
(e) Eustigmatophyceae, and (f) Raphidophyceae. Data were
plotted using ODV software using kriging for interpolation between data
points (Schlitzer, 2002).
Since species genetically related to cultivated microalgae known to produce
LCDs may also contain LCDs, we expanded our community composition analyses to
groups at a higher taxonomic level and focused on those classes or divisions
that contain LCD producers (Table S1). Specifically we investigated the
distribution of Eustigmatophyceae, since they are the most well-known class
of LCD producers, Pelagophyceae and Chrysophyceae, which include the LCD
producers Sarcinochrysis marina and Chrysosphaera parvula,
respectively (Table S1), Dictyochophyceae, which include Apedinella radians (Rampen et al., 2011), and Raphidophyceae, which include two LCD
producers, H. akashiwo and Haramonas dimorpha. We did not
detect any representative of Pinguiophyceae, a class which includes the LCD
producer Phaeomonas parva (Table S1). Reads associated with
Pelagophyceae, and mostly (97 %) affiliated with Pelagomonas calceolata, were recovered more frequently as they were present in 55
samples with an average abundance of 85 reads (2 % of total reads) per
sample and a maximum value of 935 reads (12 % of total) in the DCM of
Station 23 (Fig. 4b). Pelagophyceae reads were mostly detected in the DCM and
were particularly abundant at the three westernmost stations investigated,
where they comprised 8 % of total reads (Fig. 4b).
Chrysophyceae and Dictyochophyceae were also detected in most samples (54 and
57 samples, respectively) and their reads were recovered more frequently at
the surface and BWML of the westernmost part of the transect
(Stations 20–23) and at the surface of Stations 3–4 (Fig. 4c and d). Their
18S rRNA gene reads reached abundances of up to 55 and 41 reads (0.4 %
and 0.6 % of the total, respectively), for Chrysophyceae and
Dictyochophyceae, respectively, in the BWML of Station 20 (Table S4).
Raphidophyceae were present only in three samples from Stations 11, 12, and
13 (Fig. 4e).
Scatter plots of the concentrations of the different LCDs in the
western tropical Atlantic Ocean. (a) C30 1,13-diol vs. C30 1,15-diol, (b) C32 1,15-diol vs. C30 1,15-diol, (c) C28 1,13-diol vs. C30 1,13-diol, (d) C28 1,13-diol vs. C30 1,15-diol, (e) C30 1,14-diol vs. C28 1,14- diol, and (f) C30 1,14-diol vs. C30 1,15-diol.
Discussion
Comparison of diol distributions
In general, it is thought that 1,13- and 1,15-diols derive from a different
source than 1,14-diols in the marine realm (Sinninghe Damsté et al.,
2003; Rampen et al., 2007, 2011). Indeed, linear regressions showed that the
concentration of the C30 1,15-diol is significantly correlated with
those of the C30 1,13- and C32 1,15-diols (Fig. 5a–b).
We did not observe any significant correlation between the concentrations of
the C28 1,13- and C30 1,13- or C30 1,15-diols
(Fig. 5c–d), which might be due to the fact that the C28 1,13-diol
was below the detection limit in 19 out of 71 samples and its distribution
could be compared to that of the widespread C30-32 diols only for
the remaining 52 samples. This low abundance of the C28 1,13-diol
is consistent with the relatively high temperatures observed for the tropical
Atlantic Ocean (Fig. 1b), since the LCD core top calibration study has
revealed that the fractional abundance of the C30 1,15-diol is high
and that of the C28 1,13-diol is low when SST is relatively high
(Rampen et al., 2012).
The concentration of the C28 1,14-diol was not correlated with that
of the C30 1,14-diol (Fig. 5e), potentially suggesting a different
origin for the C28 and C30 1,14-diols. However, the
concentration of the C30 1,14-diol was significantly correlated
with the C30 1,15-diol (Fig. 5f). This is quite surprising as the
1,14-diols in seawater have been suggested to derive from Proboscia
spp. (Sinninghe Damsté et al., 2003; Rampen et al., 2009), and to a
lesser extent from A. radians (Rampen et al., 2011), whereas the
1,13- and 1,15-diols are thought to be associated with Eustigmatophyceae
(Rampen et al., 2014a, and references cited therein).
Previous studies highlighted indeed good correlations in the fluxes of
C28 and C30 1,14-diols in the water column of the Arabian
Sea (Rampen et al., 2007) and the northwestern Indian Ocean (Rampen et al.,
2008). Proboscia spp. also contain unsaturated 1,14-diols which
were not found here; specifically the warm water species Proboscia indica is dominated by C28:1 and C30:1 1,14-diols
(Rampen et al., 2007), suggesting that the 1,14-diols found here do not
derive from Proboscia spp. This is confirmed by the absence or very
low proportions of 18S rRNA gene reads from the major producers of
C28-30 1,14-diols, i.e. Proboscia spp. and
A. radians (Table 1). This suggests different sources for the
C28 and C30 1,14-diols. Since the C30 1,15-diol
accounted for >95% of the C28-32 diols, it is possible that
the C30 1,14-diol was biosynthesized in low amounts, along with
C30 1,13-diol, by the producers of the C30 1,15-diol.
This is supported by the fact that Eustigmatophyceae can contain small
amounts of 1,14-diols along with large quantities of 1,15-diols (Rampen et
al., 2014a); specifically, the C28 1,14-diol accounts for up to
15 % of the total LCDs in Pseudostarastrum enorme, and lower
proportions (1 %–5 %) of C30 1,14-diols were previously
found in Vischeria punctata and Eustigmatos vischeri
(Rampen et al., 2014a).
It has been reported that the distributions of LCDs can be affected by
riverine input, which is reflected by elevated amounts of the C32
1,15-diol (>10 %, de Bar et al., 2016; Lattaud et al., 2017b).
However, the fractional abundance of the C32 1,15-diol in the SPM
is low (0 % to 4 %, data not shown), far lower than the values
typically measured in river-influenced ecosystems such as the Iberian
Atlantic Margin (de Bar et al., 2016), the Kara Sea (Lattaud et al., 2017b),
or the Congo River plume (Versteegh et al., 2000). We did not detect other
eustigmatophycean biomarkers such as C32 alkenols or
C30-32 hydroxy fatty acids (Volkman et al., 1992; Gelin et al.,
1997b), suggesting that riverine or marine Eustigmatophyceae were unlikely to
source the C28-32 diols found here. The HCC cruise took place in a
period of the year (August/September) when the water discharge from the
Amazon River is typically low (Molleri et al., 2010), thus leading to low
inputs of riverine organic matter into the sea. The distribution of LCDs in
the sampled SPM is thus likely not impacted by terrestrial input of LCDs.
Beyond Heterokontophyta, LCDs may also be produced by lower (Speelman et al.,
2009) and higher (Wen and Jetter, 2007; Racovita and Jetter, 2016) plants.
However, only four reads from our dataset were associated with a plant
species, i.e. Panax ginseng (Table S4), which is not known to
contain LCDs. The near absence of 18S rRNA gene reads from higher plants
confirms the low riverine input of organic matter in the SPM of the tropical
North Atlantic waters analysed here.
Spearman rank correlation coefficients between LCD and
environmental variablesa.
C28 1,13
C28 1,14
C30 1,13
C30 1,14
C30 1,15
C32 1,15
Organic carbon
0.3
0.2
0.2
0.3
0.3
0.3
Salinity
-0.2
0.0
-0.5
-0.7
-0.6
-0.6
Temperature
0.2
-0.1
0.5
0.5
0.5
0.5
Phosphate
0.0
0.2
-0.3
-0.2
-0.3
-0.2
Ammonium
0.0
0.1
-0.3
-0.4
-0.4
-0.2
Nitrite
-0.2
0.0
-0.6
-0.5
-0.6
-0.4
Nitrate
0.0
0.2
-0.4
-0.3
-0.3
-0.5
Silica
0.1
0.0
0.4
0.5
0.5
0.4
Chl a
-0.1
0.0
-0.2
-0.2
-0.3
-0.1
Picoeukaryotes
-0.1
-0.1
-0.4
-0.3
-0.4
-0.2
Nanoeukaryotes
0.0
-0.1
0.1
0.2
0.2
0.2
a Significant (p value <0.01) correlation values are in
bold.
We explored the variations in the concentrations of the LCDs with respect to
environmental data. The C28 1,13- and 1,14-diols, both occurring in
low abundance, did not exhibit significant correlations with any of the
environmental data measured here (Table 2). In contrast, the concentrations
of C30 1,13-, 1,14-, and 1,15-diols exhibited significant but weak
positive correlations with temperature and dissolved silica and weak negative
correlations with salinity and nitrite. The concentration of the
C32 1,15-diol revealed a correlation with the same environmental
variables as the C30 diols, except for dissolved silica and
nitrite, and exhibited a weak negative correlation with the concentration of
nitrate. The correlations found here are likely simply due to different water
masses: the mixed layer, where the highest proportions of LCDs were measured,
exhibited indeed higher temperatures and lower salinities compared to the
DCM. We repeated the analyses after excluding DCM samples and did not find
strong positive or negative correlations between LCDs and environmental
variables (data not shown). Thus, there does not seem to be a major control
of environmental conditions on the concentrations of LCDs.
Comparison with eukaryotic abundance and diversity
Although C28-32 diols are likely produced by phytoplankton, the
variability in LCD abundance is not correlated with that of Chl a
concentration, or photosynthetic picoeukaryote and nanoeukaryote abundances
(Table 2). This lack of correlation suggests that the LCD producers accounted
for only a small proportion of phytoplankton. The high proportion of
Dinophyceae, Syndiniales, and Radiolaria revealed by our genetic libraries
agrees with previous studies on marine microbial communities based on 18S
rRNA gene sequencing in different environments (Comeau et al., 2011;
Christaki et al., 2014; de Vargas et al., 2015). However, these taxa do not
necessarily dominate marine microbial communities, and so our results are
likely due to a relatively high number of rRNA gene copies per cell (Zhu et
al., 2005). Larger-sized dinoflagellates such as Prorocentrum minimum and Amphidinium carterae can contain up to 1000 gene copies
per cell compared to <10 of rRNA gene copies for smaller-sized (<3 µm) species of Chlorophyta, Pelagophyceae, and Haptophyta (Zhu
et al., 2005).
LCD producers
Although the primers used in this study have a perfect match with the 18S
rRNA gene sequences of most eukaryotes (including all the classes containing
LCD producers), and the rarefaction curves indicate that we sampled an
appropriate (i.e. >90 %) proportion of the eukaryotic community, we
cannot fully exclude that some species will remain undetected because of
undersampling or primer mismatches. Moreover, the large number (100–1000) of
rRNA gene copies per cell present within dinoflagellates and Radiolaria might
have somehow affected the detection of LCD producers. In particular,
Nannochloropsis salina has been shown to possess only one to two
copies of the 18S rRNA gene (Zhu et al., 2005), and similarly, the other
marine Nannochloropsis species, which do not differ greatly in size
from N. salina (Fawley and Fawley, 2007), are also likely to have a
low number of 18S rRNA gene copies. Known species of LCD producers were
present in only 51 % of our SPM samples as revealed by sequencing data
(Table 1), whereas the major LCD, the C30 1,15-diol, was present in
all samples. This suggests either (1) that the LCDs found here were produced
by other species which were not detected using the current methodology,
(2) that the LCD producers were undersampled because of their low number of
rRNA gene copies per cell, or (3) that the DNA of the LCD producers was no
longer present in the SPM at the moment of sampling. Specifically, marine
Eustigmatophyceae were represented by only two OTUs (denovo2075,
Nannochloropsis oculata, and denovo229, uncultured Eustigmatophycea,
Table S4) detected in only eight samples, confirming the hypothesis of
Volkman et al. (1992) and Rampen et al. (2012) that they are not the major
producers of LCDs in the marine environment. Even if we expand our analyses
of LCD-related species to a higher taxonomic level, we do not find large
proportions of 18S rRNA reads (generally <0.9 % of total reads) except
for the class Pelagophyceae, which accounts for up to 12 % of total reads
(Fig. 2a–e). However, Pelagophyceae are unlikely to be the source of any of
the LCDs found here because their vertical distribution (i.e. mostly
detected in the DCM, Figs. 3, 4b) does not correspond well to that of LCDs,
which were either more abundant in the upper layers (C30 1,13-,
1,14-, and 1,15-diols and C32 1,15-diol) or did not vary greatly
with depth (C28 diols, Fig. 2). Chrysophyceae and Dictyochophyceae
were instead more abundant in the upper layers (Fig. 4b–c), and although
none of the three known LCD producers from these classes produces the most
abundant LCD detected in the SPM, i.e. C30 1,15-diol (Table S1),
other species within the Chrysophyceae and Dictyochophyceae may possibly be a
source for the C30 diols.
The C28 diols exhibited higher concentrations at the BWML of
Station 12 and at the surface in Station 13 (Fig. 2a and b), and higher
proportions of 18S rRNA gene reads were recovered from Pelagophyceae
(2.4 %) and Eustigmatophyceae (0.5 %), at the surface of
Stations 11–12 (Fig. 4d–f). The scattered occurrences of these groups and
the mismatches in distributions when compared to the LCDs suggest that the
LCDs in the tropical North Atlantic Ocean are unlikely to derive from
Pelagophyceae, radial centric diatoms, Raphidophyceae, and/or
Eustigmatophyceae.
Overall the abundance of known LCD producers is low and scattered and does
not match the observed abundance patterns observed for the LCDs, suggesting
that most of the LCDs measured here were not produced by any of these
species.
Correlations between the abundance of OTUs and LCD concentration
Since LCDs have been shown to be present within two genetically distant
eukaryotic supergroups, the Heterokontophyta and the Archaeplastida, the
latter including plants as well as green and red algae, the genetic and
enzymatic machinery required for the biosynthesis of LCDs might be present in
other genera and classes, including uncultured species. We, therefore, also
compared the concentration of LCDs with the composition of the entire
eukaryotic microbial community, normalized with respect to the 18S rRNA gene
abundance, at both class and OTU levels to identify co-occurrence patterns.
No significant correlation was found at class level (data not shown), whereas
the correlations at the OTU level were weak (r≤0.60) but significant
(p value <0.01) for 27 OTUs affiliated with 11 different classes
(Table 3). A reason behind the lack of correlation between taxonomic classes
and LCDs can be that pooling OTUs at higher taxonomic levels likely leads to
combining the LCD producers with species which are unable to produce LCDs but
which fall into the same taxonomic level. The ability of microorganisms to
biosynthesize LCDs can indeed vary, even between genetically related species;
some genera include both LCD producers and species which do not contain LCDs
(Table S1).
Correlation coefficient (r) for the OTUs, representing 95 % of
the sequence identity, whose abundance was correlateda with the
concentration of LCDs in SPM samples obtained in the HCC cruise.
OTU IDb
Taxon
Class
C30 1,13
C30 1,14
C30 1,15
C32 1,15
Total 1,13
Total 1,14
Total 1,15
denovo2033
Choreotrichia
Spirotrichea
0.40
denovo2137
Climacocylis scalaria
0.45
0.49
0.45
0.49
denovo940
Laboea strobila
0.53
0.46
0.60
0.56
0.45
0.59
denovo685
Oligotrichia
0.41
0.40
denovo1804
Pseudotontonia
0.56
0.47
0.56
0.47
0.53
0.41
0.57
denovo492
Blastodinium spinulosum
Dinophyceae
0.43
0.44
0.46
0.45
denovo720
Ceratocorys horrida
0.46
0.44
0.45
denovo1682
Neoceratium fusus
0.47
denovo526
Protodinium simplex
0.43
0.44
0.48
0.43
denovo267
Pyrophacus_steinii
0.43
0.42
denovo732
Dino Group I Clade 4
Syndiniales
0.40
0.46
0.41
denovo555
Dino Group II Clade 2
0.49
0.41
0.48
denovo1077
Dino Group II Clade 7
0.44
0.42
0.43
denovo1834
Dino Group II Clade 8
0.44
0.45
0.45
denovo1145
Dino Group II Clade 17
0.50
0.49
0.53
0.42
0.48
denovo2080
Dino Group II Clade 23
0.40
0.43
0.40
denovo725
Prasino Clade 9B
Prasino Clade IX
0.42
0.41
0.41
denovo1066
Pterocystida
Centroheliozoa
0.46
0.46
denovo400
HAP3
Haptophyta
0.47
0.49
0.47
0.48
denovo2132
Phaeocystis
0.46
denovo972
Haptolina
0.44
denovo465
Chrysophyceae Clade G
Chrysophyceae
0.44
0.43
0.45
0.42
denovo1680
Chrysophyceae Clade H
0.44
0.42
0.48
0.42
denovo1988
Raphid pennate
diatoms
0.41
0.41
denovo873
Pedinellales
Dictyochophyceae
0.56
0.45
0.55
0.52
0.55
0.56
denovo958
Florenciellales
Dictyochophyceae
0.43
0.45
0.44
denovo2433
Unidentified picozoan
Picozoa
0.49
0.50
0.55
0.47
0.46
0.55
a Only significant (p value <0.01 after FDR correction)
correlations are shown.b OTUs closely related to known LCD producers are in bold.
The C30 1,15-diol exhibited significant correlations (p<0.01)
with 23 OTUs and, overall, 27 OTUs were significantly correlated with
C30 or, to a lesser extent, C32 diols (Table 3). Of the
27 OTUs, 4 OTUs were affiliated with classes containing known LCD producers
(Chrysophyceae and Dictyochophyceae, Table 3). The abundance of the two
chrysophycean OTUs (denovo465 and denovo1680, Table 3) exhibited significant
correlations with the concentrations of both C30 1,13- and
1,15-diols and accounted for 52 % of the total reads from this class and
the only known LCD producer from this class (Chrysosphaera parvula)
was found to contain C32 1,15-diol (Sebastiaan Rampen, unpublished results). The two OTUs affiliated with
Dictyochophyceae (denovo873 and denovo958) and exhibiting positive
correlation with C30-32 diols, cluster within Pedinellales and
Florenciellales families, respectively, and are thus closely related to two
known LCD producers, Florenciella parvula and Apedinella radians. However, F. parvula contains C24 1,13-,
C24 1,14-, and C24 1,15-diols (Sebastiaan Rampen, unpublished results) and A. radians
produces C28, C30, and C32 1,14-diols (Rampen
et al., 2011), whereas the two dictyochophycean OTUs denovo873 and denovo958
exhibited a positive correlation with the C30 1,15-diol (Table 3).
The correlation values found here are nearly all low (r≈0.4–0.5),
raising the question of whether these relationships reflect the ability of
these species to produce LCDs or whether they are simply driven by other
environmental conditions leading to similar spatial distributions of OTUs
and LCDs. Other OTUs showing significant correlations with C30
1,15-diols represent species that are rare in the marine environment. For example, Centroheliozoa (OTU denovo1066) are mostly known as freshwater
predators (Slapeta et al., 2005), and in seawater, they have only been
sporadically detected in anoxic environments (Stock et al., 2009; Stoeck et
al., 2009), suggesting that the centroheliozoan reads found here are
unlikely to derive from active microorganisms. In contrast, the other OTUs
include marine representatives commonly found in the photic zone of seawater
and thus the reads found here might derive from living organisms:
Syndiniales are intracellular parasites of other marine protists, and the
genetic clades found here (Group I Clade 4, Group II Clades 2, 7, 8, 17, and
23) are commonly detected in the upper 100 m of the water column (Guillou et
al., 2008). Spirotrichea include several heterotrophic and mixotrophic
marine planktonic ciliates (Agatha et al., 2004; Santoferrara et al., 2017),
whereas Phaeocystis is a widespread primary producer. The OTUs of uncultured classes
exhibiting significant positive correlations with LCDs (Prasino Clade IX and
the HAP-3 clade) are also commonly observed in the photic zone (Shi et al.,
2009; Egge et al., 2015; Lopes dos Santos et al., 2016). However, cultivated
representatives would be required in order to confirm whether species within
these clades are capable of LCD synthesis.
Can 18S rRNA gene-based community composition analysis be used to
determine LCD biological sources?
The lack of correlations of C28 diols with any OTUs as well as the
low degree of correlation between OTUs and C30-32 diols and the
trace abundance or near absence of known LCD producers suggest that the 18S
rRNA genes from the microorganisms sourcing the LCDs were either absent or
present below detection level in the seawater sampled. The fact that we
sampled >90 % of the OTUs potentially present (Fig. S1) and the use of
universal eukaryotic primers suggests that LCD producers have been unlikely
to escape detection. However, the relatively low number of rRNA gene copies
found for N. oculata (Zhu et al., 2005), and likewise also in other
smaller-sized marine Eustigmatophyceae, suggests that LCD producers might
have been undersampled with respect to larger-sized species which can contain
up to 1000 rRNA copies per cell (Zhu et al., 2005).
It should be considered that both the LCDs and DNA in the SPM might derive
not only from active or senescent cells, but also from detritus (Not et al.,
2009). In addition, LCDs can persist in seawater for likely much longer
periods than the DNA of the related LCD producers. Although the biological
function of LCDs is unclear for most species, they have been shown to be the
building blocks of cell wall polymers in Eustigmatophyceae, and likewise they
might occur in other biopolymers of marine or terrestrial origin. In
Nannochloropsis cell walls, LCDs and long chain alkenols are likely
to be bound together through ester and ether bonds to form highly refractory
polymers known as algaenans (Gelin et al., 1997a; Scholz et al., 2014). These
biopolymers are thought to be quite persistent and accumulate in ancient
sediments for millions of years (Tegelaar et al., 1989; Derenne and Largeau,
2001; de Leeuw et al., 2006). Indeed, LCDs are ubiquitous in recent surface
sediments (Rampen et al., 2012) and ancient sediments of up to 65 million
years old (Yamamoto et al., 1996) showing their recalcitrant nature.
Recent laboratory experiments highlighted that LCDs from dead biomass of
Nannochloropsis oculata can persist in seawater for longer than
250 days under both anoxic (Grossi et al., 2001) and oxic conditions (Reiche
et al., 2018). In contrast, much shorter turnover times (6 h to
2 months) are typically reported for extracellular DNA in the oxic water
column (Nielsen et al., 2007). This suggests that the DNA from LCD producers
likely reflects the living eukaryotic community (recently) present when
seawater was sampled, while the LCDs probably represent an accumulation that
occurred over longer periods of time (weeks to months or even years).
Because of this large difference in turnover rates between LCDs and the DNA
from the LCD producers, 18S rRNA gene analysis of environmental samples may
be unsuccessful for identifying LCD producers. This is seemingly in contrast
to a previous study that showed that the LCD concentration in the upper
25 m of the freshwater Lake Challa (Tanzania) was related to the
number of eustigmatophycean 18S rRNA gene copies (Villanueva et al., 2014).
However, Villanueva et al. (2014) used Eustigmatophyceae-biased primers and,
since this was a lake system, Eustigmatophyceae are likely to be the major
source of LCDs in freshwater ecosystems. Importantly, they found a mismatch
for the uppermost part of the water column (0–5 m), where high LCD
abundance (38–46 ngL-1) coincided with few or no
Eustigmatophyceae 18S rRNA gene copies. This pattern was explained by them to
be caused by wind-driven and convective mixing of preserved LCDs, while
phytoplankton adjusted its buoyancy at greater depth (Villanueva et al.,
2014). The high salinity values (≥33 gkg-1) detected in
most surface samples, the low proportions of both C32 1,15-diols
(2.2 % over the total LCDs), and 18S rRNA gene reads associated with
plants (4 out of 238 564), as well as the low input of freshwater from the
Amazon River to the stations analysed here during the sampling period
(Molleri et al., 2010) suggest that the LCDs found here are unlikely to have
a freshwater origin.
Laboratory experiments carried out under different conditions of temperature,
light irradiance, salinity, and nitrate concentrations revealed an average
cellular LCD content of about 23 fgcell-1 (Balzano et al., 2017)
for Nannochloropsis oceanica. The average LCD concentration in the
SPM investigated was ca. 2.6 ngL-1, which would correspond to
ca. 1.1×106 pico/nano algal cells L-1. We detected
average phytoplankton abundances of 3.3×106 cellL-1
for picoeukaryotes and 3.6×104 cellL-1 for
nanoeukaryotes. Although nanoplanktonic Eustigmatophyceae might produce
larger amounts of LCDs than those measured in our previous study (Balzano et
al., 2017), because of their larger cell size, the nanoplankton abundances
measured here are 2 orders of magnitude lower than the densities required to
source the LCDs (1.1×106 cellL-1). Therefore, if the
LCDs measured here were biosynthesized by intact microorganisms in the water
column, nanoplankton alone would not be able to source all the LCDs measured,
and therefore in addition at least one-third of the picophytoplankton should
be able to produce LCDs, which is unrealistic. This supports the idea that
most of the LCDs detected here are of a fossil nature and not contained in
living cells. The higher concentrations of LCDs found in the SPM from the
mixed layer compared to the DCM suggest that LCDs were originally produced at
a higher frequency in the mixed layer. Moreover, their possible fossil nature
indicates that LCDs were likely to persist in the mixed layer for long
periods, eventually associated with suspended particulate matter.
The combination of lipid and DNA analyses is often complicated by different
turnover rates, especially for refractory compounds such as LCDs. Studies
focused on more labile biomarker lipids such as fatty acids or intact polar
lipids can be more successful, e.g. with short branched fatty acids (Balzano
et al., 2011), cyanobacterial glycolipids (Bale et al., 2018), or archaeal
phospholipids (Pitcher et al., 2011; Buckles et al., 2013). Therefore, care
has to be taken in inferring sources of biomarker lipids by the quantitative
comparison of DNA abundance with biomarker lipid concentrations. Analysis of
intact polar lipids, rather than total lipids, might have facilitated the
identification of diol producers.