Introduction
The genus Phaeocystis is a cosmopolitan marine phytoplankton group
that plays a key role in global carbon and sulfur cycles
(Hamm et al., 1999; Matrai et al., 1995; Rousseau et al.,
2007; Schoemann et al., 2005; Smith et al., 1991; Solomon et al., 2003;
Thingstad and Billen, 1994; Verity et al., 2007). Because of their large cell
concentrations during bloom formation, Phaeocystis have a
significant impact on the ocean biogeochemistry through carbon fixation
(Arrigo et al., 1999; Hamm et al., 1999; Matrai
et al., 1995; Rousseau et al., 2007; Schoemann et al., 2005; Smith et al.,
1991; Solomon et al., 2003; Thingstad and Billen, 1994; Verity et al., 2007),
the release of large concentrations of organic carbon upon grazing and viral
lysis (Alderkamp et al., 2007; Hamm et al., 1999; Lagerheim, 1896;
Verity et al., 2007), and export as aggregates out of the photic zone
(DiTullio et al., 2000). Through the production of dimethylsulfide (DMS),
they also directly connect ocean and atmospheric processes and carbon and
sulfur cycling (Smith et al., 2003).
Some Phaeocystis species, including Phaeocystis antarctica, undergo multiple morphotypes and can occur as
flagellated single cells or in gelatinous colonies consisting of thousands
of non-motile cells (Fig. 1). Microscopic and chemical analyses have found
that Phaeocystis colonies are filled with a mucilaginous matrix surrounded by a thin
but strong hydrophobic skin (Hamm, 2000; Hamm et al., 1999). Once formed,
cells typically associate with this outer layer of the colony (Smith et
al., 2003). Colony formation involves the exudation of (muco)polysaccharides
and carbohydrate-rich dissolved organic matter, as well as amino sugars and
amino acids; it is estimated that approximately 50 %–80 % of
Phaeocystis carbon is allocated to this extracellular matrix (Hamm et al.,
1999; Matrai et al., 1995; Rousseau et al., 2007; Solomon et al., 2003;
Thingstad and Billen, 1994). Thus, not only does the colony increase the
size of Phaeocystis by several orders of magnitude, but the extracellular matrix
material also constitutes the majority of measured algal (carbon) biomass
(Rousseau et al., 1990). The colonial form of Phaeocystis has been suggested as a
defense mechanism against grazers (Hamm et al., 1999), a means to sequester
micronutrients such as iron and manganese (Lubbers et al., 1990;
Schoemann et al., 2001), a means of protection from pathogens
(Hamm, 2000; Jacobsen et al., 2007), and as a microbiome vitamin
B12 source (Bertrand et al., 2007). Colony formation of Phaeocystis species,
including P. antarctica and P. globosa, has been linked to numerous physiological triggers including
the synergistic effects of iron and irradiance (Feng et al., 2010),
grazer-induced chemical cues (Long et al., 2007), phosphate
concentrations (Riegman et al., 1992), and the presence of different
nitrogen species (Riegman and van Boekel, 1996; Smith et al., 2003).
Micrographs of (a) a single Phaeocystis in cell culture and
(b) Phaeocystis colonies in a Ross Sea bloom.
The Ross Sea is one of the most productive regions of the Southern Ocean
(Arrigo et al., 1999, 1998; Feng et al., 2010;
Garcia et al., 2009; Sedwick and DiTullio, 1997), and the latter is an
important contributor to the cycling of carbon in the oceans
(Lovenduski et al., 2008; Sarmiento et al., 1998). In the early
spring when the sea ice retreats and polynyas form, phytoplankton blooms and
regional phytoplankton productivity are fed by the residual winter iron
inventory and perhaps iron-rich sea ice melt (Noble et al., 2013;
Sedwick and DiTullio, 1997); blooms have also been linked to changes in
irradiance and mixed layer depth (Arrigo et al.,
1999; Coale et al., 2003; Martin et al., 1990; Sedwick and DiTullio, 1997;
Sedwick et al., 2000). In the Ross Sea polynya (RSP), P. antarctica colonial cells form
almost monospecific blooms until the austral summer season begins,
comprising > 98 % of cell abundance at the peak of the bloom
(Smith et al., 2003). Although diatom abundance dominates in the summer,
the RSP typically harbors the coexistence of flagellated single cells of
P. antarctica along with diatoms (Garrison et al., 2003). During blooms P. antarctica can draw down
more than twice as much carbon relative to phosphate as diatoms and
contribute to rapid carbon export, leaving a lasting biogeochemical imprint
on surrounding waters (Arrigo et al., 1999, 2000;
DiTullio et al., 2000; Dunbar et al., 1998). Recent in vitro iron addition
experiments provide evidence that iron nutrition influences P. antarctica growth in this
region, with increasing P. antarctica biomass after iron addition (Bertrand et al.,
2007; Feng et al., 2010). Moreover, laboratory experiments with P. antarctica have
observed a high cellular iron requirement and variable use of strong organic
iron complexes (Sedwick et al., 2007; Strzepek et al., 2011;
Luxem et al., 2017).
The multiphasic life cycle of P. antarctica in the Ross Sea gives it a spectrum of
nutrient drawdown phenotypes and trophic interactions dependent on the
presence of flagellated versus colonial cells (Smith et al., 2003). Given
its prominence during early spring sea ice retreat, it has been hypothesized
that the triggers of colony formation for Phaeocystis cells are also the triggers of the
spring phytoplankton bloom. Yet experimental and molecular analyses of
potential environmental triggers and how they manifest in changes in
cellular morphology have remained elusive. Little is known about the
mechanisms responsible for colony formation in P. antarctica and how these mechanisms
respond to an environmental stimulus such as iron, both of which appear to
be integral to the ecology and biogeochemistry of P. antarctica.
Materials and methods
Culture experiments
Two strains of Phaeocystis antarctica (treated with Provasoli's antibiotics), CCMP 1871 and CCMP
1374 (Provasoli–Guillard National Center for Culture of Marine
Phytoplankton), and a Ross Sea centric diatom isolate Chaetoceros sp. RS-19 (collected
by Mark Dennett at 76.5∘ S, 177.1∘ W in December 1997 and
isolated by Dawn Moran) were grown in F/2 media with a trace metal stock
(minus FeCl3) according to Sunda and Huntsman (Sunda and
Huntsman, 1995, 2003) using a modified 10 µM EDTA
concentration and an oligotrophic seawater base. Strains were chosen
because they were culturable representatives from two distinct regions in
the Southern Ocean.
Semicontinuous batch cultures were grown at 4 ∘C under
200 µmol photons m-2 s-1 of continuous light. Each strain was acclimated to
the six iron growth condition concentrations for at least three transfers
prior to proteome and growth rate experiments (> 9 generations
per transfer for > 27 generations). The concentration of
dissolved inorganic iron within each treatment was 2, 41, 120, 740, 1200, and 3900 pM Fe' as set by the metal buffer EDTA (where
Fe' / FeTotal=0.039) (Sunda and Huntsman, 2003). During the
experiment, cultures were maintained in 250 mL polycarbonate bottles, and
subsamples were collected every 1–2 days in 5 mL 13×100 mm borosilicate
tubes to measure relative fluorescence units (RFUs) and cell counts in the
treatments. Mid-to-late exponential-phase cultures were harvested for
transcriptome and proteome analysis and cell size was measured for both
strains; cell pellets were stored at -80 ∘C (see the Supplement for additional methods). Cell counts were conducted using a
Palmer–Maloney counting chamber and a Zeiss Axio Plan microscope on 400×
magnification; cell numbers were used to determine the final growth rate of
each strain and/or treatment. During mid-to-late exponential phase
(time of harvest), cell size was determined for both strains (n=20 cells
were counted for each strain), as calculated using the Zeiss 4.8.2 software and
a calibrated scale bar. The number of cells in colonies (versus as single
cells) was determined for strain 1871 only. Briefly, counts (number of cells
associated with colonies versus unassociated) were averaged from 10 fields
of view at five distinct time points (50 fields of view total).
Experimental workflow used in this study. Culture and field
samples (top), transcriptome analyses (second row), sequence database
construction for proteomics (third row), and proteomic and metaproteomic
analyses (bottom row).
Protein extraction, digestion, and mass spectrometry analyses
Proteins from cell pellets (one pellet per treatment, two strains, and six
iron treatments for a total of 12 proteomes) were extracted using the
detergent B-PER (Thermo Scientific), quantified, purified by immobilization
within an acrylamide tube gel, trypsin digested, alkylated and reduced, and
analyzed by liquid chromatography–mass spectrometry (LC-MS) using a Michrom
Advance HPLC with a reverse-phase C18 column (0.3×10 mm ID, 3 µm
particle size, 200 Å pore size, SGE Protocol C18G; flow rate of 1 µL min-1, nonlinear 210 min gradient from 5 % to 95 % buffer
B, where A was 0.1 % formic acid in water and B was 0.1 % formic acid in
acetonitrile; all solvents were Fisher Optima grade) coupled to a Thermo
Scientific Q-Exactive Orbitrap mass spectrometer with a Michrom Advance
CaptiveSpray source. The mass spectrometer was set to perform MS–MS on the
top 15 ions using data-dependent settings (dynamic exclusion 30 s, excluding
unassigned and singly charged ions), and ions were monitored over a range of
380–2000 m/z (see the Supplement for detailed protocol).
Peptide-to-spectrum matching was conducted using the SEQUEST algorithm
within Proteome Discoverer 1.4 (Thermo Scientific) using the translated
transcriptomes for P. antarctica strain 1871 and strain 1374 (Fig. 2, see below).
Normalized spectral counts were generated from Scaffold 4.0 (Proteome
Software Inc.), with a protein false discovery rate (FDR) of 1.0 %, a
minimum peptide score of 2, and a peptide probability threshold of 95 %.
Spectral counts refer to the number of peptide-to-spectrum matches that are
attributed to each predicted protein from the transcriptome analysis, and
the Scaffold normalization scheme involves a small correction normalizing
the total number of spectra counts across all samples to correct for
run-to-run variability and improve comparisons between treatments. The R
package “FactoMineR” (Lê et al., 2008) was used for the PCA; for heatmaps, the package “gplots” was used (Warnes et al.,
2009). Proteomic samples taken from each laboratory condition were not
pooled downstream as part of the analyses; replicates shown for each
treatment are technical replicates.
RNA extraction, Illumina sequencing, and annotation
For P. antarctica cultures total RNA was isolated from cell pellets (one pellet per
treatment, two strains, and three iron concentrations for a total of six
transcriptomes) following the TRIzol Reagent (Life Technologies,
manufacturer's protocol). RNeasy Mini Kit (Qiagen) was used for RNA cleanup,
and DNase I (Qiagen) treatment was applied to remove genomic DNA. Libraries
from poly(A) enrichment mRNA were constructed using a TruSeq RNA Sample
Preparation Kit V2 (IlluminaTM), following the manufacturer's TruSeq RNA
Sample Preparation Guide. Sequencing was performed using the Illumina HiSeq
platform. Downstream, reads were trimmed for quality and filtered. CLC
Assembly Cell (CLCbio) was used to assemble contigs, open reading frames
(ORFs) were predicted from the assembled contigs using FragGeneScan (Rho
et al., 2010), and additional rRNA sequences were removed. The remaining
ORFs were annotated de novo via KEGG, KO, KOG, Pfam, and TigrFam
assignments. Taxonomic classification was assigned to each ORF and the
lineage probability index (LPI, as calculated in Podell and Gaasterland,
2007). ORFs classified as haptophytes were retained for downstream analyses.
Analysis of sequence counts (“ASC”) was used to assign normalized fold
change and determine which ORFs were significantly differentially expressed
in pairwise comparisons between treatments. The ASC approach offers a robust
analysis of differential gene expression data for non-replicated samples
(Wu et al., 2010).
For metatranscriptomes, RNA was extracted from frozen cell pellets using the
TRIzol reagent manufacturer's protocol (Thermo Fisher Scientific) (see
the Supplement for additional details on metatranscriptome
processing).
Ross Sea Phaeocystis bloom: sample collection and protein extraction and
analysis
The meta'omics samples were collected in the Ross Sea (170.76∘ E,
76.82∘ S) during the CORSACS expedition (Controls on Ross Sea
Algal Community Structure) on 30 December 2005 (near pigment station 137;
http://www.bco-dmo.org/dataset-deployment/453377, last access: 1 July 2018) (Saito
et al., 2010; Sedwick et al., 2011). Surface water was concentrated via a
plankton net tow (20 µm mesh), gently decanted of extra seawater, then
split into multiple replicate cryovials and frozen in RNAlater at -80 ∘C for metatranscriptome and metaproteome analysis. The pore size
of the net tow would have preferentially captured the colony form of
Phaeocystis, although filtration with small pore size membrane filters was particularly
challenging during this time period due to the abundance of Phaeocystis colonies and
the clogging effect of their mucilage. Moreover, the physical process of
deploying the net tow appears to have entrained some smaller cells including
the Phaeocystis flagellate cells by adsorption to partially broken colonies and
associated mucilage as observed in the metaproteome results. Two of these
replicate bloom samples were frozen for proteome analysis. A third replicate
sample from this field site was extracted for metatranscriptome analysis as
described above.
Proteins were extracted, digested, and purified following the lab methods
above and then identified first on a Thermo Q-Exactive Orbitrap mass
spectrometer using a Michrom Advance CaptiveSpray source. Then samples were
subsequently rerun on a two-dimensional chromatographic nanoflow system for
increased metaproteomic depth on a Thermo Fusion Orbitrap mass spectrometer
(see the Supplement for further details). Proteins were then
identified within the mass spectra using three databases (Fig. 2): the
translated transcriptome database for both Phaeocystis strains (Database 1), a Ross
Sea metatranscriptome generated in parallel from this metaproteome sample
(Database 2; this transcriptome is a combination of eukaryotic and
prokaryotic communities derived from total RNA and poly(A) enriched RNA
sequencing), and a compilation of five bacterial metagenomes from the
Amundsen Sea polynya (Database 3) (Delmont et al., 2014) using SEQUEST
within Proteome Discoverer 1.4 (Thermo Scientific) (Eng et al.,
1994) and collated with normalized spectral counts in Scaffold 4.0 (Proteome
Software Inc.) (see the Supplement for additional details).
The effect of iron concentration on colony formation and cell
physiology in two strains of P. antarctica – 1871 and 1374. Growth rates collected from
acclimated culture stocks prior to the start of the experiments (a, strain
1871; b, strain 1374) calculated using relative fluorescence units from
three transfers of acclimated cultures (error bars indicate SD, n=3).
Accompanying gray bars represent growth rates calculated based on cell
counts made during the course of the proteome harvest experiments (n=1).
(c) The number of P. antarctica 1871 free-living cells (gray bars) compared to cells
associated with colonies (black bars) showed a shift to a majority of
colonial cells when Fe' > 740 pM. (d) The growth rate of
Ross Sea diatom isolate Chaetoceros sp. strain RS-19 in the same media compositions
(n=1) demonstrated a higher sensitivity to iron scarcity and a lack of
iron contamination in the media. Cell size for strain 1871 (e; black circles)
and strain 1374 (f; white circles); error bars represent SD of n=20 cell
measurements per treatment.
Results and discussion
Physiological response to iron availability: growth limitation and
colony formation
The two strains of P. antarctica (1374 and 1871 from here on) were acclimated to six iron
concentrations to capture the metabolic response under different iron
regimes (Fig. 3a and b). A biphasic response in P. antarctica strain 1871 was observed;
cultures exhibited a clear single-cell versus colony response to low and
high iron, respectively, that was observed by microscopy and readily
apparent to the naked eye due to the millimeter size of the colonies. The three
low iron treatment (2 pM, 41 pM, and 120 pM Fe') cultures contained only
single, flagellated cells, whereas the three higher iron treatments (740 pM,
1200 pM, and 3900 pM Fe') had a majority of colonial cells based on
detailed microscopy counts shown in Fig. 3c. This influence of iron on colony
abundance was observed in an additional experiment, in which colonial cells
were again absent at the lowest three iron concentrations and were present
at the three higher concentrations (Fig. S10). The presence of both colony
and flagellate cells is expected in actively growing populations since
reproduction can involve returning to the flagellate life cycle stage
(Rousseau et al., 1994). Single cells and colonies were not counted in
experiments with strain 1374, as these experiments were conducted prior to
those of 1871 and the iron-induced colony formation observations therein.
However, strain 1374 was observed to become “clumpy” at high iron. This
clumping observation may reflect the loss of a specific factor needed for
the colony completion lost during long-term maintenance in culture. This
interpretation is consistent with the overall similar structural protein
expression patterns observed in both strains described below. Strzepek et
al. also observed covarying of iron concentration and colony formation in
some strains of P. antarctica (Strzepek et al., 2011).
The two strains of P. antarctica were able to maintain growth rates for all but the
lowest of iron concentrations used here, similar to prior studies of P. antarctica strain
AA1 that observed no effect of scarce iron on growth rates (Strzepek
et al., 2011). Parallel experiments with polar diatoms such as Chaetoceros (Fig. 3d)
observed growth limitation at moderate iron abundances using an identical
media composition, indicating (1) that P. antarctica has an impressive capability for
tolerating low iron compared to Chaetoceros and other diatoms (e.g., a Ross Sea
Pseudo-nitzschia sp. isolate, data not shown) and (2) demonstrating an absence of iron
contamination in these experiments. Growth rates for 1871 were significantly
different between the 2 pM Fe' treatment and all other treatments (Student's
t test with Bonferroni correction, p < 0.05; Fig. 3a); there were no
significant differences among growth rates for strain 1374 (Fig. 3b). Cell
size (including both flagellate and colonial cells) decreased with lower
iron concentration, a trend that was statistically significant (ANOVA with
Tukey's HSD test, p < 0.05) for both strains when cell sizes from each
high iron treatment (740 pM, 1200 pM, and 3900 pM Fe') were compared to cell
sizes from each low iron treatment (2 pM, 41 pM, and 120 pM Fe') (Fig. 3e and f).
Principal component analysis (PCA) of the measured proteomes for
each iron condition for strain 1871 and strain 1374 and corresponding line
graphs highlighting the proteins driving the PCA separation (PCAs:
≥0.9 or ≤-0.9). (a, d) Iron treatments (pM Fe') are highlighted
by color (2, black; 41, red; 120, orange; 740, green; 1200, purple; 3900,
blue) and large ellipses indicate confidence ellipses calculated using the R
package FactoMineR. Each small solid circle represents a technical
replicate per iron treatment (n=3); colored open squares represent the
mean of the iron treatment (empirical variance divided by the number of
observations). Proteins with eigenvalues ≥0.9or ≤-0.9 are
plotted in graphs b and c for strain 1871 and (e) and (f) for strain 1374 to
highlight the subset of proteins driving the variance in Dimension 1.
Individual protein spectral counts normalized to total spectral counts for
all treatments for a given protein, written as “normalized relative protein
abundance”, are plotted on the y axis. The six iron treatments (pM Fe') are
plotted from low to high (left to right) on the x axis.
Molecular response to low and high iron concentrations
Global proteomics enabled by peptide-to-spectra matching to transcriptome
analyses revealed a clear statistically significant molecular transition
across the iron gradient for each strain (Fig. 4). The global proteome
consisted of 536 proteins identified in strain 1871 and 1085 proteins
identified in strain 1374 (Table 1; Supplement Data 1) after summing
unique proteins across the six iron treatments. There were 55 proteins
identified in strain 1871 and 64 proteins in strain 1374 (Fig. 4) that drove
the statistical separation of proteomes across iron treatments using
principal component analysis (PCA; axis 1 PCA correlation coefficient ≥0.9 or ≤-0.9). Axis 1 accounted for 49 % of the variance for 1871 and
36 % of the variance for 1374. Moreover, using a Fisher test (p value ≤0.05), 327 (strain 1871) and 436 (strain 1374) of those
proteins detected were identified as significantly different in relative
protein abundance between representative “low” (41 pM Fe') and “high”
(3900 pM Fe') iron treatments. This significant change in the proteome
composition paralleled observations of a shift from flagellate to colonial
cells. Iron-starvation responses and iron metabolism were detected within
the high and low iron PCA protein subsets, including iron-starvation-induced
proteins (ISIPs), flavodoxin, and plastocyanin, demonstrating a
multifaceted cellular response to iron scarcity (Fig. 5). Surprisingly,
there was also a highly pronounced signal in the proteome that appeared to
reflect the structural changes occurring in P. antarctica. These structural proteins
included multiple proteins with protein family (PFam) domains suggestive of
extracellular function, adhesion, and/or ligand binding, including putative
glycoprotein domains (for example, spondin) that were present in the high
iron PCA subset in both strains (Fig. 5); the appearance of these proteins
also corresponded to the occurrence of colonies in strain 1871 (Fig. 1).
Similarly, a distinct suite of proteins was more abundant in the low iron
PCA subset (Fig. 5), including proteins relating to cell signaling (for
example, calmodulin–EF hand, PHD zinc ring finger). A number of proteins
with unknown function were also detected in the PCA subsets: 71 % unknown
for strain 1871 and 42 % unknown for strain 1374 of a total of 311
proteins annotated as hypothetical proteins (Supplement Dataset 1).
Outside of the PCAs, additional iron and adhesion-related proteins
were identified that demonstrated a similar expression profile to the PCA
subset (Supplement Fig. S1).
Heatmaps highlighting the relative protein abundance for the six
treatments for P. antarctica strain 1871 (a) and strain 1374 (b). The darker green color
indicates a greater relative abundance compared to the purple treatments.
The “shared abundance patterns” column features a checkmark when a shared
response to changes in iron availability between the relative protein
abundance and the transcript abundance was observed (for example, both
transcripts and proteins have a higher abundance under high iron compared to
low iron growth or both transcripts and proteins have a higher abundance
under low iron compared to high iron growth). The “field presence” column
indicates whether or not that protein was detected in the field metaproteome
(annotated using Database 1). Protein annotations are based on KEGG,
KOG, and PFam descriptions. Annotations in red are associated with iron
metabolism and those in blue with cell adhesion and/or structure.
Comparison of the total number of proteins and spectra measured in
the proteome for each strain and/or treatment along with the number of
differentially expressed transcripts between select conditions for P. antarctica strain 1871 and strain 1374. Proteins were identified using a
1 % FDR (false discovery rate) threshold, a peptide threshold of 95 %,
and a minimum of two unique peptides per protein. The total number of
peptide-to-spectrum matches (PSMs) is given for the total of each strain in
parentheses. A threshold of three spectral counts in at least one of the
treatments was selected for inclusion in the comparative analysis.
Strain
Treatment
Proteins identified
(Fe' pM)
(PSMs)
1871
2
204
41
214
120
234
740
226
1200
251
3900
258
Total
536 (28 887)
1374
2
581
41
613
120
600
740
654
1200
623
3900
527
Total
1085 (72 087)
Examination of iron stress response proteins in P. antarctica strain 1871 (a, b, c)
and 1374 (d, e, f). Relative protein abundance is shown as normalized
spectral counts; spectral counts have been normalized across
experiment treatments for each strain, but not to the maximum of each
protein as used in prior figures to allow for comparison of abundance for
similar isoforms. Error bars indicate the standard deviation of technical
triplicate analyses.
Identification and characterization of proteins and transcripts induced by
iron scarcity are valuable in improving an understanding of the adaptive
biochemical function of these complex phytoplankton as well as for their
potential utility for development as environmental stress biomarkers
(Roche et al., 1996; Saito et al., 2014). The enzymes flavodoxin and
plastocyanin, which require no metal and copper, respectively, and that
functionally replace iron metalloenzymes counterparts ferredoxin and
cytochrome c6, had isoforms that increased in concentration in the lower
iron treatments consistent with cellular iron-sparing strategies (Figs. 6,
S2) (Peers and Price, 2006; Whitney et al., 2011;
Zurbriggen et al., 2008). In strain 1374, however, there was an increase in
both of these iron-sparing systems at the highest iron concentration (Fig. 6d and f, Supplement Table S1).
While during both experiments cells were
growing exponentially at the time of harvest, those of strain 1374 were as
much as 7.6-fold denser in cell number than those of strain 1874 (based on
cell counts from treatments specifically used for transcriptome analyses),
and as a result the denser 1374 strain might have also experienced iron
stress even at this highest iron concentration as the high biomass depleted
iron within the medium. Of these two iron-sparing enzymes, plastocyanin
appeared to show a clearer increase in abundance at lower environmental iron
concentrations (Fig. 6c and f). In contrast, some flavodoxin isoforms could
be interpreted as being constitutive; two of the three isoforms were still
present in reasonable spectral counts at higher iron concentrations (Fig. 6a and d). Prior measurements during a Ross Sea colonial
P. antarctica spring bloom in
1998 were consistent with this interpretation, with ferredoxin
concentrations below detection and flavodoxin present (Maucher and
DiTullio, 2003). A constitutive flavodoxin could help explain P. antarctica's ability to
tolerate all but the lowest iron treatment observed in the physiological
experiments (Fig. 3a and b) and implies that the careful selection of
isoforms, or better, the inclusion of all isoforms of a protein biomarker of
interest may be valuable in interpreting complex field results.
There were also numerous isoforms of the iron-starvation-induced protein
(ISIP) group identified within the proteome of each P. antarctica strain:
specifically, nine ISIP2As and three ISIP3s in strain 1871 and three ISIP2As and four ISIP3s in strain
1374 (Fig. S1; Table S1). These ISIPs were
identified based on their transcriptome response to iron stress in diatoms
and most recently have been implicated in a diatom cell surface
iron-concentrating mechanism (Allen et al., 2008; Morrissey et al.,
2015). Interestingly in this P. antarctica experiment, these ISIPs exhibited both
“high” and “low” iron responses, but specific isoforms were more
abundant only under one of those respective conditions (Fig. 6). Given the
metamorphosis of P. antarctica between flagellate and colonial cell types observed by
microscopy and the proteome across the gradient in iron concentrations, we
hypothesize that this diversity of iron stress responses in the ISIPs may reflect the complexity associated with P. antarctica's life cycle. As the
abundant winter iron and sloughed basal sea ice reserves are depleted, newly
formed colonial cells will inevitably find themselves in the iron-depleted
environments that have been characterized in the Ross Sea almost immediately
upon bloom formation due to iron's small dissolved inventory (Bertrand
et al., 2011, 2015; Sedwick et al., 2011). As a result, P. antarctica may have distinct iron
stress protein isoforms associated specifically with the colonial cell type
(such as the high iron and colonial ISIPs; Figs. 5 and 6) in order to
acquire scarce iron during blooms, in addition to a distinct suite of iron
stress proteins produced within the flagellate cells (low iron and flagellate
ISIPs; Figs. 5 and 6). Given the rapid depletion of iron during
Ross Sea blooms, it is also conceivable that these iron-acquisition proteins
are constitutively expressed within the colony morphotype rather than being
connected to an iron-sensing and regulatory response system. Future
short-term iron perturbation studies that would complement the steady-state
experiments presented here could further investigate this hypothesis. The
multiplicity of ISIPs produced within each strain is also consistent
with the observation that both P. antarctica strains maintained high growth rates, even at
the lower 41 and 120 pM Fe' concentrations, compared to the diatom
Chaetoceros sp. whose growth rate is less than 50 % of maximal growth in similar media
(Fig. 3).
Scatterplots of relative transcript abundance (y axis) and
relative protein abundance (x axis) for P. antarctica strain 1871 (a) and strain
1374 (b)
for a high iron treatment (3900 pM Fe') relative to a low iron treatment (41 pM Fe'). Gray circles represent instances in which transcript abundance was not
significantly different between conditions (P≥0.99). Quadrants in
which
relative protein and transcript abundances agree (upper right, lower left)
and disagree (upper left, lower right) are noted, as are select genes
exhibiting the greatest relative protein abundance and/or transcript
abundance under a given treatment.
Correspondence between RNA and protein biomolecules
Many of the RNA transcripts of iron-related genes trended with their
corresponding proteins: 60 % of the iron-related gene transcripts
reflected the proteomic response in strain 1871, whereas there was a 30 %
correspondence between iron-related transcripts and proteins in strain 1374
(Fig. S1). In total, 47 % of expressed proteins in strain
1871 and 26 % of proteins in strain 1374 shared expression patterns with
associated transcripts (Fig. 7), consistent with recent studies of
proteome–transcriptome comparisons that showed limited coordination between
inventories of each type of biomolecule (Alexander et al., 2012). As mentioned
above, while both experiments were in exponential growth at the time of
harvest, strain 1374 was 7.6-fold denser in cell number than those of strain
1874 at that time. Hence, this decrease in transcript–proteome coherence in
strain 1374 may be related to harvesting in the late-log growth phase and
reflects the challenge of trying to conduct comparisons of these
biomolecules that function on different cellular timescales.
Examination of the transcriptome revealed a significant increase in
transcripts for tonB-like transporters, which can be associated with
cross-membrane nutrient transport (e.g., for iron siderophore complexes or
vitamin B12; Bertrand et al., 2007, 2013; Morris et
al., 2010) under high iron for strain 1871 and significantly greater
transcript abundances for a putative flavodoxin for strain 1374 under low
iron consistent with its substitution for ferredoxin due to iron scarcity
(Roche et al., 1996).
Observation of an iron-induced switch from single cells to
colonies
The strong connection of iron availability to putative structural components
of P. antarctica observed here served as an ideal opportunity to examine the genes and
proteins involved in morphological and life cycle transitions and colony
construction in this phytoplankter that can otherwise be experimentally
difficult to trigger in isolation. Phaeocystis colonies have captured the interest of
scientists for more than a century (Hamm et al., 1999), yet next to nothing
is known about the molecular basis of their construction. Colonies have been
considered a collection of loosely connected cells embedded within a gel
matrix and hence described as “balls of jelly” or “bags of water”
(Hamm et al., 1999; Lagerheim, 1896; Verity et al., 2007). Results
here suggest significant transformations in the cellular proteome that
corresponded to solitary and colonial morphological stages, for example,
involving structural proteins and proteins known to be post-translationally
modified such as glycoproteins or those containing glycoprotein-binding
motifs. To our knowledge, such an extensive proteome remodeling has yet to
be observed for another colonial organism or with the influence of any
other environmental stimuli in the genus Phaeocystis. As a result the details of this
response, while fascinating, are challenging to interpret due to their
novelty.
A putative spondin protein exhibited one of the largest responses between
low and high iron in both strains with a greater than 20-fold increase in
relative protein abundance and normalized 11-fold change in transcript
abundance in strain 1871 and a greater than 9-fold increase in relative
protein abundance and 3-fold change in transcript abundance in strain 1374
(Fig. 5a and Supplement Data 1). Spondin proteins are known to be
glycosylated and to be a component of the extracellular matrix (ECM)
environment, which may enable multicellularity in metazoans through cell
adhesion, and have been found to help coordinate nerve cell development
through adhesion and repulsion (Michel et al., 2010; Tzarfati-Majar
et al., 2011). Despite this large variation in protein abundance, the
function of spondins in eukaryotic phytoplankton, including Phaeocystis, remains largely
unknown. Given their responsiveness to iron availability and the associated
enrichment in colony rich cultures, these proteins could potentially
contribute to ECM-related adhesion of cells, to each other or the colony
skin, or even perhaps to the mucilage interior.
Additional glycoproteins that exhibited a strong iron response in both
strains include those containing von Willebrand factor domains (for example,
protein families PF13519, PF00092) and fibrillin and lectin (Figs. 5 and S1). In biomineralizing organisms, such as corals,
glycoproteins with von Willebrand domains are hypothesized to play a role in
the formation of the extracellular organic matrix through adhesion
(Drake et al., 2013; Hayward et al., 2011) by laying the scaffolding
for calcification. Orthologs of the von Willebrand proteins that contain
these domains have also been characterized in humans and have
protein-binding capabilities, which are important for coagulation
(Ewenstein, 1997). These dynamic von Willebrand proteins appear
to contribute to the cell aggregation and colony formation of P. antarctica colonies.
The suite of structural and modified proteins described above demonstrates a
means through which P. antarctica's colonial morphotype could be constructed, and this
dataset provides rare molecular evidence for the proteome reconstruction
needed to switch between single organisms to a multicellular colony. The
evolution of multicellularity in eukaryotes is an area of significant
interest that has mostly focused on model organisms with colonial forms such
as choanoflagellates and Volvox (Abedin and King, 2010). Genomic studies of the
former identified the presence of protein families involved in cell
interactions within metazoans, including C-type lectins, cadherins, and
fibrinogen (King et al., 2003). In other lineages of microalgae that
form colonial structures, such as Volvox carteri, there is supporting evidence for
glycoproteins cross-linking within the extracellular matrix of colonies
(Hallmann, 2003), as well as serving other important functional roles
in cell–cell attachment during colony formation (for example, colony
formation in the cyanobacteria Microcystis aerginosa) and as an integral component of cell walls
(for example, the diatoms Navicula pelliculosa and Craspedostauros australis) (Chiovitti et al., 2003;
Kröger et al., 1994; Zilliges et al., 2008). In this study,
environmental isolates of P. antarctica displayed consistent trends in similar protein
families (for example, lectins, fibrillins, and glycoproteins), and they
were produced at higher levels under elevated iron conditions when strain
1871 was primarily in colonial form. Given P. antarctica's environmental importance and
ability to control the transition between flagellates and colony cell
types through iron availability, P. antarctica may serve as a useful model for studying
multicellularity in nature and in the context of environmental change.
In contrast to these putative colonial structural proteins, there were
canonical cytoskeletal proteins such as actin and tubulin observed in P. antarctica cultures
grown under low iron conditions (Fig. S1). These proteins were
likely associated with the flagella and the haptonema, a shorter organelle
containing nine microtubules that is characteristic of haptophytes
(Zingone et al., 1999), found in the solitary Phaeocystis cell, and
similar to other eukaryotic flagellar systems such as Chlamydomonas (Watanabe et al.,
2004). Additionally, a suite of proteins with calcium-binding domains
(EF-hand protein families) was identified in greater relative abundance
under low iron growth conditions in both strains (Figs. 5, S1 and Supplement Data 1). In diatoms, calcium-signaling mechanisms have
been directly linked with how cells respond to bioavailable iron, as well as
stress responses (Allen et al., 2008; Vardi, 2008). Calcium (and
magnesium) ions also play an integral role in the ability for extracellular
mucus to gel (van Boekel, 1992). The greater abundance of putative
calcium-binding proteins under low iron conditions suggests an important
role for intracellular calcium, either in its involvement in flagellate
motility and/or having a role in inhibiting cell abilities to form
colonies while under iron limitation. This use of calcium signaling is
notable given that calcium is a major constituent of seawater (0.01 mol L-1), implying a need for efflux and exclusion of calcium from the
cytoplasm.
Phaeocystis antarctica strain-specific responses
Phaeocystis antarctica is believed to have speciated from warm-water ancestors, and populations
within the Antarctic are mixed via the rapid Antarctic Circumpolar Current
(ACC, 1–2 years) circulation with the Ross Sea and Weddell Sea, which
entrains strains nearly simultaneously (Lange et al., 2002). Moreover,
high genetic diversity has been observed across a large number of P. antarctica isolates
and even within isolates co-isolated from a bloom (Gäbler-Schwarz et
al., 2015). Given the differences in geographic location of the isolates
used in this study, there may be some differences regarding adaptation and
ecological role between them. In the Ross Sea, P. antarctica dominates, and cells exhibit
seasonal variability between flagellated states (early Spring, late summer)
and colonial stage (late Spring–early summer) (Smith et al., 2003). In
contrast, in the western Antarctic Peninsula, near the Weddell Sea from which
strain 1871 was isolated (Palmer station), P. antarctica is outnumbered by diatoms
and cryptomonads in terms of algal biomass, and colonies are generally rare
(Ducklow et al., 2007). While global proteomic and transcriptomic
analyses revealed differences among strains (Supplement Data 1), both
strains had responses that overwhelmingly supported a concerted effort
towards structural changes under high iron versus low iron, consistent with
the minor phylogenetic differences previously reported for P. antarctica isolates due to
rapid ACC circulation (Lange et al., 2002).
Location of the metaproteome sample and pigment data from a Ross
Sea Phaeocystis bloom net tow sample. (a) Station map of NBP06-01 (27 December 2005 to
23 January 2006); the metaproteome sample was taken on 30 December
by net tow location (red circle). (b) 19'-hexanoyloxyfucoxanthin
(“19'-Hex”) pigment is associated with Phaeocystis, while (c) peridinin and
(d) fucoxanthin pigments are typically associated with dinoflagellates and
diatoms, respectively (although dinoflagellates living heterotrophically can
be lacking in pigment). Comparisons of the spring and summer expeditions
(NBP06-08 and NBP06-01, respectively) revealed a shift from being dominated
by P. antarctica to being a mixture of P. antarctica and diatoms. See Smith et al. (2013) for further
details.
Examination of a Phaeocystis bloom metaproteome from the Ross
Sea
The detailed laboratory studies above can be compared to a first
metaproteomic analysis of a Ross Sea Phaeocystis antarctica bloom to provide an examination of the
in situ ecology and biogeochemical and their underlying biochemical signatures. Due
to the newness of metaproteomic eukaryotic phytoplankton research, some
methodological detail has been incorporated into this section. For field
analysis a net tow sample was collected north of Ross Island (Fig. 8) on
30 December 2005, in which Phaeocystis colonies were visually dominant. Temporal
changes in the bloom composition have been described for this summer
expedition and an austral spring expedition later that year (NBP06-01 and
NBP06-08, respectively), and a shift was observed from a P. antarctica-dominated ecosystem
to a mixture of P. antarctica and diatoms (Smith et al., 2013). Surface pigment
distributions showed the sampling region to be within a particularly intense
bloom dominated by Phaeocystis as observed by abundant 19'-hexanoyloxyfucoxanthin
pigment (Fig. 8), reaching concentrations of 1096 ng L-1 and total
chlorophyll a concentrations of 1860 ng L-1 on the sampling day.
CHEMTAX analysis of these HPLC pigments found that P. antarctica populations accounted
for approximately 88 % of surface water total chlorophyll at this time.
Fucoxanthin pigment, characteristic of diatoms, was lower here (136 ng L-1) compared to samples from the western Ross Sea (Fig. 8), consistent
with prior Ross Sea observations. Repeated sampling near the sampling region
(∼77.5∘ S) 2 weeks after taking the metaproteome sample
found lower overall chlorophyll a levels (Smith et al., 2013) consistent
with bloom decay. Iron measured very near this location (76.82∘ S,
170.76∘ E also on 30 December 2005) revealed a surface dissolved iron
concentration of 170 pM (6 m depth) and an acid-labile particulate iron
concentration of 1590 pM (Sedwick et al., 2011), consistent with iron
depletion in seawater following drawdown of the accumulated winter iron
supply and incorporation of iron into biological particulate material
(Noble et al., 2013; Sedwick et al., 2000).
The metaproteome analyses of the Ross Sea sample were conducted by bottom-up
mass spectrometry analysis of tryptic peptides using initially a
one-dimensional
and subsequently a deeper two-dimensional chromatographic methodology (1-D and 2-D
from here on), followed by peptide-to-spectrum matching of putative peptide masses
and their fragment ions to predicted peptides from translated DNA sequences.
While this approach is common for model organisms and has been successfully
applied to primarily prokaryotic components of natural communities
(Morris et al., 2010; Ram et al., 2005; Sowell et
al., 2008; Williams et al., 2012), there continue to be challenges in
metaproteomic analyses of diverse communities, particularly when including
an extensive eukaryotic component such as is present in the Ross Sea
phytoplankton bloom. VerBerkmoes et al. (2005) demonstrated the feasibility
of using mass spectrometry metaproteomic analysis for the detection of
eukaryotic proteins in a complex sample matrix. To address these issues, we
utilized three sequence databases for peptide-to-spectrum matching (see
Methods and Table S2). Analysis of both unique
(tryptic) peptides and identified proteins are provided here, and unique
peptides are particularly valuable in metaproteome interpretation as a basal
unit of protein diversity that can be definitively compared across the three
sequence databases (Saito et al., 2015).
Comparison of the total number of proteins, peptides, and spectra
measured in the Ross Sea metaproteome net tow sample using three databases
for peptide-to-spectrum matching (see Table S2). Results from two-dimensional
and one-dimensional (1-D in parentheses) analyses are shown.
Peptide-to-spectrum match (PSM) database
Total
Total
Total
Decoy FDRa
proteins
unique
spectra
percent
peptides
matched
(peptide level)
(1) Phaeocystis strains transcriptomesb
1545 (912)
3816 (2103)
14 088 (8226)
0.6 (0.17)
(2) Ross Sea metatranscriptomec
1474 (859)
3210 (1520)
10 154 (4725)
0.1 (0.7)
(3) Antarctic bacterial metagenomesd
102 (92)
237 (186)
530 (440)
3.6 (2.3)
a FDR refers to the false discovery rate of a reversed peptide
database.
b Metaproteome annotated using the laboratory-generated transcriptomes for
strain 1871 and strain 1374 (Database 1).
c Metaproteome annotated using the metatranscriptome generated from sample
split of original Ross Sea sample (Database 2).
d Bacterial metaproteome annotated using bacterial metagenomes from
Delmont et al. (2014) (Database 3).
(a) Venn diagram of the attribution of the 5885 total unique
peptides identified in the metaproteome sample to three DNA–RNA sequence
databases (Table S2). (b) Taxon group composition of genes
identified by metatranscriptome analyses (combining total RNA and polyA RNA
fractions). (c) Taxon group composition of proteins identified by the
bacterial metagenomic database (Database 3). (d) Taxon group composition
of proteins identified by the metatranscriptome database (Database 2).
The combined P. antarctica strain transcriptome database (Database 1) generated the
largest number of protein and unique peptide identifications: 1545 and 3816
in 2-D (912 and 2103 in 1-D) (Table 2, Fig. 9a). This strong
relative performance of the strain database was surprising and likely
reflects the depth of the P. antarctica isolate transcriptomes and resultant translation
into greater metaproteomic depth. Approximately 60 % of field
identifications mapped to strain 1374 (57 %); a broad synthesis of all
proteomes based on KOG annotations also indicated that the metaproteomes
appeared most similar to the Ross Sea strain 1374 (Fig. S3).
The Ross Sea metatranscriptome database (Database 2) resulted in 1475
proteins and 3210 unique peptides in 2-D analyses (859 proteins and 1520
unique peptides in 1-D) distributed across a large number of taxa, with 324
of those proteins associated with P. antarctica. The Antarctic bacterial metagenome
database (Database 3) produced 102 proteins and 237 unique peptides in
2-D (98 proteins and 186 peptides in 1-D) that mapped to bacteria likely
associated with the phytoplankton communities, given the use of a net that
would not otherwise capture free-living bacteria. The low number of
bacterial protein and peptide identifications could reflect their small
abundance or limited metagenomic coverage. Due to the extensive diversity
present, there was overlap between the peptide identifications from each
database for the 5885 total unique peptides in 2-D (3193 in 1-D); 1222 (in 2-D;
544 in 1-D) P. antarctica peptides were shared between the Phaeocystis strain and Ross Sea
metatranscriptome databases; 158 (in 2-D; 69 in 1-D) bacterial peptides were
in common between the Ross Sea metatranscriptome and the bacterial
metagenomic databases, followed by very small numbers shared between
bacterial metagenome and the Phaeocystis strains database searches (eight peptides in both
1-D and 2-D) and all three databases (seven and four peptides in 1-D and 2-D,
respectively), likely due to a small fraction of tryptic peptides shared
between diverse organisms (Saito et al., 2015).
This multi-database approach and the relatively low overlap illustrates the
necessity of employing diverse sequence databases that target distinct
components of the biological community, as well as the value in coupling
metatranscriptomic and metagenomic sequence databases to metaproteomic
functional analysis to capture the extent of natural diversity. This is
evident in the taxon group analysis, in which the metatranscriptome has a large
representation of Dinophyta and diatoms and only a small contribution from
Haptophyta that include Phaeocystis, likely due to the large genome sizes and
transcription rates, particularly of dinoflagellates, and perhaps due to
interferences of Phaeocystis RNA extraction due to the copious mucilage present (Fig. 9b).
In contrast, the metaproteome derived from the metatranscriptome database
is dominated by Haptophyta and Dinophyta, with minor contributions from
other groups (Fig. 9d), reflecting the dominant organismal composition seen
in the pigment analyses (Fig. 8). Due to a coarse net mesh size much larger
than a typical bacterial cell, the bacterial community captured by these
metatranscriptome and metaproteome analyses most likely reflects the
microbiome associated with larger phytoplankton and protists, particularly
within the abundant P. antarctica colonies. Databases 2 and 3 result in 211 and
102 bacterial protein identifications (in 2-D; 148 and 100 in 1-D), including representatives from Oceanospirillaceae, Rhodobacteraceae, Cryomorphaceae, Flavobacteria,
and Gammaproteobacteria (Fig. 9c and d). The
lower number of bacterial identifications could be due to low bacterial
biomass in the net tow sample relative to phytoplankton biomass and/or
limited metagenomics coverage.
This Ross Sea bloom metaproteome–metatranscriptome analysis
provides a window into the complex interactions of this community with its
chemical environment. Phaeocystis antarctica proteins were abundant in the sample with over 450
(in 2-D; 300 in 1-D) proteins identified, yet interestingly, we identified
proteins associated with both high and low iron treatments, including those
corresponding to flagellate and colonial life stages identified in the
culture experiments (Figs. 10 and S1). This presence of
both life cycle stages of Phaeocystis could be interpreted as evidence of an actively
growing bloom, with growing flagellate cells coalescing to form new
colonies, as well as a standing stock of colonial cells. As mentioned
earlier, division and growth of P. antarctica colonies is believed to require
transitioning back through the flagellate life cycle stage (Rosseau et al.,
1994), and hence a mixed population of flagellate and colonial stages would be
expected of a growing population, consistent with our laboratory
observations (Fig. 3c).
The presence of well-known iron-sparing proteins such as plastocyanin (Fig. 10) was consistent with the depleted dissolved iron concentration (170 pM)
in nearby surface waters that are closest to the 120 pM Fe' of the low iron
treatments (Peers and Price, 2006; Sedwick et al., 2011), as well as
incubation experiments on the same expedition initiated 3 days prior
that demonstrated iron limitation of P. antarctica (and iron–B12 colimitation of
diatom) populations (Bertrand et al., 2007). Notably, the actual Fe' of
the Ross Sea was likely considerably lower than this due to the presence of
strong organic iron complexes (Boye et al., 2001). Strzepek et al. found
evidence for growth of P. antarctica and some polar diatoms on strong organic iron
complexes at somewhat reduced growth rates in their culture experiments,
implying a high-affinity iron acquisition system such as a ferric reductase,
although the molecular components of such a system have yet to be identified
in P. antarctica (Strzepek et al., 2011). As described above, it is likely that both
flagellate and colonial cell types have a need to manifest iron stress
responses (e.g., distinct ISIPs found in the flagellate- and colonial-dominated cultures; Figs. 5 and 6) and that those distinct responses may be
based on the extensive physical differences between life cycle phenotypes.
The low contribution of chain-forming diatoms to this metaproteome sample
was consistent with the higher sensitivity of some Ross Sea diatom strains
to iron stress such as Chaetoceros (Fig. 3d) and the low iron availability. Careful
examination of targeted mass spectrometry results (precursor and fragment
ion analysis) for select iron proteins identified in culture studies showed
consistently high-quality chromatograms within the field sample,
demonstrating a capability to measure these potential peptide biomarkers
within complex environmental samples in future field studies characterizing
bloom and biogeochemical dynamics (Figs. 11 and S4–10).
Putative biomarkers identified in the Phaeocystis metaproteome annotated
using the field metatranscriptome (error bars represent SD of replicate
samples; n=2; 1-D dataset used). Green bars indicate putative “low iron”
biomarkers; red bars indicate putative “high iron” biomarkers and
correspond to the life cycle stages observed (Fig. 3).
Example spectra and chromatograms of fragment ions for two peptides
corresponding to a P. antarctica flavodoxin identified from the Ross Sea metaproteome
sample (peptide sequences found within Database 1, 1871,
contig_31444_1_606_+, 1374, contig_202625_47_661_+; Database 2,
contig_175060_39_653_+). Peptide fragmentation spectra are shown in (a) and
(c) and example chromatograms of MS1 intensities as well as with +1 and
+2 mass addition for isotopic distributions are shown (b) and (d),
demonstrating the utility of these iron stress biomarkers in field samples.
The metaproteome analyses also captured relevant functional elements of the
bacterial microbiome associated with the eukaryotic community based on the
bacterial proteins identified in both the bacterial databases and the Ross
Sea metatranscriptome (Fig. 9c and d). For example, the SAR92 clade of
proteorhodopsin-containing heterotrophic bacteria was present (Stingl
et al., 2007) and expressed both the iron storage protein bacterioferritin
and TonB receptors, the latter of which are involved in siderophore and
B12 transport. In addition, the Fur iron regulon, iron-requiring
ribonucleotide reductase, as well as the vitamin-related CobN cobalamin
biosynthesis protein, B12-requiring methyl-malonyl CoA, and thiamine
ABC transporter were observed from several heterotrophic bacteria species
including Oceanospirillaceae, Rhodobacteraceae, and Cryomorphaceae (Supplement Data 2) (Bertrand et al., 2015;
Murray and Grzymski, 2007). These results imply that heterotrophic bacteria
known to be associated with the Phaeocystis colonies, such as SAR92 and
Oceanospirillaceae, were also likely responding to micronutrients by concentrating and storing
iron and through the biosynthesis of B12. In doing so this bacterial
microbiome could have been harboring an “internal” source of
micronutrients, fostering a mutualism with Phaeocystis colonies in exchange for a
carbon source and consistent with the high particulate iron measured during
this station (Sedwick et al., 2011). This could create a
competitive advantage for P. antarctica relative to the iron and B12-stressed
diatoms for early season bloom formation, as previously hypothesized and
observed in the Ross Sea in enrichment studies (Bertrand et al., 2007).
Although diatoms were less prominent in the dataset, several diatom proteins
identified were indicative of the potential for iron stress (e.g.,
plastocyanin and ISIP3; Supplement Data 2); however, the diatom CBA1
cobalamin-acquisition protein was not identified in the metatranscriptome
and hence would not be detected in the metaproteome using the current
methods, but could be targeted in future studies from this dataset.