1. Introduction
Diatoms are one of the most important and diverse groups of phytoplankton widely distributed in oceanic waters. They form the basis of the marine food web of most aquatic ecosystems, being able to fix nearly 20% of global carbon on Earth [
1]. Each diatom cell, as a consequence of its biological activity, generates a region in its immediate environment, the phycosphere, characterized by being enriched in dissolved organic compounds released or exuded by it [
2]. This dissolved organic matter (DOM) can influence the growth and metabolism of other associated organisms in its direct environment [
3] but in particular, is host to diverse microbial assemblages that thrive in close proximity to the diatom, either attached or free-living [
4]. Interactions between diatoms and bacteria are some of the most important relationships in aquatic environments [
3,
4,
5]. These interactions are diverse and complex and can result in mutualistic, commensal, competitive, and antagonistic interactions that can lead to the demise or success of interacting species [
5,
6,
7]. Although the precise characteristics of these interactions may exhibit variability, chemical compounds emerge as the main drivers of these dynamics [
8,
9,
10].
Polyunsaturated aldehydes (PUA) are organic molecules that make up the phycosphere of various diatoms [
11,
12]. PUA are long-chain volatile oxylipins, derived from lypoxidation of intracellular polyunsaturated fatty acids (PUFA) [
13]. Some authors have demonstrated that, in nature, at the end of a diatom bloom period, PUA are released concomitantly to nutrient limitation [
14,
15] and nowadays, several authors defend their function as infochemicals regulating diatoms bloom mediated by the bottom-up control of herbivore populations [
11,
16,
17,
18,
19]. In the open ocean, it has been described a meaningful macroecological relationship with resource availability (unbalanced N:P ratio) shows a higher PUA production capacity in the phytoplankton of the poorest waters and among the small species typically populating these environments [
20,
21]. Experimentally unbalanced N:P ratios of low P or low N also triggered higher PUA production in diatoms as in [
22]; they experimentally demonstrated that low silicon, low P, and low N enhanced PUA production in a coastal diatom. The possibility that PUA may play a role as a mediator of diatom–bacteria interactions in the phycosphere has been less studied and published results are somewhat more equivocal [
22,
23]. Some authors showed differential effects of PUA on the growth and metabolism of natural free-living bacterial communities [
22,
23], and Edwards et al. suggested that PUA altered the community structure of particle-associated bacteria suggesting a role in bacterial community succession [
24]. Very recently, Eastabrook et al. have suggested that PUA is not the main driver of diatom–bacteria interactions in laboratory cultures [
25]. Recent publications also consider the significant role of attached bacteria in phosphorus recycling in nature [
26,
27].
In this work, we want to further analyze PUA as a mediator in diatom–bacteria interactions, by studying the effect that the presence and conformation of microbial consortia might have on PUA production by the diatom. To explore this possible diatom–bacteria interaction, we shifted the microbial consortium present in cultures of C. cryptica, a newly isolated PUA producer coastal diatom. We studied the effect of the presence and absence of microbes by comparing the type and amount of PUA produced by the diatom host C. cryptica co-cultured with different microbial assemblages and compared with axenic conditions. This study was performed under experimental conditions of N:P imbalance that favored PUA-enriched phycospheres as has been demonstrated in the literature.
3. Discussion
As the first significant result in this study, we have observed that bacteria enhanced the growth rate of
C. cryptica (
Table 1). Axenic cultures of
C. cryptica (A-cultures) showed significantly lower exponential growth rates (0.35–0.42 day
−1) compared with N-cultures (0.53–0.62 day
−1) and I-cultures (0.73–0.82 day
−1) at both nutrients conditions (
Table 1). Furthermore, a significant increase in growth rates of
C. cryptica was observed when non-native bacteria were inoculated (I-cultures) reaching the highest growth rates (0.82 ± 0.21 day
−1). These results evidenced that re-inoculation of the microbiota community associated with a different diatom (particularly, microbiota associated with
P. tricornutum) had an ultimate benefit on
C. cryptica growth.
The observed beneficial effects of bacteria on diatom growth are not new. Since Provasoli first suggested that bacteria could enhance algal growth [
30], many studies have corroborated this hypothesis and it is now well known that diatoms develop specific interactions with certain bacteria that conform microbial consortia, both in culture and in nature (reviewed in [
31]). Sometimes these interactions are almost forced, and bacterial activity satisfies a particular diatom requirement (e.g., vitamins or iron) [
32,
33]. Further indirect support for this view comes from the frequent observation that prolonged culturing of diatoms in the absence of bacteria negatively influences their physiology and growth [
34,
35]. What is interesting about our results is that, along with this beneficial effect of bacteria on
C. cryptica growth rates, we have also observed a significant effect of bacteria on diatom pPUA (
Table 2 and
Table 3;
Table S3 Supplementary Material) and dPUA profiles (
Table 4 and
Table 5;
Table S6 Supplementary Material), denoting a striking role of PUA in diatom–bacteria interaction.
In N-cultures, the higher pPUA concentration observed at low P cultures (higher N:P imbalance) (
Table 2) is consistent with the literature [
14,
21,
36]. We can establish the pPUA profile obtained in N-cultures, with HD (>50%), OD (>15%), and DT (>9%) (
Figure 2) as a representative pPUA profile of
C. cryptica adapted to our experimental environmental conditions. These PUA have been well documented as the dominant bioactive PUA released by diatoms in the past [
11,
12,
13,
36,
37,
38,
39,
40]. Also, this PUA profile is consistent with the cellular PUFA content observed in stock cultures of this study (
Figure 6) as main PUA precursors: eicosapentaenoic acid (EPA, C 20:5) as HD and DT precursor, C16:3 and C16:4 as OD and OT precursors and C 20:4 as DD precursor [
13,
41,
42]. Cells from N-cultures, showed the highest pPUA concentrations, compared with I-cultures and A-cultures (
Table 2), especially at low P. However, the estimated percentage of PUA released (%dPUA in
Figure 5) for N-cultures was very low (<3%) (
Figure 5) at both nutrient conditions when compared with A-cultures (up to 30% at F2) and I-cultures (up to 45% at low P). Furthermore, for N-cultures, dOT was also a representative PUA (≥20%) at both nutrient conditions (
Figure 3), which was not proportionally abundant in the pPUA of the cells (
Figure 2). This can be interpreted in two ways. Firstly, diatom cells at N-cultures have all the metabolic requirements to synthesize PUA (N-cultures in
Table 2; PUFA in
Figure 6) but they do not release all potential PUA to the media, resulting in a low % dPUA (
Figure 5). Secondly, cells at N-cultures released a higher concentration of dPUA to the media but the released PUA is used as a source of carbon by their associated autochthonous bacteria, and then, we underestimated dPUA concentration and diversity in the media. We found that dPUA was positive and significantly correlated with bacterial abundance at F2 conditions (
Figure 4A) but not at low P conditions (
Figure 4B). In addition, we found a differential effect of bacteria on the type of dPUA, as indicative that bacteria could use dPUA as a carbon source in a selective manner. It seems that introduced bacteria (I-cultures) use all dOD, dDD, and dOT (
Figure 3) to significantly increase their growth (
Table 1). As a result, this would alter the proportion of PUA types in the dissolved fraction compared to pPUA as observed by comparing
Figure 2 and
Figure 3, and furthermore, this is consistent with the growth observed for bacteria (
Table 1). PUA would help to maintain a stable diatom–bacteria association by conforming the community structure, in fact, some PUAs are positively correlated with total and alive bacteria concentration and others with dead bacteria (
Figure 4C). This is consistent with previous works that found a differential effect of PUA on marine bacteria [
22,
23]. Several authors have shown that diatoms conserve associated bacterial communities across time [
43], therefore, associations are not randomly assembled but follow a dynamic that can be reproduced [
44,
45].
Starting from the premise of considering N-cultures as representative of this diatom in this particular culture conditions, if we compare with A-cultures, the relative % of PUA released increased greatly in axenic conditions (%dPUA in
Figure 5), especially at F2, where precisely lower concentrations of pPUA were quantified in the cells (
Table 2), and growth rates were the lowest (
Table 1). That is, axenic cells with lower pPUA (A-cultures in
Table 2) released the highest percentage of dPUA at both nutrient conditions (
Figure 5). Ultimately, this resulted in the lowest observed diatom growth rates (A-cultures in
Table 1). In these cultures, any effect of bacterial presence on the quantified dPUA can be ruled out since cultures were axenic. Interestingly, when non-native bacteria (from a non-PUA producer diatom) were added to the axenic cultures (I-cultures), the dPUA released reached the highest value under low P (45% in
Figure 5), resulting in this case, in a better diatom growth rate (
Table 1) under both nutrient conditions. Even taking into account a possible bacterial use of dPUA as a source of carbon, the %dPUA was the highest observed. It is important to note that
C. cryptica cells of I-cultures come from A-cultures re-inoculated with non-native bacteria, and their pPUA should be similar, as is corroborated in
Table 2.
PUA released at low P ultimately produced a benefit in diatom growth, and, in the case of introduced bacteria (I-cultures) also bacteria grew optimally (
Table 1). Thus, we can infer an apparent mutual benefit in the diatom–bacteria growth in which PUA participates in some way. We did not analyze the diversity of associated microbiota for each experiment. Released PUA could lead to changes in the diversity of the bacterial community for N-cultures and I-cultures, as it has been reported by several authors for coastal bacteria [
23], or not, as has been documented by Eastabrook et al. [
25], for laboratory diatom cultures, but this does not detract from the fact that we found a mutual benefit for growth. We hypothesize that, albeit at nM levels, the PUA released (type and quantity) could contribute to defining a specific organic matter signature of this diatom at each assayed condition. These molecules together with other organic substances would help to conform to a particular phycosphere during bacterial colonization of the cell vicinity. This could help to design a specific bacterial niche that would confer an advantage for diatom growth, as we have observed in the presented results. The associated bacterial community would be relatively stable over time mainly in experimental cultures maintained by successive reculturing. In the case of A-cultures, where diatom cells have the lower pPUA, they released the most PUA (highest %dPUA in
Figure 5). This can be understood as a failed attempt to attract bacteria. In A-cultures, where there are no bacteria, PUA release is an unsuccessful strategy of diatom cells to attract non-existent bacteria, with a significant metabolic expenditure that eventually takes its toll, and
C. cryptica growth slows.
In this work, we have been focused on the analysis of diatom PUA, but also bacteria can release molecules that interact with diatoms and might influence the excretion of algal metabolites (including PUA). It has been demonstrated that hormones from bacteria can enhance the cell division of algal cells, its photosynthetic machinery, and potentially its carbon output to the bacteria [
31,
46]. Other bacteria manipulate algal growth by producing proteins that lyse algal cells or unknown factors that arrest algal cell division [
47,
48,
49,
50]. Then, we cannot conclude if any of the quantified PUA is upregulated in response to signals from co-cultured bacteria or if diatoms can take up metabolites released by bacteria. In fact, an uptake of any compound released from the bacteria could influence specific PUFA/PUA biosynthetic pathways of the algae [
3,
32]. This would need a new and particular experimental design focused on it.
Our results demonstrated that
C. cryptica modulated its PUA profile in response to the presence or absence of bacteria in the surrounding media, conferring a growth advantage. It is an emerging concept that marine microbial communities are part of tightly connected networks, providing evidence that these interactions are mediated through the production and exchange of infochemicals [
31]. These inter-kingdom interactions are complex, involving the exchange of cofactors, micronutrients, macronutrients, proteins, and signaling molecules, and PUA could be an active part of the variety of molecules involved.
4. Materials and Methods
4.1. Biological Material
Two diatom species were used in this study: the PUA producer
Cyclotella cryptica and the non-PUA producer
Phaeodactylum tricornutum (strain CCAP 1052/1A).
C. cryptica was freshly isolated from Atlantic coastal area at the Bay of Cádiz (Southwestern Spain). The species was identified by PCR using the protocol described by Zimmermann et al., at the University Institute of Marine Research (INMAR, Cádiz, Spain) [
51]. Before starting the experiments, we checked that PUA was not detected in monocultures of
P. tricornutum and its associated heterotrophic bacteria (introduced bacteria) by using monocultures of this diatom, following the protocols explained below (
Section 4.5). Therefore, it can be ensured that the PUA detected in all the experiments was exclusively produced by
C. cryptica.C. cryptica and
P. tricornutum stock cultures were maintained by successive inoculations at constant 20 °C and of 14:10 L:D cycle in sterile natural seawater enriched with f/2 medium [
52] and silicate. Additionally, an inoculum of the isolated strain of
C. cryptica was purified by Spanish Algae Bank (
https://marinebiotechnology.org/en/), by fluorescence-activated cell sorting (FACs) using a Sony SH800 flow cytometer (Tokyo, Japan). combined with serial dilutions. First, diatom cells were collected from a non-axenic culture of
C. cryptica using cell sorting option of the cytometer. Separated cells were inoculated one by one into a 96 multi-plate with sterile ASP12 medium. Once colonies were detected, cells were collected and transferred to Erlenmeyer flasks following [
53] protocol, at 20 °C, 50 μmol quanta m
−2 s
−1 in a 14:10 L:D cycle. Diatom populations with lower bacterial cell density were washed by successive centrifugation and dilutions until axenic conditions were observed. A stock culture of this axenic strain was also maintained, and the absence of bacteria was periodically verified by examining SYBR Green I nucleic acid gel-stained cultures by imaging flow cytometry (IFC) using a Luminex ImageStream
®X Mk II [
54] (Seattle, WA, USA).
4.2. Experimental Design
Axenic and non-axenic cultures of
C. cryptica were grown in 250 mL sterilized Erlenmeyer flasks under artificial light at 54 ± 6 μmolquanta m
−2 s
−1 of irradiance and 14:10 light:dark (L:D) cycle. They were maintained at 20 °C and orbital agitation at 90 r.p.m. under two nutrient conditions: The first one was obtained by culturing the axenic and non-axenic strains of
C. cryptica in sterilized 0.22 μm filtered natural seawater enriched with f/2 nutrient stock and silicate [
52], with a final phosphate concentration of 32.42 ± 0.57 µM (hereafter, F2). For the second one, 0.22 μm filtered natural seawater enriched with a lower final concentration of phosphate of 5.55 ± 0.16 µM (hereafter low P). Nitrate concentration was 963.45 ± 0.53 µM and silicate was 106 ± 1.01 µM for both treatments. These nutrient concentrations were quantified spectrophotometrically with a Skalar autoanalyzer following standard procedures of Strickland and Parsons [
55], from samples filtered through pre-combusted Whatman GF/F filters (200 °C; 4 h). Five replicates per treatment were followed.
To assess the potential qualitative and quantitative impact of microbial presence on PUA production, three sets of experiments were carried out: one using axenic cultures of C. cryptica (hereafter, A-culture, axenic), second, non-axenic cultures of C. cryptica co-cultured with the heterotrophic bacteria naturally associated (hereafter, N-culture, natural heterotrophic bacteria), and third, C. cryptica co-cultured with bacterial communities associated to the non-PUA producer diatom P. tricornutum (hereafter, I-culture, introduced heterotrophic bacteria). Introduced heterotrophic bacteria were obtained from a dense stock culture of P. tricornutum. This culture was filtered through sterilized polycarbonate filters (Whatman®-NucleoporeTM Track-Etch Membrane, Maidstone, UK) with 2.0 μm of pore size to remove algae while preserving the bacterial communities associated with P. tricornutum. Each experimental flask (n = 5) containing axenic C. cryptica cells, was inoculated with an introduced bacterial inoculum of 6 × 103 cells mL−1.
Daily samples were collected from each flask for quantification of diatom cell density under sterile UV ambient. Cultures were monitored from the inoculation day until late exponential growth phase. For the axenic experiments, samples for cell density quantification were only collected on the inoculation day and ending day to avoid any contamination. Once the cultures reached the late exponential growth phase, samples for particulate and dissolved PUA (hereafter, pPUA and dPUA) were collected. Also, samples for analyzing the PUFA profiles of the stock cultures of
C. cryptica under F2 and low P conditions were collected. For clarification, a detailed flow chart of this experimental design is provided in
Figure S5 (Supplementary Material).
4.3. Cell Density Quantification
Diatom and bacterial cell densities were quantified by image flow cytometry (IFC) using a Luminex ImageStream
®X Mk II (hereafter, ISX, Seattle, WA, USA). IFC enables multimode imaging of cells simultaneously in bright field, dark field (analogous to flow cytometry measured side scatter laser light, SSC), and a broad range of fluorescence wavelengths using a time delay integration charge-coupled device (CCD) camera that integrates images passing through the field, generating a high-resolution summed image associated to each particle [
56]. The ISX used was equipped with 60×/40×/20× magnifications and four excitation lasers (405 nm, 488 nm, 642 nm, and 785 nm). Samples were analyzed at 60× magnification and low flow rate, after excitation with 488 and 785 nm lasers using the INSPIRE software (2023.2.173.0) (Amnis Corp., Seattle, WA, USA). For each sample, a range between 10,000–20,000 particles was collected. Side scatter images (SSC) were obtained using 785 nm excitation and 745–800 nm emission and chlorophyll autofluorescence (642–745 nm emission) was detected with 488 nm excitation laser. In addition, a CCD camera collected images in the bright field channel (BF) associated with each particle suspended in the analyzed sample. Post-acquisition spectral compensation and data analysis were performed using the IDEAS 6.2 image analysis software package (Amnis Corp., Seattle, WA, USA).
Figure 7 shows representative dot plots obtained with associated images at 60x magnification to each dot. By combining dot plots of different channels of the ISX, suspended particles in the sample can be discerned, and images of each particle can be analyzed with IDEAS 6.2. image analysis software. The chlorophyll
a in autotrophic alive cells allows these cells to be discerned by autofluorescence of this pigment after blue light excitation. As shown in
Figure 7, alive
C. cryptica cells were easily localized in the obtained cytograms by chlorophyll autofluorescence and SSC (
Figure 7A), and the images (60× magnification) associated with each cell were collected (
Figure 8). For quantification of non-autofluorescent cells as heterotrophic bacteria, flow cytometric counting was possible through the staining of cell nucleic acids with fluorescent dyes prior to analysis. For this purpose, 1 mL samples were fixed with glutaraldehyde (0.1%) and paraformaldehyde (1%) and preserved at −80 °C until analysis. For total bacterial counting by IFC, samples were thawed and stained with SYBR Green I nucleic acid gel stain (hereafter, SYBR Green) (0.01%) (490–498 nm, S-9430; Sigma-Aldrich, Saint Louis, MO, USA; ×10 dilution in DMSO of commercial stock), in dark for 10 min [
54] before analysis by ISX. This fluorochrome allowed separation of bacterial cells from abiotic particles (e.g., detritus) (
Figure 7B).
In order to analyze the proportion of quiescent (dead) bacteria, an aliquot of 300 μL was collected and stained with SYTOX Green dead cell stain (0.001%) (488–530 nm, S-34860; Thermo Fisher Scientific, Waltham, MA, USA) immediately after sampling, rather than fixing. This stain does not penetrate live cells but only those with compromised plasma membranes. For this reason, it is used to determine cell viability [
57] and to estimate membrane integrity in bacteria [
58,
59]. The optimal concentration (10
−3 µM of the commercial solution) and time of incubation (10 min) of SYTOX Green were evaluated experimentally to adapt the protocol of Lebaron et al. [
58]. Quantification of alive bacteria was obtained by the difference between total bacteria (SYBR Green stained bacteria) and dead bacteria (SYTOX Green stained bacteria).
4.4. Microalgal and Bacterial Growth Rates
Microalgae and bacteria exponential growth rates were calculated as μ (day
−1) according to:
where N
0 and N
1 represent cell density at the start and the end of the exponential growth period, and t is the time between measurements (in days) [
14].
4.5. PUA Sampling, Extraction, and Quantification
For analysis of the pPUA fraction, 50 mL samples were collected from the different cultures and the algal biomass was concentrated by centrifugation (5 min at 1110 rcf; 3000 rpm Mixtasel-BLT, SELECTA). Then, 2 mL of derivatization reagent (25 mM solution of
O-(2,3,4,5,6-pentafluorobenzylhydroxylamine)hydrochloride; PFBHA, Fluka, Basel, Switzerland) in 100 mM Tris-HCl, pH 7, Trizma, Sigma, Steinheim, Germany) and 500 µL of 40 nM benzaldehyde (internal standard) were added to the resulting pellet. For mechanical disruption of the cells, the samples were sonicated by ultrasound (Bandelin Sonoplus, HD2070, 97%). The extraction was performed according to the protocol described in [
20].
For analysis of the dissolved fraction (dPUA), 100 mL of the different cultures were sampled and filtered at low pressure through 2 µm Whatman GF/F filters to remove algal biomass. Then 1 mL of PFBHA reagent in Tris-HCl and 500 µL of the internal standard were added. After one hour at room temperature for complete derivatization, each sample was eluted through a LiChrolut
®RP C-18 cartridge, previously washed with derivatization solution, by pumping an Eyela pump. The derivatized PUA was eluted from the cartridge with 4 mL of 10 mM PFBHA in methanol, collected in a glass vial, and incubated for at least one hour at ambient temperature to ensure complete derivatization of the aldehydes. The vials were then stored at −80 °C until extraction. For extraction, samples were transferred into a 100 mL glass separating funnel using an 8:4:8 ratio (hexane:methanol:water) following the protocol of [
60].
The obtained extracts for both, pPUA and dPUA, were analyzed by GC-MS using an Agilent 7890A GC gas chromatography instrument (Agilent Technologies Inc., Santa Clara, CA, USA) coupled to either a Synapt G2 Q-TOF high-resolution mass spectrometer (Milford, MA, USA) with an atmospheric pressure ionization source (atmospheric pressure gas chromatography, APGC) in positive mode, or to a triple quadrupole spectrometer with an electron impact ionization (EI) source in multiple reaction monitoring (MRM) mode. Chromatographic separation of PUA was carried out using an HP-5MS column (30 m × 0.25 mm (i.d.) × 0.25 mm, 5% phenyl and 95% polydimethylsiloxane), at flow rate 1 mL min−1 and injection temperature of 280 °C. The temperature ramp used was: 70 °C for 1 min, incrementing at 35 °C min−1 up to 180 °C, and 4.50 °C min−1 up to 290 °C, and maintaining 290 °C for 8 min. TOF-MS analyses in API mode were performed in a range m/z = 50–1200, with a corona voltage of 2 kV, chamber temperature of 130 °C and a corona voltage between 10–40 V. PUA were identified comparing the retention times and exact molecular mass measurement (error less than 5 ppm) with those obtained from commercially available standard samples, 2E,4E-heptadienal (90%, Sigma-Aldrich Chemie GmbH, Steinheim, Germany), 2E,4E-octadienal (>96%, Sigma-Aldrich Chemie GmbH), and 2E,4E-decadienal (85%, Sigma-Aldrich Chemie GmbH, St. Louis, MO, USA). For the correct quantification of PUA, calibration lines were performed (1–7000 nM in hexane-2 mL) by comparing the intensity of the signals of the molecular ions of the standard samples. Different synthetic standard solutions of commercial HD, OD, and DD (from 1–15 nM, 15–400 nM, and 400–7000 nM) were used to obtain calibration curves to cover the wide range of molarities in the analysis of pPUA and dPUA. The calibration curves of pPUA and dPUA were constructed separately.
The results were obtained by plotting the peak area of each aldehyde against the area of the internal standard (benzaldehyde). Reproducibility and repeatability of this methodology were evaluated by reanalysis of the standard samples two weeks after the first analysis. The chromatograms were processed with MassLynx software (version 4.1, Waters, Milford, MA, USA).
4.6. Analysis of Fatty Acids
To characterize PUFA in
C. cryptica at the two nutrient conditions assayed, a volume of 200 mL of the stock cultures at F2 and low P conditions were collected, both at exponential and stationary growth phase, following the procedure of [
61]. After centrifugation (3500 rpm for 5 min), the pellets were extracted 5 times with a solution of acetone:methanol (1:1) and sonicated (ultrasound bath, 200 W–50Hz for 5 min). The combined extracts were filtered, evaporated under reduced pressure, and frozen until the analysis of fatty acid methyl esters (FAME). The transmethylation of fatty acids was carried out by treating the extracts with 1 mL of MeOH/HCl (10:1) and heating under reflux for 1 h. After cooling, the reaction was extracted with hexane (3 × 3 mL), and the organic layers were combined, dried over anhydrous MgSO
4, and taken to dryness by rotary evaporation. Fatty acid methyl esters were analyzed by GC-MS on an Agilent Technologies 7890A GC gas chromatography instrument (Agilent Technologies Inc., Santa Clara, CA, USA) coupled to a triple quadrupole spectrometer with an electron impact ionization (EI) source at 70 eV and scanning the mass range
m/
z 50–550. Chromatographic separation of FAME was carried out using an HP-5MS column (30 m × 0.25 mm (i.d.) × 0.25 mm, 5% phenyl and 95% polydimethylsiloxane), at flow rate 1 mL min
-1 and injection temperature of 280 °C. Fatty acid identification was established by comparing their retention time and mass spectrum with MS spectra of the commercial FAME standards Supelco 37 Component FAME Mix (ref. 47885-U, Sigma-Aldrich, Darmstadt, Germany) analyzed by GC-MS under the same conditions of FAME samples and using C17:0 as internal standard.
4.7. Statistical Analysis
Statistical significance was evaluated through one-way ANOVA test at a level of p < 0.05. Two-way ANOVAs were used to test for differences in pPUA and dPUA between phosphate availability, the presence or absence of bacteria, and the interaction of both factors. When appropriate, a Tukey HSD post hoc test was applied to examine all relevant pairwise comparisons between C. cryptica cultures. Spearman correlation analysis was conducted to examine the association between PUA concentrations (pPUA, dPUA, and types) and bacterial assemblage cell densities. All statistical analyses were performed with R-version 3.4.1.