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Open Access

Peer-reviewed

Research Article

Light-dependent variations in fatty acid profiles and gene expression in Antarctic microalgal cultures

  • Jacqui Stuart ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing – original draft, Writing – review & editing

    jacqui.stuart@cawthron.org.nz

    Affiliations Victoria University of Wellington, Wellington, New Zealand, Cawthron Institute, Nelson, New Zealand

    ⨯
  • Kirsty F. Smith,

    Roles Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing

    Affiliation Cawthron Institute, Nelson, New Zealand

    ⨯
  • Matt Miller,

    Roles Conceptualization, Formal analysis, Methodology, Resources, Validation, Writing – review & editing

    Affiliation Cawthron Institute, Nelson, New Zealand

    ⨯
  • John K. Pearman,

    Roles Data curation, Formal analysis, Methodology, Resources, Software, Validation, Writing – review & editing

    Affiliation Cawthron Institute, Nelson, New Zealand

    ⨯
  • Natalie Robinson,

    Roles Funding acquisition, Resources, Writing – review & editing

    Affiliation National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand

    ⨯
  • Lesley Rhodes,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Cawthron Institute, Nelson, New Zealand

    ⨯
  • Lucy Thompson,

    Roles Methodology, Resources, Writing – review & editing

    Affiliation Cawthron Institute, Nelson, New Zealand

    ⨯
  • Sarah Challenger,

    Roles Methodology, Resources, Writing – review & editing

    Affiliation Cawthron Institute, Nelson, New Zealand

    ⨯
  • Nicole Parnell,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Lincoln University, Lincoln, New Zealand

    ⨯
  • Ken G. Ryan

    Roles Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Validation, Writing – review & editing

    Affiliation Victoria University of Wellington, Wellington, New Zealand

    ⨯

Abstract

Photosynthetic eukaryotic microalgae are key primary producers in the Antarctic sea ice environment. Anticipated changes in sea ice thickness and snow load due to climate change may cause substantial shifts in available light to these ice-associated organisms. This study used a laboratory-based experiment to investigate how light levels, simulating different sea ice and snow thicknesses, affect fatty acid (FA) composition in two ice associated microalgae species, the pennate diatom Nitzschia cf. biundulata and the dinoflagellate Polarella glacialis. FA profiling and transcriptomic analyses were used to compare the impact of three light levels: High (baseline culturing conditions 90 ± 1 μmol photons m−2 s−1), mid (10 ± 1 μmol photons m−2 s−1); and low (1.5 ± 1 μmol photons m−2 s−1) on each isolate. Both microalgal isolates had altered growth rates and shifts in FA composition under different light conditions. Nitzschia cf. biundulata exhibited significant changes in specific saturated and monounsaturated FAs, with a notable increase in energy storage-related FAs under conditions emulating thinner ice or reduced snow cover. Polarella glacialis significantly increased production of polyunsaturated FAs (PUFAs) in mid light conditions, particularly octadecapentaenoic acid (C18:5N-3), indicating enhanced membrane fluidity and synthesis of longer-chain PUFAs. Notably, C18:5N-3 has been identified as an ichthyotoxic molecule, with fish mortalities associated with other high producing marine taxa. High light levels caused down regulation of photosynthetic genes in N. cf. biundulata isolates and up-regulation in P. glacialis isolates. This and the FA composition changes show the variability of acclimation strategies for different taxonomic groups, providing insights into the responses of microalgae to light stress. This variability could impact polar food webs under climate change, particularly through changes in macronutrient availability to higher trophic levels due to species specific acclimation responses. Further research on the broader microalgal community is needed to clarify the extent of these effects.

Citation: Stuart J, Smith KF, Miller M, Pearman JK, Robinson N, Rhodes L, et al. (2025) Light-dependent variations in fatty acid profiles and gene expression in Antarctic microalgal cultures. PLoS ONE 20(1): e0317044. https://doi.org/10.1371/journal.pone.0317044

Editor: Thomas Roach, University of Innsbruck, AUSTRIA

Received: June 28, 2024; Accepted: December 19, 2024; Published: January 16, 2025

Copyright: © 2025 Stuart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The raw sequences were submitted to NCBI short read archive under accession number: PRJNA1125028 reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1125028?reviewer=r3q7ii8bhqeorqa35clp2k5ok2.

Funding: This study was supported by Ministry of Business, Innovation and Employment [CAWX1801] grant to the Cawthron Institute naming Kirsty Smith, New Zealand’s National Institute for Water and Atmospheric Research [COAO404] and an Antarctic Science Platform (ASP) [ANTA1801] grant naming Natalie Robinson. The funders had no role in study design, data collection and analysis, decision to publish and preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: C20:4N-6, Arachidonic Acid; C22:6N-3, Docosahexaenoic Acid; C20:5N-3, Eicosapentaenoic Acid; FA, Fatty Acid; GC, Gas Chromatography; C12:0, Lauric Acid; C14:0, Myristic Acid; MUFA, Monounsaturated Fatty Acid; C18:1N-9C, Oleic Acid; C18:5N-3, Octadecapentaenoic Acid; C16:0, Palmitic Acid; C16:1, Palmitoleic Acid; PUFA, Polyunsaturated Fatty Acid; C18:0, Steric Acid; C18:4N-3, Stearidonic Acid; SFA, Saturated Fatty Acid

Introduction

Photosynthetic eukaryotic microalgae support marine biodiversity and biochemical processes in polar ecosystems. These organisms are incorporated into the sea ice column as it forms. They grow in the liquid phase between ice crystals, and become a key macronutrient source for many organisms during the frozen winter and following spring [1–3]. Ice-associated algal communities can persist through summer and autumn, but their contribution to macronutrients is significantly reduced compared to the dominant pelagic phytoplankton [2, 3]. Though polar microalgae are capable of withstanding extreme natural seasonal variability [4], the increased pressure on polar ecosystems due to the changing climate [5], has begun to influence ice associated microalgae community composition [6].

Microalgal growth is driven by many abiotic factors, such as light, temperature, nutrients, and salinity [7, 8]. Of these, light is the most important factor for biomass growth and accumulation in auto-trophic photosynthetic microalgae [9–11]. Light availability in sea ice habitats is directly influenced by the thickness of sea ice and associated snow cover [4, 12–14]. Snow plays a substantial role in regulating under-ice light intensities due to its high albedo, effectively reducing light penetration through the ice below [4, 13].

Snow cover and depth in Antarctica is coupled with the age and duration of sea ice cover [15]. With climate change predicted to reduce sea ice coverage, duration and thickness around Antarctica [16], there may be less time for snow to accumulate. This could lead to increased light penetration during ice algae growing season, however the extent to which light availability will change remains uncertain. The growing season for ice associated microalgae is currently only a few months, and is likely to become more time restricted in coming years [17].

Eukaryotic microalgal communities within polar marine ecosystems are the main source of biomolecules in this environment [18]. These molecules include proteins, carbohydrates, and lipids, which are all essential for the functioning of the Antarctic marine food web [3, 19]. Within microalgal cells, the allocation of photosynthates to the synthesis of various biomolecules is largely influenced by environmental conditions [19]. This means that changes in environmental conditions can impact the quantity and composition of proteins, carbohydrates, and lipids available to primary grazers such as krill and zooplankton [3, 20, 21] (Fig 1).

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Fig 1. Schematic of biomolecular composition for high-level microalgae taxonomic groups and the flow of these through the Antarctic food web.

Microalgae taxonomic groups include diatoms, dinoflagellates (dinos), chlorophytes (chloros) and haptophytes (haptos). Biomolecular composition data adapted from [27]. Though Krill are a component of the zooplankton community, they have been highlighted separately here to show their importance for whales and humans.

https://doi.org/10.1371/journal.pone.0317044.g001

Environmental variability, both within expected ranges and climate change enhanced, can induce shifts in the composition of eukaryotic microalgal communities [4, 6]. Within the eukaryotic microalgal communities, the relative proportion of different high-level taxonomic groups (e.g. class level) can be the largest driver of biomolecular variation in an ecosystem. Fatty acids (FAs) are the densest form of energy of these biomolecules [22]. Different types of microalgae exhibit distinct FA profiles [23, 24]. Pennate diatoms (Bacillariophyceae) for example, have higher levels of certain FAs compared to other taxa such as polar centric diatoms, chlorophytes or haptophytes [25]. Antarctic fast ice and the sub-ice platelet layer ecosystems are dominated by pennate species [26].

Most experimental research into shifts in FA composition associated with light levels for Antarctic microalgae has focused on mixed diatom species [28, 29], while a far smaller number include haptophytes [30] and chlorophytes [31]. Dinoflagellates have not been studied using manipulative experiments to date. Though diatoms do contribute substantially to the ice microalgal community in many cases, dinoflagellates can make up a larger proportion of the eukaryotic microalgal community in the mid and upper extents of sea ice [6, 32]. Within sea and platelet ice environments dinoflagellates consistently contribute more biomass than both chlorophytes and haptophytes, but not diatoms [6, 32].

As FAs are important macronutrients in the polar marine environment, we undertook a laboratory-based experiment to assess the impact of shifting light levels on FA production in two key microalgal groups (pennate diatoms and dinoflagellates) from sea ice eukaryotic microalgal communities. Both pennate diatoms and dinoflagellates have shown substantial shifts in relative abundance within eukaryotic microalgal communities of sea ice and associated under shifting ice regimes in Antarctica [6]. With this experiment, we aimed to investigate: 1) if light conditions that reflect different sea ice thickness change FA composition in two microalgal isolates from key taxonomic groups; 2) how these changes are expressed in FA and photosynthetic biosynthetic gene pathways; and 3) what this may imply for sea ice eukaryotic microalgal community composition changes and their impact on available FAs in the food web. Understanding and quantifying the biomolecular composition of microalgal species from key taxonomic groups will contribute to identifying how environmental conditions impact the synthesis of microalgae biomolecules. These in turn can be applied to analyse observations in broader ice associated eukaryotic microalgal community biomolecular characterisations.

Materials and methods

Microalgae isolates and growing conditions

Two microalgal strains were used, one pennate diatom Nitzschia cf. biundulata (CAWB171; S1A Fig in S1 File) and one dinoflagellate Polarella glacialis (CAWD459; S1B Fig in S1 File). Both were isolated from samples collected in Cape Evans, McMurdo Sound, Antarctica (-77.636, 166.377) in November 2021 (N. cf. biundulata) and November 2022 (P. glacialis). The dinoflagellate P. glacialis was isolated from brine water samples. A hole was drilled in the sea ice to 0.5 m depth, then left for 30 minutes to allow brine to drain into the hole. The pennate diatom N. cf. biundulata was isolated from melted ice scraped from the bottom of sea ice cores. Samples were collected and transported under the permit 2022080076 granted by the New Zealand Ministry for Primary Industries (Manatū Ahu Matua). Cultures were isolated and maintained at the Cawthron Institute Culture Collection of Microalgae (CICCM; http://cultures.cawthron.org.nz/ciccm/). Both cultures were maintained in f/2 media [33] at 35.3 ppt salinity, under 90 ± 1 μmol photons m−2 s−1 irradiance (12:12h Light:Dark photoperiod) at 4 ± 1°C. Small sub-unit rDNA sequences for each isolate used in this experiment were submitted to GenBank under accession numbers PP928079 (N. cf. biundulata) and PP922274 (P. glacialis). These isolates were selected due to their prevalence in field samples.

Experiment design and setup

General culturing conditions described above served as the high light treatment. These growing conditions may seem high for microalgae from sea ice, however, they can experience similar or even greater irradiance levels during late spring melt or in future thinning ice scenarios [4]. This variability underscores the relevance of our chosen experimental conditions for assessing microalgal responses. Two additional light treatments were also used, mid light (10 ± 1 μmol photons m−2 s−1) and low light (1.5 ± 1 μmol photons m−2 s−1) (S2 Table in S1 File). These light levels are reflective of natural light intensities sea ice algae can experience under consolidated fast ice, dependant on ice thickness and snow cover. Seed cultures for each isolate were grown in general culturing conditions, with media refreshed every two weeks. Once enough biomass was accumulated, a total of fifteen cultures were inoculated with 8,000–10,000 cells/mL in 400 mL of f/2 media for both N. cf. biundulata and P. glacialis isolates. Five replicate cultures of each isolate were then acclimated to their respective light treatments for seven days before the experiment began. Cell counts were completed every two days to assess growth rates, with each replicate harvested once they reached exponential growth phase. The low light treatment for P. glacialis was harvested after 56 days as growth had plateaued.

FA extraction and identification

At the point of harvest 280–300 mL of P. glacialis and Nitzschia sp. replicates were centrifuged (3000 x g for 10 minutes) in 50 mL falcon tubes (Corning CentriStar, China). Pellets were stored at –20°C until FAs were extracted. Each pellet was freeze-dried (MartinMartin Christ, Germany) and weighed. Algal pellets (3–30 mg; S3 Table in S1 File) were dried and processed for FA methyl esters (FAMEs) using gas chromatography (GC) analysis as defined in [34]. FA composition was established as defined in [35], with the relative response factor (RRF) of each peak determined from a commercial standard of equal weight [35].

In our results, total proportional FAs and the contributions of polyunsaturated (PUFA), monounsaturated (MUFA), and saturated fatty acids (SFA) are assessed. SFAs have no double bonds between carbon atoms, MUFAs have one double bond, and PUFAs contain two or more double bonds, each playing distinct biological roles. The Omega nomenclature are used to express FAs in the results section, figures and discussion.

RNA extraction

To prepare samples for RNA extractions 100 mL of culture from each replicate (n = 15) was vacuum filtered (S2 Table in S1 File) using 0.45 μm PVDF membrane filters (Merck, Ireland). Samples were snap frozen in liquid nitrogen and immediately stored at -80°C until extraction. Extraction of RNA was complete in sterile conditions using the RNeasy Plant Mini Kit (Qiagen, Germany) per manufacturer’s instructions with the addition of an initial homogenisation step. Samples were homogenised using ZR BashingBead Lysis Tubes (0.1 mm beads; Zymo Research, USA) with Cell Lysis Buffer and the addition of β-mercaptoethanol, as directed in the kit protocol in a 1600 MiniG Spex SamplePrep machine (New Jersey, United States) for 2 mins at 1500 rpm. DNase treatments were then undertaken with a TURBO DNA-free Kit (ThermoFisher Scientific), following the manufacturer’s instructions to remove any remaining DNA contamination. Immediately post DNase treatment, RNA quantification and quality checks were undertaken. The concentration was assessed using a NP80 nanophotometer (Implen, Munich) and quality was visualised on 1.5% agarose gel stained with RedSafe Nucleic Acid Staining Solution (iNtRON Biotechnology, Korea). Samples were then dried and sent to GENEWIZ (AZENTA Life Sciences, Suzhou, China) for subsequent RNA-seq library preparation. Sequencing was completed by GENEWIZ using the Illumina™ NovaSeq 6000 Sequencing System with 150 pair end reads, at a minimum of 20M PE raw reads per sample.

Bioinformatic analysis

Both DNA isolates used in this experiment went through the following bioinformatic and statistical analysis steps separately. Any difference in processing of isolates is highlighted. Assembly of a de novo transcriptome was undertaken via the Oyster River Protocol (ORP) [36] from 9 of the replicates for each isolate. Three random samples from each treatment were selected. The ORP removes sequencing adaptors (Trimmomatic [37]) and filters for quality (Phred <2) before correcting reads (RCorrector [38]). Three unique assemblies were then created using the following assemblers: TransAbyss v2.0.1 [39, 40] and two SPAdes v3.15.2 [41] assemblies using a kmer length of 55 and 75. A single high quality assembly was then produced by merging all assemblies (OrthoFuse [36]). This was then assessed using Benchmarking sets of Universal Single-Copy Orthologs (BUSCO v5.4.4 [42]) with the Odb10 Alveolata dataset as the reference database [43]. Transcripts were annotated against the KEGG database via DIAMOND, then finally read mapping and quantification was performed (SALMON v.1.4.0). All raw sequences are available on NCBI SRA (PRJNA1125028).

Statistical analysis

Statistical analysis was conducted in R (v4.2.1). Statistical differences of percent contribution of FA changes between treatments were assessed via ANOVA, with Tukey’s HSD test for multiple comparisons used to find significant differences. Unless otherwise stated in the results, significant differences between treatments had a p value of <0.05. For transcriptomics data, Kegg Ortholog numbers were used to merge annotated transcripts, and differential expressed genes identified via DESeq2 (wald test and parametric fit). Adjusted p-values were calculated using the Benjami and Hochberg method (BH; [44]). KEGG pathway groups were created for differentially expressed genes and statistically significant log fold changes were tallied and plotted using ggplot2 [45]. Differentially expressed genes from photosynthetic, FA biosynthesis and FA degradation pathways were mapped against pathway diagrams using the KEGG database. The pipeline for statistical analysis is available on GitHub (https://github.com/JustJaxz/Lipids-and-Light).

Results

Growth curves

Growth rates for microalgal cultures N. cf. biundulata and P. glacialis varied under different light treatments (Fig 2). The high and mid light conditions for N. cf. biundulata reached the exponential growth phase target range of 60, 000–70,000 cells/mL in 10 and 12 days respectively. The five low light replicates reached the target range at two different time points: 15 days (n = 3) and 22 days (n = 2). Polarella glacialis was harvested at 15 days (all high replicates), 22 days (all mid light replicates) and 56 days (all low light replicates). High and mid light P. glacialis replicates were harvested in the target range of 70,000–100,000 cells/mL, low light replicates did not reach exponential phase targets (average cell density: 21,370 ± 5107 cells/mL).

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Fig 2.

Growth curves for a) Nitzschia cf. biundulata and b) Polarella glacialis under high, mid, and low light experimental conditions. * marks harvest day.

https://doi.org/10.1371/journal.pone.0317044.g002

Fatty acids (FAs)

There were some significant differences in overarching FA groups for N. cf. biundulata among light treatments. Production of saturated FAs (SFAs) was significantly different (ANOVA: F = 18.6, p < 0.001) with high light producing more than mid (Tukey: 95% CI [-0.31, -0.10], p < 0.001) and low (95% CI [-0.30, -0.09], p < 0.001) treatments (Fig 3A). There was no significant difference between mid and low light for SFAs. Monounsaturated FAs (MUFAs) showed the same trend with significant differences (F = 26.7, p <0.0001) between high and both treatment conditions (95% CI, mid: [-0.17, -0.07] p < 0.01; low [-0.16, -0.06] p < 0.01), with no significant difference between mid and low treatments (Fig 3A). No significant differences were observed in polyunsaturated (PUFA) and Omega-3 polyunsaturated FAs (N-3 PUFA) production for N. cf. biundulata.

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Fig 3.

Total fatty acid (FA) profile and percentages of saturated FAs (SFAs), monounsaturated FAs (MUFAs) and polyunsaturated fatty acids (PUFAs) and omega 3 FAs (N-3-PUFA) produced by a) Nitzschia cf. biundulata and b) Polarella glacialis under high, mid, and low light experimental conditions. Boxplots display median values, and significant changes in FA production are annotated: *p = < 0.05, **p = < 0.01, *** p = < 0.001, ****p = < 0.0001 (P value adjustment method: Benjamini-Hochberg).

https://doi.org/10.1371/journal.pone.0317044.g003

Cultures of P. glacialis had significant differences in production of SFAs and MUFAs among all experimental conditions (SFAs: F = 59.88, p < 0.001; MUFAs: F = 22.77, p < 0.001) with mid light significantly lower than both high (95% CI, SFA: [-8.4, -5.1], p < 0.01; MUFA: [-5.9, -0.1], p < 0.05) and low light (95% CI, SFA: [-3.84, -0.48], p < 0.05; MUFAs: [-10.3, -4.5], p < 0.05; Fig 3A). High and low light also had significant differences in FA production, with higher SFAs in high light conditions (95% CI, SFA: [-6.3, -2.9], p < 0.05) and higher MUFAs in low light (MUFAs: [1.4, 7.3], p < 0.05; Fig 3B). Proportional contribution of PUFAs was substantially greater than other reported FAs in our results, all P. glacialis cultures, ranging from 53%– 74% of total FAs (Fig 3B). PUFAs in the mid light condition were produced in significantly higher proportions than both high (95% CI, [10.5, 18.3], p < 0.01) and low light (95% CI, [8.92, 16.72], p < 0.01) conditions. N-3 PUFAs, including Stearidonic and Eicosapentaenoic acids were significantly higher in high light treatments than mid (95% CI, [-6.98, 1.22], p < 0.05). Low light had no significant differences in production from either high or mid conditions.

Saturated fatty acids (SFAs)

The major SFAs were similar in both N. cf. biundulata and P. glacialis isolates (Fig 4, S4 Table in S1 File). In N. cf. biundulata cultures, lauric acid (C12:0) was significantly lower in high light compared to mid (95% CI [0.42, 0.52] p < 0.01) and low (95% CI [0.39, 0.49], p < 0.01) treatments and myristic acid (C14:0,) significantly increased as light decreased (Low-mid: 95% CI [-0.17, -0.03], p < 0.01; mid-high: [0.04, 0.18], p < 0.01). Palmitic acid (C16:0) significantly decreased as light increased (Low-mid: 95% CI [0.1, 0.24], p < 0.05; mid-high: [-0.55, -0.32], p < 0.0001) and was the most abundant SFA for N. cf. biundulata cultures. Stearic acid production (C18:0) significantly increased between mid and low conditions (Fig 4A; 95% CI [-0.30, 0.00], p < 0.05).

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Fig 4.

Percentages of a) Saturated Fatty Acids (SFAs), b) monounsaturated fatty acids (MUFAs) and c) polyunsaturated fatty acids (PUFAs) produced by Nitzschia cf. biundulata and Polarella glacialis cultures under high, mid and low light experimental conditions. Results reported in this figure represent FAs that had significant changes or contribute substantially to percent total FAs. Full FA results in S4 Table in S1 File. SFAs include: Lauric (C12:0), Myristic (C14:0), Palmitic (C16:0), Steric (C18:0); MUFAs include: Palmitoleic (C16:1), Oleic (C18:1n-9C); and PUFAs include Arachidonic (C20:4n-6), Stearidonic (C18:4n-3), Docosahexaenoic (C22:6n-3), Eicosapentaenoic (C20:5n-3) and Octadecapentaenoic (C18:5n-3). Boxplots display median values, and significant changes in production are annotated: *p = < 0.05, **p = < 0.01, *** p = < 0.001 (P value adjustment method: Benjamini-Hochberg).

https://doi.org/10.1371/journal.pone.0317044.g004

Polarella glacialis had a significant increase in C12:0 between high and both mid (95% CI [0.63, 1.13], p < 0.05) and low (95% CI [0.23, 0.73], p < 0.05) treatments, with mid also producing significantly higher amounts than low light (95% CI [0.15, 0.65], p < 0.05). C14:0 significantly increased in high light compared to other light levels (95% CI Low: [-5.77, -3.23], p < 0.05; mid: [0.63, 1.13], p < 0.01). Production of C16:0 was significantly lower in mid light compared to both high (95% CI [-3.09, -1.07], p < 0.05) and low light (95% CI [-2.75, -0.73], p < 0.05), while C18:0 significantly decreased between high light and both mid (95% CI [-1.06, -0.14], p < 0.05) and low conditions (95% CI [-1.14, -0.22], p < 0.05; Fig 4A).

Monounsaturated fatty acids (MUFAs)

Two major MUFA profiles were identified in both N. cf. biundulata and P. glacialis isolates (Fig 3B). In N. cf. biundulata cultures palmitoleic acid (C16:1) was significantly lower in mid light conditions than both high (95% CI [-0.38, -0.22], p < 0.001) and low light (95% CI [-0.18, -0.01], p < 0.05). In contrast, the production of oleic acid (C18:1n-9C) in mid light was significantly higher than both high (95% CI [0.62, 0.94], p < 0.01) and low light (95% CI [0.19, 0.53], p < 0.01). Polarella glacialis cultures had significantly more C16:1 in low light conditions than high (95% CI [0.23, 0.65], p < 0.05) and mid (95% CI [-2.75, -0.73], p < 0.01). Contribution of C18:1N-9C were significantly lower in mid light than both other conditions (95% CI, low: [-0.73, -0.31], p < 0.05; high: [0.23, 0.65], p < 0.05) (Fig 4B; S4 Table in S1 File).

Polyunsaturated fatty acids (PUFAs)

Four major PUFAs were detected for N. cf. biundulata and P. glacialis (Fig 4C). N. cf. biundulata had a significant increase in arachidonic acid (C20:4n-6) in mid light conditions compared to high (95% CI [0.06, 0.29], p < 0.05) and low light (95% CI [0.73, 0.98], p < 0.01). Stearidonic (C18:4n-3) percentage decreased with light levels, with highest production of C18:4n-3 in high light and lowest in low light. Both mid and low light produced significantly higher levels of Docosahexaenoic acid (C22:6n-3) than high (95% CI, low: [0.14, 0.51], p < 0.05; mid: [0.04, 0.40], p < 0.05). Eicosapentaenoic (C20:5n-3) contributed substantially to PUFA proportion overall in N. cf. biundulata and did increase linearly with light levels, however these changes were not statistically significant.

Octadecapentaenoic (C18:5n-3) was only detected in P. glacialis, contributing between 22%– 47% of total PUFAs. P. glacialis had significantly higher levels of C18:4n-3 in high conditions compared to both mid (95% CI [-2.00, -1.40], p < 0.05) and low light (95% CI [-1.74, -1.14], p < 0.05). This was significantly higher in mid light conditions compared to both high (95% CI [13.50, 25.82], p < 0.01) and low light (95% CI [9.88, 22.20], p < 0.01). There was no significant difference between high and low light. Mid light also had significantly higher amounts of Eicosapentaenoic (C20:5n-3) than both high (95% CI [0.96, 1.72], p < 0.05) and low light (95% CI [0.06, 0.82], p < 0.05). Docosahexaenoic acid (C22:6n-3) was a relatively large contributor to total PUFAs, ranging between 16–25% across treatments. There were no significant changes in C22:6n-3 production across light treatments (Fig 4C; S4 Table in S1 File).

Transcriptomics

Sequencing output and assembly.

Transcriptome assembly for N. cf. biundulata contained 146,966 transcripts and was 98.9% complete, with 17% single copies, 81.9% duplicated, 0.6% fragmented and 0.5% missing according to BUSCO. For P. glacialis assembly of the transcriptome contained 2,333,204 transcripts was 96.5% complete, with 45.6% single, 50.9% double, 2.9% fragmented and 0.6% missing according to BUSCO. Significant differences in transcriptomes were found for all experimental conditions of both N. cf. biundulata (PERMANOVA: F = 25.04; p = 0.001) and P. glacialis (PERMANOVA: F = 9.42; P = 0.001) cultures (S5 Fig in S1 File).

Differentially expressed genes.

Low light levels are representative of thick sea ice conditions (~2 m thick). Gene regulation is reported using low light as the baseline, with up or down regulation in relation to low light transcripts. There was a substantial number of differentially expressed genes among each light level treatment for both N. cf. biundulata and P. glacialis. For N. cf. biundulata most of these were related to genetic information processing, environmental information processing and ‘other metabolism’. Briefly, the genetic information processing genes encompass mechanisms involved in DNA replication, transcription and translation, environmental information processing refers to pathways and responses that allow organisms to sense and adapt to changes in their environment. Other metabolism encompasses a variety of metabolic pathways that are not classified under primary metabolic functions such as secondary metabolism and energy production. Lipid metabolism genes among N. cf. biundulata did have a number of differentially expressed genes, with a substantial portion of those from the FA biosynthesis, elongation, and degradation pathways. A number of energy metabolism, and specifically photosynthesis genes were also differentially expressed (S6 Fig in S1 File).

Overall, there was a lower number of differentially expressed genes for all P. glacialis light treatments compared to N. cf. biundulata. Genetic information and environmental information processing also accounted for the majority of differentially expressed genes in P. glacialis. However, translation genes in low light treatments, energy metabolism and ‘other metabolism’ also had higher numbers when compared to other pathways within P. glacialis treatments (S6 Fig in S1 File). There were exceptionally low levels of differentially expressed genes involved in lipid metabolism, with none for FA biosynthesis or elongation, and only two involved in the FA degradation pathway.

Photosynthetic gene pathway.

Nitzschia cf. biundulata had differentially expressed genes across all regions of the photosynthetic pathway (Fig 5A). Mid light conditions caused up-regulation of multiple genes in photosystem II (PSII) while high light conditions caused down-regulation. The PSII pathway showed similar patterns of increased activity in the mid light treatment compared to the low light treatment, although these changes were not statistically significant (S7 Fig in S1 File). Differentially expressed genes within the cytochrome b6/f complex (Photosystem electron transport; Pet) were consistently up regulated in mid light conditions. Up-regulation of Photosystem I (PSI) differentially expressed genes were also generally observed in mid light conditions. There was only one differentially expressed gene in ATPase synthesis region, which was also upregulated in mid light conditions. Cultures of P. glacialis had 22 differentially expressed photosynthesis genes compared to 16 for N. cf. biundulata (S8 Fig in S1 File). Differentially expressed photosynthesis genes in P. glacialis were evenly distributed across the PSII, Pet, PSI, and ATPase regions. In the mid-light treatment, gene expression was consistently downregulated across all components of the photosynthetic system (Fig 5B).

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Fig 5.

Simplified gene pathways with overarching patterns of differential expression for genes from a) Nitzschia cf. biundulata and b) Polarella glacialis cultures grown in high, mid and low light conditions. Up and down regulation of high and mid light are noted in relation to low light conditions, as these are reflective of ‘typical’ sea ice conditions. When bars are stacked this represents differential expression between high and mid, while side by side indicates no differential expression.

https://doi.org/10.1371/journal.pone.0317044.g005

FA biosynthesis and degradation.

Differentially expressed genes from the initiation stage of the FA biosynthesis pathway from N. cf. biundulata cultures grown in mid light conditions were mostly down-regulated compared to low light (Fig 5A). Up-regulation of genes occurred in both the mitochondrial and endoplasmic reticulum elongation stages of the FA biosynthesis pathway, and no differentially expressed genes were detected in the termination phase. Genes were down-regulated in the dehydrogenation (removal or transfer of a proton, or hydrogen) and oxidation stages of the FA degradation pathway from both mid and high light cultures. However, FA degradation genes related to hydration were up regulated for both mid and high light (S9-S11 Figs in S1 File). There were no differentially expressed genes observed throughout both biosynthesis, elongation, and the main FA degradation pathways in P. glacialis cultures (Fig 5B; S12 Fig in S1 File). The two involved in FA degradation processes that were differentially expressed were in the aldehyde to FA component of the pathway.

Discussion

Light levels emulating thinning sea ice conditions caused substantially different intracellular responses in microalgal isolates from two high-level taxonomic groups. Both the pennate diatom (N. cf. biundulata) and dinoflagellate (P. glacialis) cultures experienced changes in growth rates, as well as shifts in FA composition and gene expression in energy and FA metabolism pathways. Growth rates increased for both isolates as light intensity increased. Low light conditions were clearly unfavourable for P. glacialis growth, as cell densities barely increased, and all cultures failed to enter the exponential growth phase during the experiment. This inability for P. glacialis to reach exponential growth under low light conditions was surprising, as this dinoflagellate has been reported as a widespread and resilient species in polar sea ice environments where light levels are often low [46, 47]. The poor growth under low light could be due to several factors, such as not reaching the minimal light threshold to support efficient photosynthesis, day-length or light wavelength cues which may not have been fully replicated in the experimental set up.

Additionally, photosynthetic genes were down-regulated in high light treatments compared to low light, indicating P. glacialis growing under low light was investing considerable energy in survival as opposed to growth. Optimal growth conditions related to light are not uniform for microalgae taxa [11]. For example, Arctic sea ice algae are less tolerant of high light levels and may experience photoinhibition, a process where excessive light energy damages photosynthetic apparatus [48, 49]. In contrast, higher light is linked to increased cell densities and faster growth rates [50–52], as observed here. The Low treatment light levels used in this study are in the lower range of intensities investigated previously (Low: 1.5 ± 1, Mid: 10 ± 1, High: 90 ± 1 μmol photons m−2 s−1) [e.g. 53–55]. Given that both isolate species are naturally exposed to large changes in light intensity in Antarctic sea ice environments annually [4], all the light levels used in this study fall within the range they would naturally experience.

FA profiles for N. cf. biundulata showed no significant changes in production within the overarching groups of SFAs, MUFAs and PUFAs. Though a N. cf. biundulata FA profile has not previously been characterised, other Nitzschia sp. profiles are comparable to those observed here under high light conditions [56]. However, variation among Nitzschia species are often greater than differences observed between light treatments in this study. C20:5N-3 made up a substantial proportion of N. cf. biundulata’s PUFAs, similar to other Nitzschia sp. profiles [56]. The indication of a linear decrease in EPA with rising light levels, though not statistically significant, suggests that N. cf. biundulata may tend to produce less C20:5N-3 under high light conditions. This reduction of C20:5n-3 in microalgae grown under increased irradiance has been observed in several other microalgal species [57], including Nitzschia alexandrina [58]. C20:5n-3 is a major structural component in chloroplast membranes [59, 60], the reduction under high irradiance may indicate a broader metabolic shift from production of structural (PUFA) to storage lipids [57]. This aligns with the increase in production of C16:1 and C16:1n-7 observed here in N. cf. biundulata grown under high irradiance. Each of these can be important in the biosynthesis of the primary form of storage lipids, triacylglycerols (TAGs) [61]. However, the lack of statistical significance means this trend cannot be confidently linked to light changes without further investigation.

Significant changes within individual SFA production for our N. cf. biundulata cultures included the reduction of C14:0, which would increase cell membrane fluidity, allowing for enhanced photosynthetic efficiency and reduction in the risk of photodamage [62, 63]. Increased fluidity may also help prevent damage to membranes due to the formation of ice crystals [64]. This response, coupled with the up-regulation of photosynthetic genes indicates N. cf. biundulata cells are enhancing photosynthetic activity and biomass production [65, 66] in light conditions emulating thinning sea ice conditions. The elevation of C16:0 in conjunction with MUFA C18:1N-9C in mid light would suggest an increase in energy storage [66, 67]. Interestingly, in our highest light treatments, we observed elevated levels of C16:1, which aligns with findings from [68, 69] that suggest increased C16:1 is indicative of lipid accumulation under high irradiance and nutrient stress. This increase in energy storage is reinforced by the down-regulation of FA degradation genes in mid-light cultures [70].

FA profiles for P. glacialis cultures were dominated by PUFAs, and mainly C18:5N-3 which contributed up to 50% of total FAs, aligning with previous FA profiles for this species [71]. Significant differences in the proportion of SFAs, MUFAs, PUFAs and N-3 PUFAs relative to total FAs were observed. In mid light, the proportion of SFAs and MUFAs decreased, while the proportion of PUFAs increased compared to both low and high light conditions. This most likely indicates a change in energy storage strategy [66, 72]. For P. glacialis, the significant changes in PUFA proportions were marked by a decrease under both low and high light conditions. A reduction in PUFA levels is commonly seen under lower light [72, 73], making the lower FA production levels in conditions with the highest light intensity intriguing.

The unimodal response of PUFA proportion to light treatments may be related to a species-specific optimum for P. glacialis. A draft genome assembly of P. glacialis strains has highlighted functional innovation associated with environmental adaptation and niche specialisation [74]. Higher PUFA production in mid light cultures may indicate increased thylakoid membrane stacking for photosynthesis optimisation [75]. PUFAs are important for maintaining membrane fluidity which supports the formation of stacked membranes, particularly under ideal light conditions [76]. This unimodal pattern could also reflect a trade-off between energy capture and photoprotection, where intermediate light levels allow for optimal energy production without triggering excessive stress responses, from too little or too much light.

Interestingly, this was not reinforced in photosynthetic pathway transcripts, as photosynthetic genes in the mid-light treatment were down regulated in comparison to low-light. This difference in responses may be explained by a shift in resource allocation towards protective mechanisms or increased metabolic efficiency under mid-light conditions. In contrast, the up-regulation of photosynthetic genes and reduction in PUFA proportion in low-light treatments indicates an attempt to decrease proton leakage and improve energy efficiency and production within the cells [65, 77]. Similar to N. cf. biundulata cultures in this study the reduction of PUFA proportion in high light may indicate the prioritisation of photoprotective strategies [29]. This is achieved by reduced production of structural lipids (PUFAs) as fewer chloroplast membranes are needed under bright light [57, 60].

High levels of C18:5N-3 have been observed in P. glacialis previously [71], however there was a significant increase in production of C18:5N-3 (up to 20%) under emulated thinning ice conditions. Biologically high levels of C18:5N-3 indicates enhanced membrane fluidity, and improved synthesis of longer-chain PUFAs like C20:5N-3 and C22:6N-3 [78, 79]. No evidence of up-regulation within FA biosynthesis pathways was found to suggest an effort to increase synthesis in mid light. Moreover, no significant increase was observed in C22:6N-3. C20:5N-3 did increase in production in mid light conditions, though compared to C22:6N-3 (19–22%) and C18:5N-3 (25–44%) was only a minor contributor to overall PUFA production (3–5%). This observation of biomolecule production without corresponding transcriptomic changes is not unique to P. glacialis. The dinoflagellate species Alexandrium catenella and A. minutum have both demonstrated that their production of toxic biomolecules may also not be regulated at the transcriptional level [80, 81]. These observations suggest that dinoflagellates employ different strategies compared to diatoms when acclimatising to varying conditions, highlighting the complexity of metabolic regulation in these organisms.

The FA C18:5N-3 is an ichthyotoxic molecule, (toxic to fish) [82, 83]. Other marine dinoflagellates that are high producers of C18:5N-3 have been linked to fish mortality [82, 84]. Increased production of C18:5N-3 under emulated thinning ice conditions could be of concern for toxicity at higher trophic levels in polar environments. Further investigation into the influence of other abiotic changes in conjunction with light would be required to elucidate the level of risk this species may present in the future.

Changes in sea ice cover and duration in McMurdo Sound will influence microalgae community composition and potentially reduce diversity [6]. Predicted shifts within Antarctic microalgae communities, moving from larger diatom species toward smaller taxa like P. glacialis, could alter energy transfer efficiency within Antarctic food webs, affecting overall productivity [85–88]. Our findings indicate that P. glacialis enhances C18:5N-3 production under mid-light conditions, which raises concerns about the potential emerging threat of toxins in the Antarctic food web and the associated risks to marine fauna. Conversely, N. cf. biundulata exhibited stable fatty acid profiles but reduced specific SFAs, a change that could enhance membrane fluidity and photosynthetic efficiency. This response might allow N. cf. biundulata to acclimate effectively to fluctuating light conditions, ensuring metabolic efficiency amidst environmental changes.

The transfer of energy and carbon through trophic levels in the Antarctic food webs is anticipated to be impacted by reduced microalgal community diversity [85, 86]. Smaller cell sizes reduce grazing efficiency for primary consumers [89, 90]; combined with lower diversity, this means shifts in FA and other biomolecule production could be amplified within ecosystems. Anticipated shifts in community composition and trophic transfer underscore the need to understand single stressor impacts, as examined in our study, to better predict the multifaceted effects of climate change on Antarctic ecosystems.

Lab based experiments to assess the impact of environmental changes have limitations. This study only includes a single stressor, which is not reflective of what will be experienced in the natural environment [e.g. 50, 91]. Understanding the acclimation response of species to single stressors does still provide valuable insights, such as establishing baseline responses, identifying key molecular and physiological mechanisms, or determining stress tolerance thresholds. Prolonged growth of microalgae isolates in culture conditions may also influence the acclimation response of both species to lower light levels [92]. Future work investigating the community level biomolecular composition and meta-transcriptomics in situ would help elucidate how much impact variable light levels will have on nutrient and carbon transfer within the sea ice and sub-ice platelets ecosystems. Additionally, the use of respective data such as absolute fatty acid amounts or POC standardised fatty acid content.

Light levels emulating thinning sea ice and snow conditions have an impact on the photosynthetic activity and FA composition of N. cf. biundulata and P. glacialis isolates. Though light level changes are not necessarily a concern with changing climate in most marine ecosystems, thinning of ice and variation of snow loads will impact light condition in sea ice environments [5]. Differing responses in photosystems and growth rates have the potential to create changes in overall microalgae community composition and their nutritional quality with potential impacts on the whole food web. Further studies are needed to assess the resulting change in macronutrients available to higher trophic levels and food web structure in Antarctica.

Supporting information

S1 File. Comprehensive phylogenetic, microscopy, experimental conditions, fatty acid analysis, and transcriptomic data for Nitzschia cf. biundulata and Polarella glacialis under varying light treatments.

https://doi.org/10.1371/journal.pone.0317044.s001

(PDF)

S1 Graphical abstract.

https://doi.org/10.1371/journal.pone.0317044.s002

(TIF)

Acknowledgments

We thank the Scott Base staff of the 2022 science season and all members of the K892 and K043 field teams for logistics support and assistance in the sampling process. We thank Antarctica New Zealand for their financial and logistic support via the Sir Robert Irvine Doctoral Scholarship (J.S.). New Zealand’s National Institute for Water and Atmospheric Research and Antarctic Science Platform supported the 2022 field sampling season, as well as time for NR and KR.

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