Although diatoms are the most species-rich group of algae, diatom research has immensely benefited from model species such as Thalassiosira pseudonana and Phaeodactylum tricornutum (Falciatore et al., 2020). However, due to limitations given by the nature of both models (e.g., small and streamlined genomes, limited evidence for sexual reproduction, mesophiles, in culture for many decades), additional diatom species have been developed into models. One of them is F. cylindrus, which is the first and only eukaryotic psychrophile so far that is genetically tractable (Faktorová et al., 2020) with a sequenced genome, and grows well under laboratory conditions (Mock et al., 2017). Hence, it has been used to study physiological and molecular adaptation to polar conditions. Significant seasonality in light and the overall low temperatures are amongst the strongest selecting agents shaping the evolution and adaptation of F. cylindrus and likely other polar diatoms. In this section of the review paper, we will focus on molecular data gained from the sequencing of polar diatoms, mainly F. cylindrus, and how this molecular data compares to that from other psychrophilic microbes, and mesophilic model diatom species.

In most temperate and polar aquatic environments, especially coastal waters, diatoms are typically one of the first taxa groups to bloom with the changing seasons by taking advantage of renewed nutrients and increased photosynthetically active radiation (PAR) (Soreide et al., 2010; Kvernvik et al., 2018). Polar diatoms are no exception, due to mechanisms allowing them to survive the polar winter with the necessary cellular components ready and waiting for the return of spring (Peters and Thomas, 1996; van de Poll et al., 2020). However, the molecular mechanisms underpinning these adaptations have been a mystery. Polar phytoplankton have been documented to employ a diverse set of strategies, such as forming resting spores, heterotrophy and respiration of stored lipids (Bunt and Lee, 1972; Reeves et al., 2011; McMinn and Martin, 2013; Schaub et al., 2017). The traditional view of a pause in biological activity has recently been challenged as more data during the winter months is collected, indicating that there is stable biological activity (Berge et al., 2015). This is suggesting a diverse polar microbial community with multiple mechanisms of survival in prolonged darkness. The addition of detailed transcriptomics and proteomics data from F. cylindrus cultured in complete darkness has enabled researchers to explore genetic variation, and how it could explain the physiological activity in prolonged darkness. Within 24h of the onset of darkness, there was a significant reduction in the abundance of light-harvesting protein complexes (LHCs), which transfer light energy to the photosystem reaction center (Kennedy et al., 2019). Despite the sudden widespread downregulation in the abundance of LHCs, these complexes are maintained at a low basal level in the absence of light. This coincided with the upregulation of proteins affiliated with glycolysis, the TCA cycle and the Entner–Doudoroff pathway, suggesting a coordinated shift from photosynthesis to cellular respiration to maintain essential functions for survival (Figure 1; Kennedy et al., 2019). This large response in the proteome corroborates data on differential expression in the F. cylindrus transcriptome in response to prolonged darkness (Mock et al., 2017). Dynamic regulation of the proteome and transcriptome to significant changes in light intensity does not appear to be exclusive to F. cylindrus. For example, the model psychrophile Chlamydomonas raudensis and the natural community around it, also maintained photosystem complexes at a basal level when cultured in dialysis bags in Lake Bonney (McMurdo Dry Valleys, Antarctica) during the transition to polar night conditions (Morgan-Kiss et al., 2016). Despite this study focusing on a different ecosystem and a distantly related species from diatoms, the same molecular response was discovered, suggesting co-adaptation to the polar climate across distinct phytoplankton communities.

After months of darkness, the sea-ice thickness begins to decline in the spring and more light can reach diatom populations in the surface ocean eventually becoming sustained high light during the summer months. The F. cylindrus genome encodes significantly more LHCx genes, a subset of LHC sequences that are induced by high-light stress, than the model temperate diatoms T. pseudonana and P. tricornutum. The evolutionary expansion and upregulation of LHCx genes, known to reduce high-light stress, suggests their key role in the adaptive evolution to the polar light climate (Figure 1; Mock et al., 2017). Interestingly, the LHCx gene family is also expanded in the mesophilic alga Aureococcus anophagefferens, enabling this species to rapidly form blooms in high-light coastal environments (Gobler et al., 2011; Mock et al., 2017). Thus, to be prepared for high-light environments after a period of darkness, polar diatoms appear to be downregulating a subset of photosynthesis-associated genes accompanied by increasing cellular respiration. This combination of acclamatory metabolic processes enables them to survive the polar winter in an active state (Berge et al., 2015) and take full advantage of the spring and summer high-light conditions, especially in open water by upregulation of LHCx genes. These results show the usefulness of genomic data in researching how phytoplankton adapted to the physical polar environment, and the extent to which adaptations are possibly unique to specific taxa groups, giving more insight into the potential impact climate change will have on diverse polar phytoplankton communities.

Metagenomics, metatranscriptomics, and metaproteomics are emerging as important tools for understanding the molecular basis of adaptation in polar microbes by allowing us to study organisms in their ecosystem and to capture their interactions in the natural environment (McCain et al., 2021). Metagenomics has been used to establish which diatom species are present in certain locations, providing a baseline for future monitoring systems. For example, the Tara Oceans project investigated the global distribution of diatoms and discovered unexpected species in polar oceans (Malviya et al., 2016; Carradec et al., 2018; Obiol et al., 2020). Similarly unexpected was that polar oceans are a particular hotspot of viral diversity with high levels of novel genes (Gregory et al., 2019). Thus, metagenomics helps to reveal the microbial biodiversity in polar oceans and how different it is when compared to metagenomes from non-polar oceans. Metagenomics has also been applied to provide insights into how polar environmental gradients (e.g., sea-ice water interface) influence microbial biodiversity. For instance, a metagenomics survey of microbial communities in the Canadian Arctic found that in sea-ice there was a higher concentration of algal genes and chlorophyll-a compared to seawater underneath, where prokaryotic genes were more dominant. Metagenomic data showed that this was largely due to diatoms. Differences in operational taxonomic units (OTUs), a group of closely related individuals, between sea-ice and sea-water samples indicated that sea-ice dwellers have different strategies for adaptation than seawater-based microbes. Increased variability was found in the sea-ice communities, perhaps indicative of the adaptability of polar diatoms as a dominant and diverse group of microbes in the sea-ice habitat (Yergeau et al., 2017).

Sequencing of individual species such as F. cylindrus, P. glacialis, and Chlamydomonas sp. UW0241 (Figure 2), has advanced our fundamental understanding of polar algae (Bayer-Giraldi et al., 2010; Mock et al., 2017; Stephens et al., 2020; Zhang et al., 2021). However, their sequencing requires resources and takes time. Yet, they serve as a reference not only for fundamental research but also for analyzing phytoplankton-enriched metagenomes. The latter together with reference genomes therefore will allow for the assembly of novel algal genomes isolated from natural communities including their associated microbiomes. Insights from this work will help us to understand the similarities and differences between genomes from natural communities, rather than relying on model organisms which risks not representatively capturing the diversity of adaptive mechanisms underlying their complex intertwined co-evolution. For instance, targeted metagenomics has been used to establish the evolutionary history of picoprymnesiophyte populations in the North Atlantic, which make up approximately 25% of global pico-plankton biomass. Their dominance was found to be due to high gene density, and genes that are likely to be involved in defense and nutrient uptake (Cuvelier et al., 2010).

Metagenome-assembled genomes (MAGs) of prokaryotes have been produced using large quantities of metagenomic data from both the Arctic and Antarctic (Cao et al., 2020; Royo-Llonch et al., 2021). Increased availability of high-quality MAGs without the requirement for individual culturing and sequencing will allow researchers to study a broader, more representative range of polar microbiomes and compare between species to understand mechanisms for polar survival (Cao et al., 2020; Duncan et al., 2020). MAGs have been used to study polar bacterial and archaeal populations from the Tara Oceans dataset, providing the first compendium of Arctic prokaryotic MAGs including differences in gene enrichment between Arctic and Antarctic populations (Cao et al., 2020; Royo-Llonch et al., 2021).

Although most MAGs from polar oceans are still from prokaryotes, one of the first MAG-based datasets from natural Arctic and Atlantic microbial communities was used to reveal inter-kingdom species associations including microbial eukaryotes (Duncan et al., 2020). Besides the generation of first eukaryotic MAGs from a polar ocean (e.g., diatoms, prasinophytes), this dataset was used to identify metabolism enriched in MAG-based species associations. By identifying which protein families are enriched in selected species, comparisons of associations to a background set of phylogenetically related species known not to have associations shed first insights into shared metabolism, potentially underpinning their biotic interactions. By applying this approach, Duncan et al. (2020) found positive and negative associations between algal and bacterial MAGs including some that were only found in the Arctic (e.g., Micromonas MAG associated with a Gammaproteobacteria MAG). Combined with cell isolations from the same samples, these MAG-based species associations can be empirically tested if the microbial partners can be co-cultivated under laboratory conditions.

Investigation of the genes expressed across global oceans was carried out as part of the Tara Oceans project (Bork et al., 2015) and the Sea of Change project (Martin et al., 2021). The former has been used to create an ocean atlas of eukaryotic genes from temperate and tropical regions whereas the latter has been used to produce the first pole-to-pole catalog of expressed genes from microalgae and other microeukaryotes. Most of the expressed genes in the Tara Oceans Atlas were novel, indicating that eukaryotic ocean life is under-studied and therefore incompletely understood. Additionally, genes specific to the Southern Ocean were different to those found elsewhere, potentially indicating that adaptation to polar conditions results in a very different gene set to non-polar species (Carradec et al., 2018). The latter insights were corroborated and extended by the Sea of Change project, which revealed that global algal microbiomes can be largely separated into two main groups: polar and non-polar species associations. The same demarcation was found for expressed genes of the algal partners considering their biogeographic distribution from pole to pole based on a combination of sequence co-occurrence analysis and the geographical location of breakpoints in their beta diversity (Difference in species composition between neighboring assemblages) (Martin et al., 2021). Thus, it appears that there are ecosystem boundaries in sub-polar regions of the upper ocean in both hemispheres separating polar from non-polar algal microbiomes.

Although metaproteomics with natural polar microbial communities is still in its infancy, the first study on coastal Antarctic phytoplankton communities has been undertaken. This study by McCain et al. (2021) used proteome-level traits to identify ecological strategies for different taxa. For instance, haptophytes appear to have a lower regulatory cost than diatoms, which may explain the observed haptophyte-to-diatom bloom progression in the Ross Sea. As protein synthesis is the main energy sink in cells, the quantity of cellular proteins measured as part of complex metaproteomes relates to costs involved in their synthesis. Hence, the nature and quantity of specific proteins identified potentially provides insights into the costs of traits and trade-offs. Challenges arise, however, with incomplete taxon-specific proteomes as part of complex metaproteomes especially if reference data are missing.

Genetic monitoring using metagenomics and metatranscriptomics of mixed communities to oversere changes in populations over time can be used to study adaptations to changing environmental conditions, showing the rate of evolution over time and how adaptations are occurring (Hansen et al., 2012; Pearson et al., 2015). However, sequence-based monitoring of microbial communities and their populations needs to be carefully designed to identify the most appropriate sites for capturing changes in the biogeography of species and their populations including alterations of gene flow and climate-driven range-shift expansions in their distribution. Reoccurring surveys with sufficient spatial sampling in a given geographic area or multiple monitoring sites with frequent sampling will be needed for a rigorous assessment of how warming in polar oceans influences microbial populations and the biogeochemical cycles they drive. Furthermore, monitoring needs to cover complete seasonal cycles to allow us to tease apart long-term ecosystem change from seasonal effects, which might be challenging as the latter are much more pronounced at high latitudes. Although many different geographical sites have been used for sampling and monitoring polar microbial communities, two stand out: The Fram Observatory in the Arctic and long-term observation of plankton at the Western Antarctic Peninsula (Lin et al., 2021; Wietz et al., 2021). The Fram observatory is based on autonomous samplers, which can sample at discrete time intervals to cover a complete seasonal cycle. Preliminary results covering 12 months including the dark winter period have revealed that the strong seasonal dynamics including the variable sea-ice extent are the main drivers for the succession in changes of microbial biodiversity based on 16 and 18S rDNA data and subsequent analysis (e.g., alpha diversity). Furthermore, the persistent sea-ice cover appears to reduce the seasonality effect on changes in microbial biodiversity (Wietz et al., 2021). As the autonomous samplers can be redeployed, they provide a platform for long-term monitoring of microbial communities in the Arctic Ocean. A similar monitoring system is not yet, to our knowledge, available for the Southern Ocean, although there is a ship-based multi-year monitoring programme at the Western Antarctic Peninsula (Lin et al., 2021) based on similar methods (e.g., 16/18S rDNA). Modeling has also been shown that sea ice plays a significant role in structuring microbial communities and their productivity (Lin et al., 2021). However, increasing temperatures due to warming in this region are likely to result in declining microbial diversity (Tonelli et al., 2021).

Both of these monitoring programs have provided novel and highly needed insights into how the environmental conditions of the changing polar oceans shape their microbial inventory. To further understand how biotic and abiotic forces determine the abundance and distribution of microbial taxa in a polar ocean, future studies need to provide quantitative insights into how microbial traits (e.g., freeze-thaw resistance) underpin species interactions and how they are associated with habitat characteristics (e.g., sea-ice thickness, oceanic fronts, temperature gradients, snow-cover thickness). These biotic and abiotic forces likely encompass speciation, dispersal, and biotic interactions in a complex and highly dynamic polar environment.

The responses of microbial taxa to different habitat characteristics (environmental filters) and biotic interactions (biotic filters) vary depending on taxa-specific traits including their ability to acclimate, adapt, and their level of competitiveness (Ovaskainen et al., 2017). Thus, these traits will determine which taxa colonize habitats and dominate in seasonal successions (phenology) and therefore contribute to the community assembly process. To address this fundamental question, it is instrumental to apply explanatory models which can integrate data generated for quantitative insights into how microbial communities including their networks relate to environmental conditions and larger-scale ecosystem processes (e.g., seasonality of microbial diversity linked with habitat transformations such as freezing and melting of sea ice) (Ovaskainen et al., 2017; Tikhonov et al., 2020). One approach to tackle this challenge is hierarchical joint species/taxa distribution modeling (HJSDM) (Tikhonov et al., 2020). The heart of this approach is integrating species co-occurrence in co-variation with environmental conditions, and phylogeny of species and traits. Data required for this approach can be extracted from metagenomes, metatranscriptomes, and metaproteomes. For instance, MAGs will provide a genomic catalog (e.g., Cao et al., 2020; Royo-Llonch et al., 2021) of co-occurring microbial taxa in a phylogenetic but also spatio-temporal context if based on long-term sampling. Their genes, transcripts, and proteins provide trait information for revealing co-variation with environmental conditions. If sampling covers larger geographic regions, as done for the West Antarctic Peninsula, or the central Arctic Ocean as part of MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) (Wake, 2019), we will be able to delineate processes influencing the biodiversity of microbial communities at different spatio-temporal scales (Figure 3). Generally, a spatio-temporal context is required for separating drivers of seasonal differences vs climate-driven differences in microbial community composition and therefore to make robust predictions as to how warming impacts polar ecosystems.