Tushar Kaushik
Vaishnavi Dixit
Rahul Mohan- 1Biodiversity and Palaeobiology Group, Agharkar Research Institute, Pune, Maharashtra, India
- 2Savitribai Phule Pune University, Pune, Maharashtra, India
- 3National Center for Polar and Ocean Research (MoES, Govt. of India), Vasco de Gama, Goa, India
Neogloboquadrina pachyderma is the dominant planktonic foraminiferal species in polar oceans and a major contributor to biogenic carbonate production in the Southern Ocean. Its calcite test serves as a key geochemical archive for reconstructing past ocean conditions through stable isotope and trace element proxies. As polar marine ecosystems undergo rapid environmental change, understanding the ecological associations and trophic dependencies of N. pachyderma within surrounding pelagic communities is increasingly important, particularly in extreme Antarctic environments. Here, we present the first molecular characterization of pelagic eukaryotic assemblages associated with individual N. pachyderma (genetic Type IV) specimens collected from Prydz Bay, East Antarctica, using a single-cell 18S rRNA gene metabarcoding approach. High-throughput sequencing revealed that the N. pachyderma eukaryome was dominated by dinoflagellates (Dinophyceae), diatoms (Bacillariophyceae), radiolarians, Syndiniales, and metazoans. In contrast, ambient seawater communities were enriched in cercozoans, followed by dinoflagellates, radiolarians, and ciliates. The distinct composition of eukaryotic assemblages associated with N. pachyderma relative to the surrounding seawater suggests selective incorporation of a subset of the local pelagic eukaryotic pool, potentially reflecting trophic interactions or transient associations with aggregate-associated prey. These results provide preliminary molecular insights into the trophic linkages or pelagic eukaryotic associations of Antarctic N. pachyderma Type IV, in Prydz Bay, East Antarctica, and establish an important baseline for future investigations. Our findings highlight the role of pelagic eukaryotes in shaping foraminiferal microbial interactions and underscore the relevance of such associations for interpreting palaeoceanographic proxies in a rapidly changing Southern Ocean.
1 Introduction
The planktonic foraminifer Neogloboquadrina pachyderma is among the most abundant and ecologically significant protists in the global ocean (Morard et al., 2024), with a strong dominance in high-latitude planktonic communities of both hemispheres (Tolderlund, 1971). As a major marine calcifier in polar environments, N. pachyderma contributes substantially to pelagic biogenic carbonate production (Rembauville et al., 2016) and plays a crucial role in regulating the export of inorganic carbon to the deep ocean (Tell et al., 2022). Following its death, the calcite test of N. pachyderma sinks to the seafloor, forming an invaluable geochemical archive used to reconstruct past environmental and oceanographic conditions.
Historically, the coiling direction of N. pachyderma, a phenotypic feature sensitive to environmental fluctuations, was widely employed as a palaeoceanographic proxy, with shifts between sinistral and dextral forms interpreted as responses to glacial–interglacial temperature variations. However, molecular investigations have since redefined this paradigm, demonstrating that the sinistral and dextral morphotypes represent distinct genetic species: N. pachyderma and N. incompta, respectively, with only a minor proportion (<3%) of aberrantly coiled individuals present in both forms (Darling et al., 2006, Darling et al., 2023). Subsequent genetic studies have revealed remarkable diversity within N. pachyderma, encompassing at least eight genetic types distributed across the world’s oceans (Darling and Wade, 2008). Recent metabarcoding analyses have uncovered additional cryptic lineages (Morard et al., 2024). Among these, Types IV, III, and II are characteristic of polar, subpolar, and transitional upwelling regions of the Southern Ocean, respectively, with diversification patterns likely shaped by Quaternary climatic oscillations (Darling et al., 2004).
The sinistral N. pachyderma (sensu Darling et al., 2006) dominates modern Southern Ocean assemblages (Darling et al., 2004, Darling et al., 2007), particularly south of the Subantarctic Front (SAF), where during austral summer (December–February) the sea surface temperature (SST) ranges between 2–8°C immediately south of the SAF, decreasing to near-freezing (−1.8 — 1°C) further south near the Antarctic Polar Front or sea ice edge. Warmer pockets (up to 6–10°C) can occur in northern parts of the zone due to eddy activity, whereas, during austral winter (June–August) the sea surface temperature (SST) ranges 0–4°C, with minimal warming in surface layers due to strong winds, deep convection, and limited solar heating (Holliday and Read, 1998). Much of the area approaches freezing point, especially south of the Polar Front during austral winters. The annual normalized sea surface salinity ranged between 33.8–34 ‰ (Meilland et al., 2016; Morard et al., 2024; Holliday and Read, 1998). Antarctic populations exhibit a unique overwintering strategy, becoming incorporated into sea ice during its formation (Dieckmann et al., 1991). As a persistent component of both modern and Quaternary polar assemblages, N. pachyderma serves as an indispensable proxy for reconstructing Antarctic paleoenvironments (Brandon et al., 2022).
In recent decades, the Southern Ocean has undergone pronounced physical and ecological transformations. The Antarctic Circumpolar Current (ACC) is migrating southward, accompanied by progressive ocean warming and freshening (Swart et al., 2018; Chapman et al., 2020). These environmental shifts are expected to alter pelagic eukaryotic community structure and biogeographic patterns (Kaushik et al., 2025), as these communities respond sensitively to temperature, salinity, and sea-ice variability. Given that pelagic eukaryotes constitute a principal food source for N. pachyderma (Schiebel and Hemleben, 2017; Greco et al., 2021; Bird et al., 2025), projected changes in planktonic community composition under ongoing climate change pose additional challenges to the survival and calcification dynamics of this species. Understanding the trophic dependencies and ecological associations of Antarctic N. pachyderma with its surrounding pelagic eukaryotic communities is therefore essential for contextualizing its ecological role and for refining interpretations for geochemical proxy signals under changing Southern Ocean conditions.
While extensive work has been carried out on the biology, ecological niche, trophic interactions, and environmental responses of N. pachyderma (Type I) in the North Atlantic and Arctic regions (Bertlich et al., 2021; Greco et al., 2019, Greco et al., 2021, Greco et al., 2023; Westgård et al., 2023; Meilland et al., 2023; Bird et al., 2025), comparable studies from the Southern Ocean remain scarce. In particular, molecular investigations of eukaryotic assemblages associated with Antarctic N. pachyderma are limited, leaving unresolved questions about whether Antarctic populations harbor similar or distinct microbial consortia compared to Arctic calcifiers. To predict future ecological and biogeochemical responses of Antarctic N. pachyderma, understanding its trophic dependencies and/or interactions with ambient pelagic eukaryotic communities is essential.
In this study, we genotyped Neogloboquadrina pachyderma collected from Prydz Bay, East Antarctica, and employed a high-throughput single-cell 18S rRNA metabarcoding approach to characterize the pelagic eukaryotic community association with the N. pachyderma, hereafter referred to as the eukaryotic interactome, which remains unexplored for Antarctic populations. This initial study provides preliminary insights into the potential ecological associations and trophic strategies of this key Antarctic calcifier within a polar marine ecosystem, establishing a baseline for future, more comprehensive investigations.
2 Material and methods
2.1 Sampling location
Prydz Bay is a prominent embayment along the East Antarctic coastline, located in the Indian Sector of the Southern Ocean (67°45′–69°30′S, 69°–80°E), encompassing an area of approximately 8.0 × 104 km² (Zhao et al., 2014). It represents the third-largest embayment in Antarctica and receives discharge from nearly 16% of the East Antarctic Ice Sheet through the Lambert Glacier and Amery Ice Shelf system (Hodgson et al., 2016). The bay remains covered by sea ice for most of the year, opening for only a few summer months, during which sampling for this study was conducted. Water depths within Prydz Bay range from 500 to 700 m, bordered by the Four Ladies Bank to the northeast and the Frame Bank to the northwest, both characterized by shallow depths of <200 m. To the south, the bay terminates at the Amery Ice Shelf. These shallow banks partially restrict deep-water exchange between Prydz Bay and the adjacent open ocean (Smith and Tréguer, 1994). Four principal water masses shape the hydrography of Prydz Bay: Summer Surface Water (SSW; –1.8°C < T < 2.1°C, 20.6 < S < 34.2), Dense Shelf Water (DSW; –1.95°C < T < –1.85°C, S ≥ 34.5), Ice Shelf Water (ISW; T < –1.95°C), and modified Circumpolar Deep Water (mCDW; –1.85°C < T < –0.5°C) (Wong et al., 1999; Herraiz-Borreguero et al., 2015). Circulation within the bay is dominated by a persistent clockwise gyre that extends across the entire embayment in front of the Amery Ice Shelf (Passchier et al., 2003). Its extensive continental shelf, variable bathymetry, and dynamic circulation create a complex hydrographic regime that shapes local pelagic productivity. Seasonal variations in sea-ice extent, temperature, solar radiation, and zooplankton grazing drive pronounced temporal and spatial variability in primary production and chlorophyll a concentrations (Zhao et al., 2014). These conditions foster a distinct pelagic ecosystem potentially supporting unique microbial and protistan assemblages that influence N. pachyderma ecology. The Indian Antarctic research station Bharati (69.4077°S, 76.1872°E) is situated along the western shore of Prydz Bay, providing a logistical base for sampling operations.
2.2 Sampling methodology
Sampling was carried out aboard the R/V S.A. Agulhas I during the 11th Indian Scientific Expedition to the Southern Ocean (ISESO-11), conducted between January and March 2020. Hydrographic parameters, including temperature (°C), salinity (‰), and dissolved oxygen (µmol kg-1), were measured using a conductivity–temperature–depth (CTD) profiler (Sea-Bird Scientific, USA). Sea surface temperature (SST) and salinity were additionally recorded in situ using a handheld multiparameter probe (Orion A-Star, Thermo Fisher Scientific, USA) for validation of CTD data. Nutrient concentrations, specifically nitrite (NO2-) and phosphate (PO4³-), were quantified onboard using a portable spectrophotometer (HACH DR1900, Hach, USA), following standard colorimetric protocols (Figure 1; Supplementary Table S1). Specimens of Neogloboquadrina pachyderma and corresponding seawater samples were collected during the austral summer (February 2020) from two stations in Prydz Bay, East Antarctica: Station 7 (67.00°S, 76.50°E) and Station 5 (67.00°S, 80.00°E) (Figure 1).
Figure 1. Study area and surface oceanographic conditions in the Indian section of Southern Ocean, Prydz Bay, East Antarctica. Spatial distribution of hydrographic and biogeochemical parameters at the sampling stations in the study area. Panels (A) Bathymetry (m), (B) sea surface temperature (°C), (C) salinity (psu), (D) dissolved oxygen (DO; µmol kg-1), (E) phosphate (µmol kg-1), and (F) nitrate (µmol kg-1). Red circles indicate the locations of Station 5 (Stn 5) and Station 7 (Stn 7). Black contour lines in panels E and F represent selected nutrient isopleths highlighting spatial gradients. Color scales represent the spatial distribution of each parameter, with contour lines highlighting gradients. Grey areas indicate land and ice-covered regions.
2.3 Water sampling
Surface seawater samples were collected using a 10 L Niskin bottle mounted on the CTD carousel. From each cast, a 1 L aliquot was transferred into pre-cleaned polypropylene bottles and filtered under gentle vacuum pressure (<20 cm Hg) through sterile 0.22 µm polycarbonate membranes (Millipore, Merck, USA) to capture eukaryotic protistan fractions. Prior to each filtration, all tubing and filtration units were sterilized by sequential rinsing with 10% bleach solution and double-distilled water for 2 minutes using the vacuum pump to prevent contamination. Following filtration, membranes were transferred into sterile 5 mL microcentrifuge tubes containing 2.5 mL of RNAlater® stabilization solution (Thermo Fisher Scientific, USA). Samples were held at 4°C for approximately 20 minutes to ensure complete permeation of the preservative and subsequently cryopreserved at –20°C until further processing. RNAlater® is a non-toxic, aqueous reagent that preserves cellular and nucleic acid integrity, thereby eliminating the need for immediate downstream processing or flash-freezing in liquid nitrogen.
2.4 Collection of Neogloboquadrina pachyderma
Due to the relatively shallow depths at the sampling sites (580 m below sea level at Station 5 and 332 m at Station 7; Supplementary Table S1), living Neogloboquadrina pachyderma specimens were collected exclusively from the surface layer (0–10 m). Sampling was conducted using a Bongo net (Hydro-Bios, Kiel, Germany) equipped with a 100 µm mesh size. The net was deployed from the research vessel via a winch system and towed horizontally between 0 and 10 m depth at a speed of 1–2 nautical miles per hour for approximately 10–15 minutes. Upon retrieval, living N. pachyderma individuals were immediately sorted onboard within one hour of collection, following established taxonomic criteria (Darling et al., 2006; Weiner et al., 2016; Brummer and Kučera, 2022). Specimens were then mounted on micropaleontological slides and cryopreserved at –20°C for the duration of the expedition. In the laboratory, living and dead N. pachyderma individuals were identified under a stereo-zoom microscope equipped with a digital imaging system (SMZ1270, Nikon, Japan). Specimens exhibiting yellowish cytoplasmic coloration within all but the terminal chambers of the final whorl were classified as living, whereas colorless tests were categorized as dead (Figure 2).
Figure 2. Light microscopy image of individuals (A–I) of Neogloboquadrina pachyderma genotype IV collected from Prydz Bay, East Antarctica. Scale bar: 100 μm.
2.5 DNA extraction, amplification, and genotyping
Prior to DNA extraction, each Neogloboquadrina pachyderma specimen was gently rinsed two to three times with 0.2 µm-filtered surface seawater and transferred into sterile microcentrifuge tubes. Following imaging and morpho-taxonomic identification, each specimen was assigned a unique identification number (ID), which was consistently used throughout DNA extraction, amplification, barcoding, and downstream metabarcoding analyses. DNA was extracted from ten individual N. pachyderma specimens by gently homogenizing each in 50 µL of sterile guanidinium isothiocyanate (GITC) lysis buffer (Pawlowski, 2000; Weiner et al., 2016). Blank lysis controls without specimens were processed in parallel to monitor contamination. For genotyping, a ~600 bp fragment corresponding to the 3′ partial region of the small subunit (SSU) rRNA gene was amplified using planktonic foraminifera-specific primers S15rf (5′-GTGCATGGCCGTTCTTAGTTC-3′) and S19f (5′-CCCGTACRAGGCATTCCTAG-3′) (Weiner et al., 2016). PCR reactions followed a standard thermal cycling protocol: initial denaturation at 95°C for 5 min, followed by 42 cycles of denaturation (95°C for 30 s), annealing (53°C for 60 s), and extension (72°C for 90 s), with a final extension at 72°C for 10 min. PCR products were purified using the FavorPrep PCR Purification Kit (Favorgen, Taiwan) according to the manufacturer’s instructions and subjected to Sanger sequencing (Barcode Bioscience, Bengaluru, India). The resulting sequences were submitted to GenBank (NCBI) under accession numbers PQ304529–PQ304533. The obtained partial 18S rRNA gene sequences of Neogloboquadrina pachyderma were manually curated and aligned using BioEdit (Hall, 1999). Reference sequences representing different N. pachyderma genotypes were retrieved from GenBank (NCBI) for comparative analyses (Figure 3; Supplementary Table S1). Multiple sequence alignments were generated using the MUSCLE algorithm (Edgar, 2004) with default parameters implemented in MEGA version 7 (Kumar et al., 2016). Phylogenetic relationships were reconstructed using the Maximum Likelihood (ML) approach with 100 bootstrap replicates to assess clade support (Figure 3), as implemented in MEGA version 7 (Kumar et al., 2016).
Figure 3. Maximum likelihood phylogenetic tree constructed from the 3′ partial SSU rRNA gene fragment, illustrating the evolutionary relationships among previously described Neogloboquadrina pachyderma genotypes and the sequences obtained in this study (shown in bold). The tree is rooted with Globorotalia inflata following Darling et al. (2007). Bootstrap support values are shown at major nodes. The scale bar denotes the number of substitutions per site.
2.6 18S rDNA metabarcoding
For water samples, 0.22 µm polycarbonate filter membranes were thawed on ice and centrifuged at 5,000 rpm for 1 min to remove excess RNAlater®. Filters were washed 2–3 times with sterile, filtered, salt-adjusted phosphate-buffered saline (PBS) before being transferred to sterile centrifuge tubes. DNA extraction was performed using the DNeasy PowerWater Kit (Qiagen, Germany) according to the manufacturer’s protocol, with a clean filter processed in parallel as a negative control. DNA concentrations from both foraminiferal and water samples were quantified using a Qubit fluorometer (Life Technologies, USA). The 18S rDNA metabarcoding of all specimens of N. pachyderma (NP2–NP11) and water samples (WS10 and WS2) targeted the hypervariable V4 region of the 18S rRNA gene using universal eukaryote primers TAReuk454FWD1 (5′-CCAGCAGCCGCGGTAATTCC-3′) and TAReukREV3 (5′-ACTTTCGTTCTTGATCGA-3′) (Stoeck et al., 2010), which offer high taxonomic resolution across the eukaryotic domain (Del Campo et al., 2019; Greco et al., 2021). PCR amplification was conducted in triplicate 25 µL reactions containing the KAPA HiFi HotStart Kit (KAPA Biosystems, USA), 0.5 µM of each primer, and 10 ng of template DNA. Thermal cycling included 35 cycles of denaturation (96°C for 1 min), annealing (57°C for 30 s), and extension (72°C for 1 min), followed by a final extension at 72°C for 10 min. A secondary indexing PCR (round 2) was performed using 1 µL of the first-round amplicon to attach Illumina barcoded adaptors (Nextera XT v2 Index Kit, Illumina, USA). Amplicons were verified by electrophoresis on 1% agarose gels stained with GelRed and pooled before sequencing. All reactions were run in triplicate, and purified amplicons were quantified with a Qubit fluorometer. Equimolar libraries were sequenced on an Illumina HiSeq platform (2 × 150 bp paired-end reads) at Medgenome Labs, Bengaluru, India. All laboratory work was performed under strict contamination control protocols: glassware and plasticware were autoclaved and UV-sterilized, molecular-grade reagents were used exclusively, and blank extractions and PCRs were included as negative controls.
2.7 Bioinformatics
Illumina sequencing produced 591,870 high-quality paired-end reads. Raw sequences were processed using QIIME 2 (version 2024.2; Bolyen et al., 2019). Low-quality bases (Phred score < 33) were filtered, and adapter sequences were removed with Cutadapt (version 4.4; Martin, 2011). The remaining high-quality reads were denoised, merged, and dereplicated using the DADA2 plugin (Callahan et al., 2016) with truncation parameters set to –p-trunc-len-f 123 and -p-trunc-len-r 91. Singleton and doubleton reads were removed, and chimeric sequences were filtered using the QIIME 2 feature-table plugin. Amplicon sequence variants (ASVs) were taxonomically assigned using the PR2 database (Guillou et al., 2012) and a Naïve Bayes classifier implemented in scikit-learn (Pedregosa et al., 2011). The sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1119288 for N. pachyderma specimens NP2–NP7 and WS10, and in the DNA Data Bank of Japan (DDBJ) Sequence Read Archive under accession PRJDB38012 for N. pachyderma specimens NP8–NP11 and WS2.
2.8 Data processing
ASVs that could not be taxonomically assigned beyond the phylum or class level using the PR2 reference database (Guillou et al., 2012) were considered unclassified at higher taxonomic ranks and were subjected to manual validation using NCBI BLASTn against the nucleotide (nt) database. ASVs were excluded from downstream analyses when BLAST results did not support a single, consistent taxonomic assignment, defined as the presence of multiple equally high-scoring hits (≥99% sequence identity with comparable query coverage) mapping to different taxa, or when taxonomic annotations among the top hits were inconsistent.
Alpha and beta diversity analyses were conducted in R (version 4.3.2; R Core Team, 2024) using the vegan package (Oksanen et al., 2024). Alpha diversity was quantified for each sample using standard ecological indices, including observed ASV richness, Shannon diversity, Simpson diversity, and Pielou’s evenness, calculated from the untransformed ASV abundance matrix (Table 1). Furthermore, to assess differences in eukaryotic community composition between Neogloboquadrina pachyderma and filtered seawater, beta diversity analysis was performed using Bray–Curtis dissimilarity, which was conducted in R (v4.3.2; R Core Team, 2024) using the vegan package (Figure 4). Community-level differences between Neogloboquadrina pachyderma and seawater samples were tested using PERMANOVA (func: adonis2) based on Bray–Curtis dissimilarity with 999 permutations, reporting F-statistics and variance explained (R²). Homogeneity of multivariate dispersion was tested using the function betadisper permutations to determine whether differences between groups were driven by variability within groups rather than differences in community composition (Table 1).
Table 1. Alpha diversity and community composition metrics comparing Neogloboquadrina pachyderma–associated microbial communities (NP2–NP11) and seawater samples (WS2 and WS10).
Figure 4. Principal coordinates analysis (PCoA) of Bray–Curtis dissimilarity showing differences in eukaryotic community composition between Neogloboquadrina pachyderma–associated (NP) samples and seawater (WS) samples with total variance of 57.43%.
3 Results
3.1 Hydrography and nutrient characteristics
Both sea surface water and N. pachyderma were collected from the upper ~10 m of the water column from the two Antarctic stations, particularly further south of the Polar Front (PF) in the Prydz Bay, East Antarctica. Station 5 (67°00′ S, 80°00′ E) was sampled at a depth of 580 m below sea level and included six N. pachyderma specimens (NP2–NP7) and one filtered seawater sample (WS10). In situ conditions at Station 5 were cold and saline, with a temperature of −0.36°C and a salinity of 33.67‰. Dissolved oxygen concentrations averaged 332 µmol kg-1, pH was 7.8, and nutrient concentrations were 1.5 µmol kg-1 phosphate and 20 µmol kg-1 nitrate (Supplementary Table 1). Station 7 (67°32.3873′ S, 76°50.9404′ E) was sampled at a shallower depth of 332 m and comprised four N. pachyderma specimens (NP8–NP11) and one filtered seawater sample (WS2). Environmental conditions at Station 7 showed slightly warmer and less saline waters, with a temperature of −0.30°C and a salinity of 32.67‰. Dissolved oxygen concentrations were higher (350 µmol kg-1), pH was slightly elevated (8.02), and nutrient levels included 1.44 µmol kg-1 phosphate and 24.5 µmol kg-1 nitrate (Supplementary Table 1). The hydrographic parameters recorded in Prydz Bay, East Antarctica, during the sampling period reflect the typical characteristics of the Antarctic Surface Water (AASW; –1.5 < T < 2°C; 32.5 < S < 34.2 psu) (Kostianoy et al., 2004). The measured macronutrient concentrations nitrite (NO2-) and phosphate (PO4³-) are consistent with the nutrient-enriched surface waters commonly observed in polar regions during the austral summer (Kaushik et al., 2025).
3.2 Neogloboquadrina pachyderma 18S rDNA genotyping
Phylogenetic analysis of Neogloboquadrina pachyderma sequences from Prydz Bay (East Antarctica) revealed a well-supported clustering with previously described Type IV lineages (Figure 3). All Prydz Bay sequences (PQ304529–PQ304533) formed a coherent clade together with reference Type IV sequences from the South Atlantic, indicating close genetic affinity across Southern Ocean regions. This Type IV clade was strongly supported by high bootstrap values (100), confirming its phylogenetic distinctiveness from other regional genotypes. In contrast, N. pachyderma sequences representing North Atlantic Type I, North Pacific Type VII, Arabian Sea Type VIII, and multiple South Atlantic Types (II, III, V, and VI) formed clearly separated clades, consistent with previously reported basin-specific genetic structuring of this species (Darling et al., 2006). The Prydz Bay sequences did not cluster with Northern Hemisphere (Type I) or lower-latitude genotypes (Types II, III, V, and VI), highlighting the regional specificity of Antarctic N. pachyderma Type IV populations. The placement of Prydz Bay specimens within the Type IV lineage supports earlier observations that this genotype is characteristic of Southern Ocean and Antarctic waters and suggests a broad circum-Antarctic distribution with limited gene flow to non-polar populations (Darling et al., 2004; Darling and Wade, 2008).
3.3 Eukaryotic diversity revealed by 18S rDNA metabarcoding
After quality filtering, a total of 341 unique amplicon sequence variants (ASVs) were retained for downstream statistical analyses (Supplementary Table S2). In seawater samples, Cercozoa dominated the eukaryotic community, accounting for 41.97% of total ASVs, followed by Dinophyceae (25.23%), Radiolaria (14.23%), Ciliophora (6.01%), and Arthropoda (3.52%) (Figures 5, 6; Supplementary Table S2). Among these, Massisteria marina, Prorocentrum sp., Gyrodinium rubrum, Acanthoplegma sp., and Syndiniales Group I and II were the most abundant taxa. Within Bacillariophyta, Pseudo-nitzschiasp., Fragilariopsis sp., and Asteronellopsidaceae sp. represented the dominant diatoms in the water column (Figure 7). Conversely, the eukaryotic interactome associated with N. pachyderma Type IV was primarily composed of Dinophyceae, followed by Bacillariophyta, Radiolaria, and Syndiniales (Figures 5, 6). Comparative analyses of dominant taxonomic groups Dinophyceae, Syndiniales, and Bacillariophyta revealed that Gymnodiniales and Dino Group I were markedly enriched in foraminiferal samples; in contrast, their relative abundance was lower in the ambient seawater. It is essential to note that single-cell 18S rDNA metabarcoding reflects the taxonomic composition of DNA associated with a specimen. However, it does not allow for discrimination between active ingestion, transient contact with aggregates, surface-associated cells, or environmental DNA. Similar caveats have been discussed in comparable metabarcoding studies on planktonic foraminifera (e.g., Greco et al., 2021). Therefore, our interpretations are based on relative abundance patterns and contextual ecological information rather than definitive trophic assignments.
Figure 5. Relative abundance (%) of major taxonomic groups across all samples N. pachyderma (samples NP2–NP11) and surface seawater samples (WS10 and WS2) from Prydz Bay. Stacked bar plots illustrate the proportional contribution of dominant groups, including Arthropoda, Bacillariophyceae, Dinophyceae, Radiolaria, Ciliophora, Cercozoa, Syndiniales, Bigyra, and other minor taxa. NP: N. pachyderma and WS: Water Sample.
Figure 6. Treemaps illustrating the averaged eukaryotic taxonomic composition at higher (class-level) ranks in (A) N. pachyderma Type IV specimens and (B) filter membrane samples representing the ambient sea-surface water. Colors correspond to taxonomic groups; groups with relative frequency <1% are grouped as “Others.”.
Figure 7. Comparative taxonomic composition of major phytoplankton and parasitic dinoflagellate groups associated with Neogloboquadrina pachyderma Type IV and the surrounding water sample. Pie charts depict the relative contributions of taxa within Dinophyceae, Bacillariophyta, and Syndiniales. Left panels represent assemblages associated with N. pachyderma Type IV, while right panels correspond to the ambient water sample.
3.4 Statistical analyses
Alpha diversity metrics revealed contrasting patterns between Neogloboquadrina pachyderma (NP2—NP7) and seawater (WS10 and WS2) eukaryotic communities (Table 1). Neogloboquadrina pachyderma (NP2—NP7) samples showed comparatively higher evenness and Simpson diversity, whereas seawater samples (WS10 and WS2) exhibited higher ASV richness. Shannon diversity values were broadly comparable between the two sample types, indicating differences in community structure rather than simple richness effects. Beta diversity analysis based on Bray–Curtis dissimilarity demonstrated a clear separation between Neogloboquadrina pachyderma (NP2—NP7) associated and seawater communities (WS10 and WS2) in ordination space (Figure 4; Table 1). PERMANOVA confirmed that sample type explained a significant proportion of the variance in community composition (F = 4.39, R² = 0.31, p = 0.017), indicating that the associated interactomes of Neogloboquadrina pachyderma (NP2—NP7) are compositionally distinct from surrounding seawater assemblages. Tests for homogeneity of multivariate dispersion revealed significant differences in within-group dispersion between Neogloboquadrina pachyderma (NP2—NP7) associated and seawater samples (WS10 and WS2) (PERMDISP, p = 0.001). Together, these results suggest that NP-associated eukaryotic communities are both compositionally distinct and more structured relative to the ambient pelagic community.
4 Discussion
Two morphologically distinct coiling forms of Neogloboquadrina pachyderma, sinistral and dextral, have long been recognized in the world’s oceans (Kennett and Srinivasan, 1983). Cifelli (1961) first proposed the name N. incompta for the dextral morphotype, and subsequent morpho-genetic studies confirmed that coiling direction corresponds to two deeply divergent genetic lineages (Darling et al., 2006). To date, eight genetic types of N. pachyderma have been documented globally (Darling et al., 2007, Darling et al., 2017). Type I predominates in the Arctic and North Atlantic (Darling et al., 2004, Darling et al., 2007; Bird et al., 2025), whereas Types II, III, and IV characterize planktonic populations of the Southern Ocean (Darling et al., 2004, Darling et al., 2007). Recent surveys from the Indian Sector of the Southern Ocean (ISSO) confirm the presence of Type IV (André et al., 2018), with Types I and IV forming the basal polar clades in phylogenetic analyses (André et al., 2014).
Our results show that all N. pachyderma individuals analyzed from Prydz Bay, East Antarctica, clustered within genotype Type IV (Figures 2, 3), indicating that this lineage was dominant in the sampled specimens at the time of collection. The presence of Type IV in subzero waters and its frequent incorporation into sea-ice brine channels corroborate earlier observations that Antarctic populations overwinter within sea ice (Dieckmann et al., 1991). This overwintering strategy likely emerged during late Quaternary glacial–interglacial cycles (1.1–0.5 Ma) and may have contributed to the evolutionary divergence of polar genotypes (Darling et al., 2004, Darling et al., 2007). Understanding the trophic ecology and pelagic interactions of Type IV is therefore essential for refining species concepts and calibrating paleoceanographic proxies that rely on this taxon. Despite its ecological importance, the trophic-level interactions of Antarctic N. pachyderma Type IV and its relationship with local pelagic eukaryotic communities have remained unknown.
Our study is the first attempt to provide preliminary insights into the N. pachyderma eukaryotic interactome from Prydz Bay (Figures 5-7), by employing a high-throughput single-cell 18S rRNA metabarcoding approach to characterize the trophic level associations between the pelagic eukaryotic community and N. pachyderma. The eukaryome of N. pachyderma genetic type IV was dominated by Dinophyceae (43.32%), followed by Bacillariophyta (28.54%), whereas the ambient seawater community was dominated by Cercozoa (41.97%), Dinophyceae (25.23%), and Radiolaria (Figure 5). These stark differences indicate that Type IV does not passively mirror the surrounding community but instead exhibits selective or opportunistic associations. This distinction is noteworthy when our results are compared to those of Arctic N. pachyderma Type I, which exhibits a strong dietary association with diatoms (Greco et al., 2021). In contrast, the Antarctic N. pachyderma Type IV appears more closely linked to dinoflagellates, a characteristic of Antarctic marine ecosystems where dinoflagellates and microbial loop processes play a prominent role (Arrigo et al., 1999; Hörstmann et al., 2021; Kaushik et al., 2025). Although Antarctic Type IV appears more strongly associated with dinoflagellates than the diatom-rich interactomes of Arctic Type I, this contrast may primarily reflect environmental differences, including seasonal timing, sea-ice conditions, and regional food-web structure, rather than intrinsic genetic differences (Greco et al., 2021). Currently, the available data cannot distinguish between environmental drivers and genotype-specific feeding behavior in Antarctic and Arctic N. pachyderma, underscoring the need for further studies.
Our results are congruent with the previous studies that have shown dinoflagellates and cercozoans to be the dominant protists in post-winter and pre-bloom assemblages in the polar Southern Ocean (Hörstmann et al., 2021; Kaushik et al., 2025). Our results showed that the heterotrophic and mixotrophic dinoflagellates, including Gymnodinium, Gyrodinium, and Protoperidinium were particularly abundant in the N. pachyderma Type IV eukaryotic interactome. These taxa are well known for their widespread distribution across the Southern Ocean (Stoecker et al., 1995; Christaki et al., 2015; Torstensson et al., 2015; Swalethorp et al., 2019) and frequently dominate pre-bloom protistan communities. In the present study, diatoms were relatively scarce in the seawater samples; however, they were one of the most abundant groups in the N. pachyderma eukaryotic interactome (Figures 5-7). During the sampling period, large heterotrophic dinoflagellates and tintinnid ciliates intensively graze phytoplankton, especially Fragilariopsis and Gyrodinium (Safi et al., 2007; Wilson et al., 2015), which may explain the low in-water diatom abundance. In contrast, diatom presence in N. pachyderma Type IV suggests aggregate ingestion, possibly linked to sinking aggregates enriched in diatom material.
Syndiniales (MALVs) constituted the third-most abundant eukaryotic group within the N. pachyderma interactome. These parasitoids have been previously documented in N. pachyderma from the Arctic (Greco et al., 2021) and occur widely across Southern Ocean protistan assemblages (De Vargas et al., 2015; Clarke and Deagle, 2020; Kaushik et al., 2025). Their distribution might vary mainly by the abundance of their hosts, primarily radiolarians, ciliates, and others (Bråte et al., 2012), or metazoans (copepods, fish eggs) and release free-living dinospores following host death (Guillou et al., 2008; Clarke et al., 2019). In N. pachyderma, the presence of Syndiniales likely reflects indirect associations via ingestion of infected prey or marine aggregates rather than direct parasitism on the foraminifer, as no morphological evidence of infection was observed in the present investigation. Furthermore, given the small sample size and single-season sampling, our findings represent a baseline snapshot and should be validated with broader temporal and spatial coverage. Expanded sampling aided with microscopy will be necessary to determine whether the patterns observed here are consistent features of N. pachyderma Type IV trophic ecology. Future studies combining metabarcoding with microscopy or host-specific probes could clarify ecological roles.
5 Conclusions
As Southern Ocean ecosystems undergo rapid restructuring driven by climate-induced changes in sea-ice extent, water mass properties, and protistan community composition, understanding the trophic dependencies of N. pachyderma will be critical for assessing its ecological resilience and for refining palaeoceanographic proxies derived from its calcite test. This study provides the first molecular characterization of pelagic eukaryotic assemblages associated with individual Neogloboquadrina pachyderma Type IV specimens from Prydz Bay, East Antarctica, using a single-cell 18S rRNA gene metabarcoding approach. The associated eukaryotic assemblages within N. pachyderma interactome were dominated by dinoflagellates, diatoms, radiolarians, and Syndiniales, whereas ambient seawater communities were comparatively enriched in cercozoans and ciliates. These contrasting patterns suggest that N. pachyderma Type IV incorporates a selective subset of the local pelagic eukaryotic pool, potentially through targeted feeding or ingestion of aggregate-associated prey. Our findings indicate that the trophic ecology of the Antarctic genotype differs markedly from that reported for Arctic populations, with a pronounced association with heterotrophic and mixotrophic dinoflagellates and a notable contribution of diatoms despite their relatively low abundance in the surrounding water column. This pattern suggests a specialized trophic strategy in Antarctic N. pachyderma, adapted to subzero, ice-influenced environments characterized by microbial loop–dominated food webs and episodic phytoplankton blooms.
Although based on a limited number of specimens and a single sampling period, the results presented here represent a preliminary molecular snapshot of potential eukaryotic associations with Antarctic N. pachyderma. This study provides an important baseline for future investigations and demonstrates the applicability of single-cell metabarcoding approaches for resolving foraminiferal trophic interactions. Overall, this work establishes a foundational framework for future studies linking foraminiferal ecology, polar microbial networks, and biogeochemical feedbacks in a warming Antarctic environment.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
Author contributions
TK: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing. VD: Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. RM: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was financially supported by the NCPOR-wide grant (NCPOR/2019/PACER-POP/ES-05) awarded to TK and RM. Additional support was provided by the Anusandhan National Research Foundation (erstwhile SERB) through a Core Research Grant (CRG/2021/002439) to TK and a Junior Research Fellowship (JRF) to VD, as well as by an Agharkar Research Institute in-house project (ARI-017) to TK.
Acknowledgments
The authors thank the Director of the Agharkar Research Institute, Pune, for providing the necessary facilities and encouragement. We are also grateful to the Director of the National Centre for Polar and Ocean Research (NCPOR), Ministry of Earth Sciences (MoES), Goa, for granting permission to participate in the 11th Indian Scientific Expedition to the Southern Ocean. We sincerely acknowledge the captain and crew of R/V Agulhas for their support during sampling.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frpro.2026.1743384/full#supplementary-material
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Keywords: 18S rRNA gene, eukaryotic interactome, Neogloboquadrina pachyderma, planktonic foraminifera, Prydz Bay, single-cell metabarcoding
Citation: Kaushik T, Dixit V and Mohan R (2026) 18S rDNA metabarcoding unveils trophic interactions between Neogloboquadrina pachyderma and the pelagic eukaryotic community in Prydz Bay, East Antarctica. Front. Protistol. 4:1743384. doi: 10.3389/frpro.2026.1743384
Received: 10 November 2025; Accepted: 09 January 2026; Revised: 06 January 2026;
Published: 02 February 2026.
Edited by:
Erik Hanschen, Los Alamos National Laboratory (DOE), United StatesReviewed by:
Hongfei Li, Zhejiang Ocean University, ChinaAna Baricevic, Rudjer Boskovic Institute, Croatia
Copyright © 2026 Kaushik, Dixit and Mohan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tushar Kaushik, dHVzc2hhcmthdXNoaWtAZ21haWwuY29t
†Present addresses: Tushar Kaushik, Biodiversity and Palaeobiology Group, Agharkar Research Institute (DST, Govt. of India), Maharashtra, IndiaVaishnavi Dixit, Biodiversity and Palaeobiology Group, Agharkar Research Institute (DST, Govt. of India), Maharashtra, India