Organic matter degradation by oceanic fungi differs between polar and non-polar waters

Domain: earth_science
Published: 2025-08-15
Editorial outcome: major_revision

Abstract
Recent discoveries have uncovered pelagic fungi as significant contributors to the recycling of organic matter in the ocean. However, their drivers and whether the environmental filtering on the functional role of prokaryotes also applies to pelagic fungi remain unknown. In this study, we employed the metagenomic and metatranscriptomic approaches to explore the fungi mediated organic matter degradation in the sunlit ocean. Samples were collected from the subtropical Atlantic Ocean (non-polar) to the Southern Ocean (polar), and differentiated between small (0.2−3 µm, SF) and large (>3 µm, LF) size fractions, to study niche partitioning in fungal communities and functions. Fungi accounted for 2–5% of eukaryotic genes and transcripts. Fungi contributed over 3% of eukaryotic carbohydrate-active enzymes (CAZymes) transcripts but less than 0.5% of protease transcripts, highlighting their specialized role in carbohydrate degradation. Non-polar and polar regions exhibited distinct fungal community composition and metabolic functions, potentially disrupting the balance of organic matter storage and cycling in these ecologically sensitive regions. Temperature emerged as a key driver of fungal CAZyme activity, revealing sensitivity to ocean warming. Our findings underscore the active role of pelagic fungi in organic matter degradation while revealing the environmental and ecological factors shaping their functional contributions across global oceanic regions.

Introduction
Covering more than two-thirds of the Earth’s surface, the marine ecosystem represents one of the major reservoirs of organic matter 1,2. Consequently, microbial composition and activities can directly influence global biogeochemical cycles and, ultimately, climate change 3,4. While most of the marine microbiology research has focused on the role of prokaryotes and eukaryotic phytoplankton, marine fungi have been essentially neglected in open ocean studies 5–7. However, marine fungi are likely functionally analogous to their terrestrial counterparts, where they degrade high-molecular weight organic substrates of plant detritus, participating actively in the key elemental cycles by releasing CO2 to the atmosphere 5,8–10. Marine fungi exhibit high diversity in various marine habitats, ranging from surface waters to deep-sea sediment 11, suggesting cryptic and abundant fungal communities in marine ecosystems. Indeed, recent evidence showed that fungi are actively involved in multiple biogeochemical cycles, influencing the biogeochemistry in the oceans, primarily by their degradation of carbohydrates, amino acids, and lipid metabolism in the pelagic realm and the deep sediment biosphere 8–10,12–15. These findings indicate that pelagic fungi are active members of the oceanic ‘microbial loop’ together with other heterotrophic microbes, where they might play a distinct role compared to heterotrophic bacteria in the marine carbon cycle 8–10.

The composition and diversity of planktonic communities, from viruses to small metazoans, are influenced by site-scale processes, including resource availability, and regional-scale processes, such as dispersal or invasion history 16. Environmental heterogeneity, characterized by spatial variation in environmental conditions, is known to modulate the relative impact of these processes 17–19. The data generated from the Tara Oceans Expedition has significantly advanced our understanding of marine eukaryotic and fungal diversity and function across a spectrum of size fractions, depths, and oceanic regions at a global scale 8,9,20. Nevertheless, pelagic fungi at both site and regional scales remain understudied. Specifically, our comprehension of how environmental heterogeneity influences fungal communities and their ecological functions within marine ecosystems, particularly concerning their latitudinal biogeographical distribution pattern, remains limited. This contrasts with the recognized important role of environmental heterogeneity in shaping the diversity and function of other microbes (heterotrophic bacteria, phytoplankton, archaea, and viruses), exhibiting pronounced clusterings between non-polar and polar environments 21–24. However, whether this environmental clustering/differentiation observed in the functional role of other pelagic heterotrophic microbes also applies to pelagic fungi remains unknown, precluding a basic understanding of the ecological and biogeochemical role of this enigmatic kingdom in the global ocean.

The size of cells and of particles is crucial for the adaptability of pelagic prokaryotes to changes in their microenvironment and nutrient conditions 8,9,25–28. Typically, planktonic microorganisms are divided into free-living (FL) and particle-attached (PA) communities 29. PA pelagic prokaryotes frequently develop into dense clusters characterized by high extracellular enzyme activity 30. In contrast, FL microorganisms with small genomes are optimized for environments with low substrate availability, and they tend to express membrane transporter genes at high levels 27,31,32. Previous research has indeed indicated that size fractions influence the composition of microbial eukaryote communities 33. Given these differences, it is likely that the abundance and functional diversity of oceanic fungal taxa also differ between various size fractions. However, there remains a lack of comprehensive studies focusing on the fungal component, especially in smaller size fractions.

To address these knowledge gaps, in this study, we used advanced sequencing technologies to enable comprehensive profiling of fungal communities and their molecular functions with high functional and taxonomic resolution. The Southern Ocean, which remains one of the least studied oceanographic regions globally 34, and the poorly understood microbiome structure and functioning of the Southern Ocean, has been suggested to represent critical challenges for ecologists in predicting community structures under future climate change scenarios 34–37. Not surprisingly, metatranscriptomics analyses of pelagic fungi living in the Southern Ocean are extremely limited: the only available studies are two recent publications in which we used the Tara Oceans dataset (which was limited in the Southern Ocean to 4 stations) to study the global contribution of pelagic fungi to organic matter degradation 8,9. In the present study, we utilized high-resolution (25 stations) ocean genomic datasets from samples we collected from the euphotic zone across non-polar and polar regions (Fig. 1A, Supplementary Data 1), combined with environmental data, to explore the linkages between ecosystem structure, functional potential, and carbon degradation dynamics. We investigated both the metabolic potential (metagenomics) and the gene expression (metatranscriptomics) of pelagic fungi since DNA-based results might differ dramatically from the actual transcription and function of microorganisms in the environment 38. We focus specifically on the CAZymes and peptidases, key enzymes involved in the degradation of carbohydrates and proteins, respectively, which are the major macromolecules in organisms including fungi inhabiting marine detrital organic matter such as marine snow 39. Furthermore, to distinguish fungi colonizing particles of varying sizes—which might offer different micro-environments and nutrient availability—we also analyzed two size fractions at each station: a small (0.2–3 µm, SF) and a large (>3 µm, LF) size fractions. Our research reveals significant divergence in both fungal functional diversity and taxonomic composition between polar and non-polar oceanic regions, consistent with previously reported heterotrophic prokaryotes dynamics. Conversely, we observed only minor disparities in their particle microenvironment (0.22–3 µm vs. >3 µm), which is in contrast to heterotrophic prokaryotes 38. Overall, our study fills a critical knowledge gap by enhancing our understanding of the ecological and biogeochemical roles of pelagic fungi in the ocean, bringing it in line with the well-established knowledge of other microbial plankton groups, such as prokaryotes, viruses, and eukaryotic plankton.

Results
Ubiquitous fungal proteinases and CAZymes in the ocean
Our comprehensive sequencing efforts yielded 455.1 Gb of raw data from 42 metagenomes, averaging 10.8 Gb per DNA library (Supplementary Data 2), and 1,959.9 Gb of raw data from 53 metatranscriptomes, averaging 37.0 Gb per sample following poly(A)+ mRNA enrichment using magnetic Oligo(dT) beads (Supplementary Data 3). In the metagenome, fungi constituted 3.13–5.35% of total eukaryotic reads (Fig. 1B), a range consistent with previous metagenomic studies 40. In the metatranscriptome, 39% to 98.4% of transcript reads were assigned to Eukaryota (Fig. S1A), with fungal-affiliated transcripts contributing 3.57–5.0% to total eukaryotic transcripts (Fig. 1B). This consistent detection of fungi across stations, sampling depths, and size fractions suggests their stable and widespread presence in marine ecosystems, regardless of environmental variability. Using mRNA read frequency as a proxy for gene expression, we obtained 16,132,379 non-redundant eukaryotic protein-coding sequences after clustering at 90% similarity using CD-HIT 40. Fungal transcripts showed a higher relative abundance in the large size fraction (>3 µm) compared to the small size fraction across both polar and non-polar regions (Fig. 1B). This pattern suggests a preference of fungi for inhabiting larger environments, which may include both particle-associated aggregates (e.g., marine snow and algal detritus) and larger fungal cells or structures (e.g., hyphae) that are not necessarily particle-bound 5,41–43.

Concerning specific genes involved in the degradation of proteins and carbohydrates, we identified 694 fungal peptidase sequences and 1219 CAZyme sequences in the metagenome (Supplementary Data 4). In contrast, the metatranscriptome revealed a substantially higher occurrences, with 5929 fungal peptidase sequences and 23,644 CAZyme sequences identified after assembly (Supplementary Data 5). This disparity in sequence recovery can be attributed to the methodological differences between the two approaches, particularly the greater sequencing depth and poly(A)+ enrichment in the metatranscriptomic analysis and due to insufficient sequencing coverage of fungal genes in the metagenome, which enhanced the detection of expressed genes. Given that both CAZymes and peptidases can be secreted into the periplasmic space or extracellular environment 26,42, we further investigated their secretory potential by analyzing the presence of signal peptides using SignalP 44. In the metagenome, only 12 fungal peptidases and 74 CAZyme sequences were predicted to contain signal peptides (Supplementary Data 4). In contrast, in the metatranscriptome, we identified 223 fungal peptidases and 1720 CAZyme sequences with secretory potential (Supplementary Data 5). Rarefaction analysis confirmed robust coverage of both CAZyme and peptidase families across our datasets (Fig. S1B). Our findings corroborate previous studies demonstrating the ubiquitous functional roles of marine fungi in oceanic biogeochemical cycles, particularly in the degradation and transformation of nitrogen and carbon compounds 8,10,14,45–47. Furthermore, the richness of both enzyme families was markedly higher in the metatranscriptomic dataset than in the metagenomic dataset (Fig. S1C). Disparities in the genomic potential (genes) and expression profiles (transcripts) of fungi involved in carbohydrate and protein degradation, as has been recently shown for microbial communities from marine and soil ecosystems as well as in human gut 38, confirming the importance of studying fungal transcripts in addition to genes to fully understand their functional potential and expression patterns.

The overall contribution of fungi to both total and secretory peptidases was relatively lower compared to CAZymes (Fig. 1C–F). In polar waters, fungal peptidase genes and their transcripts contributed a significantly higher proportion of total eukaryotic proteases (0.40% to 0.54% for genes and transcripts) compared to non-polar waters (0.15% to 0.23% for genes and transcripts) across both size fractions (Fig. 1C) (p < 0.05). The significantly lower α-diversity and higher β-diversity of peptidases in polar oceans indicate a more uneven distribution of proteases, characterized by the dominance of specific proteinases and a broader diversity of protein substrates (Fig. S1C, D). The higher relative abundance of peptidases in polar compared to non-polar waters likely reflect fungal adaptations to efficiently degrade the substantial biomass and proteins in productive waters 48–50. In contrast, in non-polar ocean, over 3% of fungal transcripts contributed to the total eukaryotic CAZyme transcript pools (Fig. 1E). This contrast to the relatively low fungal contribution (0.77–1.11%) to eukaryotic CAZyme transcripts observed in polar regions (Fig. 1E), suggesting that pelagic fungal communities in non-polar oceans play a disproportionately active role in the degradation of carbohydrates. The decreased diversity of fungal CAZymes in non-polar regions likely reflects the adaptation to specific enzyme targets driven by the availability of complex carbohydrates and warmer temperatures (Fig. S1C, D).

Latitudinal and temperature-driven variations in geographic clustering patterns of marine fungal peptidases and CAZymes
Principal coordinate analysis (PCoA) using Bray-Curtis dissimilarity, based on the relative abundance of genes and transcripts encoding both total and secretory peptidases and CAZymes, revealed a clear separation of samples into two distinct clusters: non-polar and polar regions (Fig. 2A–D), as well as a separation between genes and transcripts (Fig. 2A, C). Beyond regional disparities, fungal CAZymes also exhibited distinct differences between the SF and LF size fractions in the non-polar ocean within the metagenome (Fig. 2C, pairwise PERMANOVA, p < 0.01). Non-metric multidimensional scaling (NMDS) analysis of fungal peptidases and CAZymes along a latitudinal gradient revealed significant geographic clustering, further distinguishing non-polar from polar regions (Fig. S2). The clustering of both total and secretory fungal peptidase genes was associated with latitude as confirmed by the Mantel test (Fig. 2G, Mantel test, p < 0.05). No significant correlations between environmental parameters and fungal CAZyme genes were observed, whereas the functional composition of fungal CAZyme transcripts was linked to temperature (Fig. 2H, Mantel test, p < 0.05). These results are consistent with those reporting that the biogeography of pelagic marine bacteria functional diversity is governed by latitude and temperature 51–54. Our results are also in agreement with a previous DNA-based (metagenomic) study, which suggested temperature as the main driving factor governing global pelagic fungal functional diversity in the ocean, although we now show that this pattern occurs at the expression level based on transcripts 55. The influence of temperature on fungal functional role suggests a critical role of potential global warming on the ecological function of pelagic fungi in the ocean.

Conserved of extracellular enzymatic strategies for protein and carbohydrate degradation in marine fungal communities
We found a pronounced secretory capability of fungal proteinases and CAZymes in non-polar waters, with 8.13–10.38% of total fungal CAZyme transcripts exhibiting secretory activity (Fig. 1F). These proportions are comparable to the secretory activity observed in prokaryotes (<10%) from open oceanic and deep-sea environments 26,42. The secretory capability of total fungal peptides was lower than for CAZymes, and was significantly higher in non-polar (4.13–5.11%) than in polar waters (<1%) (Fig. 1D). These proportions fall within the range reported for prokaryotic peptides (2–9%) 26. These findings highlight the substantial extracellular carbohydrate degradation capacity of marine fungi, consistent with a saprotrophic lifestyle 56–58. Different correlations between total peptidases and CAZymes were found in non-polar (R = 0.91, p < 2.2e–16) versus polar (R = 0.63, p = 5.8e–5) regions, indicating distinct environmental regulations of fungal enzymatic systems across latitudinal gradients (Fig. 2E). Notably, a consistent positive correlations between secretory peptidases and CAZymes was found in both non-polar (R = 0.43, p = 3.7e–08) and polar (R = 0.63, p = 0.011) waters (Fig. 2F), demonstrating the conservation of extracellular enzymatic strategies (in both protein and carbohydrate degradation) in marine fungal communities. This pattern suggests that while intracellular enzyme pools are strongly influenced by local environmental conditions, the extracellular degradation machinery maintains a core functional consistency across ecosystems. Particularly in non-polar warmer oceans, fungi appear to have evolved an efficient extracellular system for complex carbohydrate degradation, operating more independently of intracellular enzyme abundance variations.

Taxonomic shift in pelagic fungi linked to functional changes in peptidases and CAZymes
Ascomycota constituted the predominant fungal phylum in both polar and non-polar marine environments, accounting for 50.8–76.06% of total fungal genes and transcripts (Fig. 1G). These findings align with previous observations that Ascomycota represents the most abundant fungal group in coastal and oceanic waters 59. Chytridiomycota (13.32–15.59%), Mucoromycota (8.59–16.97%), Basidiomycota (7–13.19%), and Zoopagomycota genes and transcripts were also detected but less abundant (Fig. 1G). This taxonomic distribution of all fungal genes and transcripts diverged significantly from the specific phylogenetic profiles of fungal peptidase and CAZyme. Ascomycota, Basidiomycota, and Chytridiomycota were the primary contributors to these functional genes, with Mucoromycota also playing a notable role in CAZyme production (Fig. 3). Both total and secretory genes and transcripts of fungal peptidases and CAZymes exhibited distinct geographic clustering, with clear differentiation between non-polar and polar regions and minor differences between size fractions (Figs. 3A, C, E, G, S3). This suggests that functional roles are phylogenetically constrained, with specific taxa disproportionately driving enzyme production. Fungi may exhibit a preference for inhabiting larger environments (Fig. 1B). However, it is important to note that the large size fraction (>3 µm) likely represents a mixed population, encompassing both particle-attached fungi and larger free-living fungal cells or structures, such as hyphae. This morphological plasticity and ecological adaptability of marine fungi may explain the minimal functional and taxonomic differences observed between the large and small size fractions in our study. Many fungal taxa can transition between small (e.g., yeast cells, spores, or hyphal fragments) and large (e.g., hyphae or particle-associated aggregates) forms, depending on environmental conditions and life cycle stages 7,60. This flexibility likely results in overlapping functional profiles across size fractions, as the same fungal species may contribute to similar ecological processes (e.g., organic matter degradation) regardless of their size-based pelagic categorization 61,62.

Concerning peptidases, Ascomycota (unclassified Ascomycota, Saccharomycetes, Sordariomycetes, and Eurotiomycetes) dominated in the polar waters, representing 55.29–56.61% of total peptidase transcripts, while their contribution decreased substantially in non-polar waters, accounting for only 23.39–26.82% of the total peptidase transcripts (Figs. 3A, S4B). This decline was accompanied by an increase in the relative contribution of Basidiomycota (Agaricomycetes and unclassified Basidiomycota) and Chytridiomycota (Chytridiomycetes, Monoblepharidomycetes and Neocallimastigomycetes) affiliated peptidase transcripts, which collectively represented 57.30% to 62.14% of the total in non-polar waters (Figs. 3A, S4B). The Chytridiomycota-affiliated transcripts encoding secretory peptidases were particularly abundant in non-polar waters, constituting 43.43% to 49.90%, compared to only 19.71% to 22.73% in polar waters (Fig. 3C). Similarly, concerning CAZymes, Ascomycota (including unclassified Ascomycota, Sordariomycetes, Eurotiomycetes, and Lecanoromycetes) and Chytridiomycota (Chytridiomycetes and Monoblepharidomycetes) dominated the gene and transcript pool of fungal CAZymes across all samples, representing 48% to 75% of the metagenome and 36% to 54% of the metatranscriptome (Figs. 3E, S3E, S5A, C). In non-polar regions, Basidiomycota accounted for a large proportion of total CAZyme-related genes (27% in the metagenome) and transcripts (>40% in the metatranscriptome) (Figs. 3E, S3E). The contribution of Basidiomycota to CAZyme-related genes and transcripts declined in polar oceans, dropping to 15.5% in the metagenome and 34% in the metatranscriptome (Figs. 3E, S3E). However, despite this decline, the secretion of CAZyme-genes and transcripts by Basidiomycota exhibited an opposite trend, with higher relative proportions of secretion in polar oceans (Figs. 3G, S3G). Interestingly, our analysis revealed that the contribution of Chytridiomycota (Chytrids) to both total and secretory CAZyme genes and transcripts (26–69%) was significantly higher in polar oceans compared to non-polar regions (16–49%) (Figs. 3, S3). The pronounced dominance of Chytridiomycota-encoded fungal CAZymes in polar ecosystems, particularly in the Southern Ocean, underscores their critical role in cold-adapted marine environments. This observation aligns with their unique physiological adaptations to aquatic ecosystems, such as motility and the ability to parasitize diatoms, which are dominant members of marine phytoplankton communities in polar regions 37,63,64. Chytrids have been documented in various cold environments, such as high-arctic tundra soils 65, soils under persistent snow packs 66, high-mountain lakes 67, and sea ice and Arctic waters 34–36. In freshwater and coastal marine environments, chytrids have been proposed to function as a trophic bridge, or “mycoloop,” between phytoplankton and zooplankton/meiofauna. This occurs through the conversion of carbon from large, inedible algae into smaller, lipid-rich zoospores, which are utilized for reproductive dissemination 68. Together with the widespread presence of Chytridiomycota encoding proteases in both polar and non-polar regions indicates that Chytridiomycota (Chytridiomycetes) are not exclusively specialized for polar ocean ecosystems for degrading both carbohydrates but also possess versatile metabolic capabilities for degrading proteins across diverse marine environments. Their ability to adapt to varying environmental conditions and substrate availability underscores their ecological importance in global marine nutrient cycling. This versatility is particularly relevant in the context of global warming, which is predicted to profoundly impact microbial networks and functions, potentially disrupting broader community functions such as parasitic infections and the saprotrophic recycling of organic matter 5,56. For instance, the increasing representation of chytrids in polar communities has been linked to ice retreat 37, which may further alter community structures by perturbing parasitic or saprotrophic interaction networks and marine biogeochemical cycles.

Carbohydrate degradation and specific utilization strategies in pelagic fungi differ between polar and non-polar oceans
We further investigated the specific functions of fungal peptidases and CAZymes. Concerning peptidases, serine peptidases (SPs) were ubiquitously expressed across non-polar and polar regions, with their contribution to total fungal peptidase transcripts increasing with size fraction and toward higher latitudes (40.91–43.91% in non-polar vs. 50.75–63.51% in polar; Fig. 3B). In contrast, the contributions of metallo peptidases (MPs), cysteine peptidases (CPs) and aspartic peptidases (APs) to fungal transcripts decreased from non-polar to polar oceans (Fig. 3B). Notably, peptidase families, i.e., SPs, MPs and CPs, were distinctly differentiated between small and large size fractions in non-polar oceans within metagenomes (Fig. S3B). This contrasting pattern was explained by shifts in fungal community composition (ecological partitioning). Specifically, Chytridiomycota-affiliated genes were abundant in small size fractions of non-polar oceans (41.7%) and served as the primary contributors to SPs (Fig. S6A), whereas Basidiomycota-affiliated genes were dominant in polar oceans (46.14–58.06%) and were the main contributors to APs (Fig. S3A, B). Among these peptidase families, various types of serine peptidases (SPs) exhibited differential expression between non-polar and polar waters. Using machine-learning-based random forest classification 69, 55 peptidase families were predicted to drive the functional clustering between non-polar and polar oceans (Fig. S7A, Supplementary Data 6). These included the S1 family (S01), which plays a central role in proteolysis, a critical process for many biological functions; subtilisins (S08); prolyl oligopeptidases (S09); serine carboxypeptidases (S10); sedolisins (S53), which are acid-acting endopeptidases and tripeptidyl-peptidases; and rhomboid proteases (S54). The relatively high abundance of prolyl oligopeptidases (S09) and sedolisins (S53) in non-polar oceans suggests that these fungal peptidases may be associated with the degradation of plant-derived organic matter 70, which is more prevalent in these regions due to terrestrial input and coastal influences. S09 family is the most abundant secreted peptidases in non-polar oceans, suggesting their critical function in hydrolyzing peptide bonds in organic matter, thereby facilitating the release of bioavailable nutrients in non-polar marine ecosystems. The peptidase families C01, S01, and S54 are significantly more abundant in polar oceans than in non-polar regions (Fig. S7B). C01 peptidases, such as cathepsins, exhibit high efficiency in degrading complex proteins, a function particularly adapted to the low-temperature environments characteristic of polar waters 71. The elevated abundance of S01 peptidases in polar oceans is likely associated with their pivotal role in breaking down protein-rich organic matter, notably detritus derived from seasonal algal blooms. Polar marine ecosystems experience pronounced seasonal peaks in primary productivity, often dominated by diatoms and other cold-adapted phytoplankton 72. Upon bloom senescence, these organisms generate substantial proteinaceous material, necessitating efficient enzymatic hydrolysis for nutrient recycling. S01 peptidases facilitate this process by cleaving peptide bonds within these substrates, thereby enhancing the bioavailability of nutrients in the polar waters 73,74. Fungi, particularly those belonging to the phylum Chytridiomycota, are one of key contributors (28.7–34.3% of total peptidase genes and 19.5–25.6% of total peptidase transcripts) to serine peptidase production in polar ecosystems (Fig. S6A). S54 peptidases, which function as intramembrane serine proteases, play an essential role in membrane protein degradation and cellular stress responses 75. Their high prevalence in polar oceans likely reflects an adaptive mechanism for maintaining cellular homeostasis and enhancing microbial resilience under extreme cold conditions. Collectively, this divergence in peptidase family distribution—marked by the predominance of S09 peptidases in non-polar oceans versus the elevated abundance of C01, S01, and S54 families in polar regions—reflects the distinct evolutionary and ecological strategies adopted by fungal communities to optimize organic matter processing and nutrient cycling, driven by variations in temperature regimes, nutrient availability, and organic matter composition between polar and non-polar marine ecosystems.

Concerning CAZymes, we found that fungal carbohydrate processing was primarily mediated by glycoside hydrolases (GHs), glycosyltransferases (GTs), and carbohydrate-binding modules (CBMs), consistent with previous research on fungal CAZymes in the global ocean and deep-sea sediments 8,42. In the metatranscriptome, the most prevalent fungal CAZyme classes in the total CAZyme transcript pool were CBMs (47.2–49.6% in non-polar and 31.1–34.0% in polar) and GTs (ca. 23.4% in non-polar and 39.3–43.8% in polar) (Fig. 3F). These findings were consistent with the metagenomic data, further validating the transcriptional activity of these CAZyme classes (Fig. S3F). Although GTs, responsible for glycosidic bond formation, and CBMs, which facilitate the recognition and binding of specific carbohydrate substrates, play distinct roles in carbohydrate metabolism 76,77, both constitute a high proportion of total CAZyme genes, underscoring their critical role in substrate targeting and degradation (Fig. S3F). To acquire a deeper understanding of these enzymes, we focused our analysis on the differently expressed CAZyme families in the two ocean regions, since they were more prominent than fungal peptidases. In total, 81 CAZyme families were identified to be responsible for the fungal CAZyme clustering between two ocean regions again using a machine-learning random forest classification (Fig. 4A, Supplementary Data 7). Among the identified important CAZymes, xylanase (CBM13) was the most abundant enzyme in both non-polar and polar waters, and was significantly higher in the non-polar (41.1% in total CAZyme transcripts) compared to the polar ocean (20.1% in total CAZyme transcripts) (Fig. 4B). The CBM13, previously known as cellulose-binding domain family XIII, has been shown to associate with either GH10 or GH18 modules in the catalytic utilization of xylan as its primary substrate 78,79. Xylan is the most abundant hemicellulose polymer in nature and mainly originates from marine (red and green) algal sources 80. Among GT families, GT8 was more abundant in polar environments compared to non-polar oceans (Fig. 4B). GT8 enzymes are involved in glycoprotein folding quality control and cell wall biosynthesis 81. The active expression of GT8 may influence fungal adaptation to environmental stress by regulating the composition and structure of the cell wall. In more productive waters, where nutrient availability supports higher growth rates, GT8 could also play a role in facilitating increased cell wall biosynthesis to accommodate rapid hyphal extension and colony expansion 82–84. This suggests that GT8 activity may be linked not only to stress adaptation but also to the ability of fungi to thrive in resource-rich environments. In contrast, GT62, which participates in glycan structure modification and is exclusively secreted by fungi 42,85, was more abundant in non-polar oceans in both total and secreted CAZyme transcripts (Fig. 4B, C). This distribution pattern of GT62, which is consistent with that of GH45 and GH128, enzymes that target beta-glucan as substrate, suggesting a specialized adaptation to oligotrophic conditions. In these nutrient-poor environments, the ability to modify and degrade complex polysaccharides like beta-glucans may be critical for fungal survival and growth (Fig. 4B).

Among degradative CAZymes, GHs are essential enzymes required for the breakdown of oceanic polysaccharides, releasing short oligo/di-saccharides which can be translocated into the cell and further processed to release energy 76,86. GHs were consistently abundant across all samples in both polar and non-polar regions, comprising 14.2–23.1% of CAZyme genes and transcripts (Figs. 3F, S3F). In contrast, other degradative CAZymes, such as CEs and PLs, were barely detected in our samples (Fig. 3F). Notably, the relative abundance of GHs significantly increased at both the total and secretory gene levels, as well as at the secretory transcript level, in polar regions compared to non-polar regions (Figs. 3H, S3F, H). This relative increase in polar waters was driven by Basidiomycota, alongside the dominant contribution of Ascomycota to GH production (Fig. S6E). Furthermore, through random forest classification, we identified several GH families crucial for carbohydrate utilization, including cellulase (GH5), cellobiohydrolase (GH7), chitinase (GH18 and GH19), xylanase (GH30), endoglucanase (GH45), and glucanase (GH128) (Fig. 4B). In particular, the GH7, GH18, GH19, GH45, and GH128 families exhibited notably high secretion activity (Fig. 4C), further highlighting their functional importance. These enzymes were predicted to hydrolyze cellulose, β-glucan, xylan, chitin, or combinations thereof as substrates (Fig. 4B, C).

Chitin is a prevalent insoluble polysaccharide in oceanic environments. Both total and secretory GH19 chitinases exhibited significantly higher expression levels in the LF size fraction compared to the SF in non-polar oceans (Fig. 4C, S8A), suggesting enhanced chitin degradation activity in the LF niches. This pattern indicates that the ecological role of GH19 may be linked to either particle-attached communities or larger fungal structures, such as hyphal fragments or spores, which are more likely to be retained in the LF size fraction. In these environments, high extracellular enzyme activity facilitates efficient substrate degradation 30,42,87,88. In contrast, GH18 chitinases were more abundant in polar oceans (Figs. 4B, C, S8A). This distribution may reflect the adaptation of GH18 to colder environments 89 and its broader substrate specificity, enabling the degradation of diverse chitin sources such as zooplankton exoskeletons and fungal cell walls 47,83, and the seasonal influx of chitin from blooming zooplankton populations 39. These contrasting distribution patterns highlight the divergent ecological roles of GH18 and GH19 chitinases. Moreover, the CBM1, which has an affinity to chitin, cellulose and xylan, were notably more prevalent in secretory transcripts in polar oceans (12.4%) compared to non-polar regions (7.3%) (Fig. 4C). While CBMs lack direct enzymatic activity on carbohydrates, their interactions with catalytic domains significantly enhance the degradation of large, complex polysaccharides. In conjunction with other hydrolytic systems, CBMs effectively target polysaccharides 76,90–93. These interactions direct CBM1-associated enzymes, such as GH18, to potentiate cellulolytic activities on insoluble substrates 94,95.

Additionally, AAs, which are involved in oxidative and extracellular ligninolytic processes, were also present, although at low abundance in fungal CAZyme transcripts (5.16–7.43%). Yet, we observed a significant increase in the proportion of secretory transcripts of AAs in non-polar oceans (17.34%) (Fig. 3B). The AAs CAZyme families such as lytic chitin monooxygenase AA11 (EC 1.14.99.53) and AA15 (EC 1.14.99.53), belonging to auxiliary activities in particular displayed high levels of expression in non-polar ocean samples compared to polar waters (Fig. 4B, C). Although they may not directly hydrolyse carbohydrates, their close association with carbohydrates in the phytoplankton cell wall degradation suggests that ligninolytic enzymes could collaborate with other hydrolytic systems 96,97.

Altogether, this analysis highlights the distinct roles of various CAZyme families in carbohydrate degradation mediated by marine fungi across non-polar and polar oceans. These findings suggest a potential connection between fungal CAZymes and the availability of specific carbohydrate substrates 42, as well as the distribution and transcriptional activity of fungal clades in different environments. Stable isotope labeling method showed close relationship between substrate uptake and corresponding CAZyme gene expression, which further highlight that the expression of CAZyme and peptidase is indicative for specific substrate degradation 14. Notably, the distribution of fungal peptidases and CAZymes appears to be more strongly influenced by geographic and environmental factors than by particle size or habitat fractionation—a pattern that contrasts sharply with prokaryotic communities, which are often more sensitive to microhabitat variations. As climate change accelerates, polar regions in particular are undergoing significant warming, accompanied by shifts in nutrient availability and primary production 98,99. These environmental changes are likely to reshape fungal community composition and metabolic functions, potentially disrupting the balance of organic matter storage and cycling in these ecologically sensitive regions. For instance, alterations in fungal CAZyme expression and activity could influence the degradation of complex carbohydrates, thereby affecting carbon sequestration and nutrient fluxes.

Collectively, our study underscores the overlooked role of the oceanic mycobiome in governing biogeochemical cycles within both small and large-size fraction-associated microenvironments, spanning non-polar and polar oceanic regions (Fig. 5). The widespread and diverse presence of fungal functional transcripts in the open ocean highlights that their substantial influence on marine ecosystem dynamics has been previously underestimated 8–10. Our study revealed a significant association between the taxonomic affiliation of specific peptidase and CAZyme groups probably linked to the availability of carbohydrate and protein substrates characteristic of polar and non-polar waters (Fig. 5). This taxonomic specificity in CAZyme and peptidase utilization underscores the crucial role of fungal diversity in mediating enzymatic activities vital for the breakdown of polysaccharide and protein substrates within oceanic ecosystems (Fig. 5). Describing the active contribution of pelagic fungi in the degradation of organic matter in the Southern Oceans, allowed us to reveal pronounce differences in fungal CAZymes and peptidase taxonomic affiliation and functional diversity between non-polar and polar oceanic environments, highlighting distinct carbohydrate and protein niche segregation of pelagic fungi across these globally significant oceanic regions. The methodological framework employed in this study enabled the identification of distinct mechanisms utilized by divergent fungal communities for degrading specific organic carbon substrates. While some functional variations were observed between large and small size fraction-associated communities, the ecological differentiation between polar and non-polar environments was more pronounced than the variations across size fractions. Notably, polar regions exhibited a preference for chitinase (GH18), chitin-binding CBM1, and GT8, a glycosyltransferase involved in polysaccharide biosynthesis, while total and secretory GH19 chitinases showed relatively higher expression levels in LF (large fraction) size fractions of non-polar oceans. In addition, non-polar oceans displayed higher abundances of key enzymes, including the prevalent xylanase (CBM13), glycosidic bond-modifying enzyme (GT62), and glucan-utilizing enzymes GH45 and GH128. Our findings also indicate that temperature is a major environmental parameter shaping total fungal CAZyme activities, indicating a susceptibility of the organic matter degradation of pelagic fungi to ocean warming. Collectively, these findings indicate an active contribution of pelagic fungi to organic matter degradation in the ocean, while revealing the main environmental and ecological factors as well as the enzymatic mechanisms driving it. To further elucidate fungal contributions to organic matter cycling, future studies should incorporate stable isotope analysis 14 to trace the flow of organic matter through fungal pathways. Such advancements will enhance our understanding of fungal roles in marine ecosystems and their adaptive responses to environmental changes.

Methods
Environmental sampling
The samples were collected during the oceanographic research cruises ANTOM-I (15 December–15 January, 2020/2021) and ANTOM-II (January 23–February 6, 2022). The stations were selected to provide a broad representation of oceanic ecosystems in the Atlantic (non-polar) and Southern (polar) Oceans spanning a broad latitudinal gradient across the subtropical to polar oceans (Fig. 1A, Supplementary Data 1). Thirty-one to 106 liters of seawater from the surface and deep chlorophyll maximum (DCM) layers were sequentially filtered with a McLane in-situ pump through >3 μm (hereafter, communities referred to as large size fraction “LF”) and 0.2 μm (hereafter, communities referred to as small size fraction “SF”) polycarbonate filters (Millipore). In total, 25 stations and 53 samples covering different sampling depths in both surface (5–20 m) and DCM (25–100 m) layers and size fractions, encompassing both the SF and LF-associated fungal communities, were collected. After surfacing the pump, the filters were immediately stored at −70 °C until DNA and RNA extraction. Environmental parameters were measured separately with a Sea-Bird SBE9 CTD (conductivity, temperature, depth) rosette deployed at the same depth as the in-situ pump.

DNA extraction and shotgun metagenomic sequencing
The total DNA was extracted using a standard phenol extraction protocol 100. The concentration of the extracted DNA was estimated using Quant-iT™ PicoGreen® (Thermo Fisher Scientific). In total, 42 genomic DNA extracts (corresponding to 18 SF+9 LF from non-polar and 7 SF+8 LF from polar) were subjected to library preparation using the NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® (New England Biolabs, USA) according to the manufacturer’s instructions. The genomic DNA was randomly sheared into short fragments. The fragments with adapter were size selected via beads-based size selection of libraries that are approximately 500 bp. The DNA libraries were sequenced on an Illumina NovaSeq 6000 platform using paired-end mode (2×150 bp) at the Novogene (UK), according to effective library concentration and data amount required.

RNA extraction and metatranscriptomic sequencing
The total RNA was extracted using a TRIzol™ method 101. RNA extracts were treated with DNase I (Ambion). The total RNA yield was quantified using the Quant-iT™ RiboGreen® assay (Thermo Fisher Scientific). 53 samples (corresponding to 18 SF+16 LF from non-polar and 9 SF+10 LF from polar) were subjected to library preparation. The mRNA present in the total RNA sample was isolated with magnetic beads of oligos (dT)25. Subsequently, mRNA was randomly fragmented and cDNA synthesis proceeded through the use of random hexamers and the reverse transcriptase enzyme. Once the synthesis of the first chain was finished, the second chain was synthesized with the addition of an Illumina buffer. The resulting products went through purification, end-repair, A-tailing, and adapter ligation. Fragments of the appropriate size were enriched by PCR, where indexed P5 and P7 primers were introduced, and final products were purified. The library was checked by fluorometry (Qubit®, ThermoFisher, USA) and real-time PCR for quantification and bioanalyzer Agilent 2100 for size distribution detection. The 53 cDNA libraries were sequenced on an Illumina NovaSeq 6000 platform in paired-end mode (2×150 bp) at Novogene (UK), according to effective library concentration and data amount required.

The primary reason for the difference in sample numbers (42 metagenomic vs. 53 metatranscriptomic) is due to logistical challenges during sample collection and our focus on obtaining high-quality RNA for metatranscriptomic analyses. The limited seawater volume collected restricted the available biomass for both DNA and RNA extractions. Prioritizing the required RNA input (>700 ng) for metatranscriptomics, we had fewer samples available for metagenomic sequencing (42 DNA samples).

Bioinformatic analysis of metagenomics and metatranscriptomics
The metatranscriptomic reads were quality filtered using Trimmomatic (0.39) using default settings 102. The rRNA reads were removed using SortMeRNA (2.0) 103. The clean reads from each sample were assembled individually using MEGAHIT (1.2.9) with default settings 104. Putative genes were then predicted using MetaEuk with easy-predict (release 7-bba0d80) 105. Protein sequences were clustered at 90% similarity (-c 0.9 -G 0 -aS 0.9) using CD-HIT (4.6.8) 40. The abundance of each predicted gene was evaluated by mapping reads back with the Burrows-Wheeler Aligner (BWA) algorithm (0.7.17) 106 and the relative expression of the gene were calculated by mapping read counts to genes after normalizing against gene length (reads per kilobase mapped, RPKM) using CoverM (0.6.1) (identity 0.95) (https://github.com/wwood/CoverM).

For all the predicted genes, CAZymes were annotated using hmmsearch against the dbCAN database 107 (e-value < 1×10−10; coverage > 0.3). The domain with the highest coverage was selected for sequences overlapping multiple CAZyme domains. The CAZyme sub-families were further grouped into different glycan substrate groups (e.g., cellulose, chitin, xylan) by searching against dbCAN-sub (downloaded in August 2023). Peptidases were annotated using DIAMOND (2.1.6) BLASTp 108 searches against the MEROPS database 109 (e-value < 1×10−10). SignalP (6.0) was used to detect the presence of signal peptides for eukaryotic sequences under eukarya mode 44.

We compared gene prediction results between MetaEUK and Prodigal, a tool originally designed for prokaryotic gene prediction, on eukaryotic transcriptomic data 105,110. Previous studies have reported that several prokaryotic gene prediction tools, such as Prodigal, GeneMarkS-T, and TransDecoder, perform well on eukaryotic transcriptomic sequences, particularly those that are intronless or minimally spliced. This is largely due to shared characteristics in translation initiation across domains, including the use of start codons (AUG) and the identification of open reading frames (ORFs). Therefore, we conducted comparison between MetaEuk and Prodigal. We hypothesized that Prodigal could produce gene predictions comparable to those of MetaEuk when applied to eukaryotic metatranscriptomic assemblies, which predominantly contain continuous, intronless protein-coding sequences. This is particularly relevant when using contigs longer than 200 base pairs, as commonly observed in eukaryotic metatranscriptomic data 8,9,111. To link metagenomic data to functional annotations, we mapped gene abundances from the metagenome to gene categories derived from the metatranscriptomic assembly using the BWA algorithm 106. This approach allowed us to extract abundances of CAZymes and peptidases while avoiding the complexities and potential biases associated with metagenome assembly, particularly given the dominance of prokaryotic sequences in metagenomic datasets 8,9,20,112. The comparison results revealed the functional and taxonomic distributions of fungal genes/transcripts, as well as fungal genes/transcripts encoding peptidases and CAZymes, showed minimal changes between Metaeuk and Prodigal predictions (Figs. S9, S10). The taxonomic patterns of fungal genes/transcripts encoding peptidases and CAZymes consistently differed from those of overall fungal genes/transcripts (Figs. S9, S10). Ascomycota, Basidiomycota, and Chytridiomycota were the primary contributors to peptidase and CAZyme genes, with Mucoromycota additionally prominent in CAZyme production (Fig. S10).

The taxonomic annotation of CAZyme and peptidase sequences was performed by the Diamond blastp-based LCA (last common ancestor) algorithm at the protein level using the NCBI non-redundant protein database (NCBI-nr; release date: April 2023) as the reference, with an e-value threshold of 10−5 to ensure high-confidence matches 108,113. In the LCA algorithm, an NCBI taxonomic identifier (TaxID) is based on the last common ancestor of all hits whose bit scores fall within the first 10% of the best hit score (–top 10). By integrating multiple high-scoring matches, the LCA algorithm minimizes erroneous annotations and provides a more accurate representation of taxonomic diversity in our dataset. To cross validate the taxonomic assignment, we also conducted parallel taxonomic annotation (metaeuk taxtocontig) using a comprehensive protein reference database comprising approximately 88 million curated sequences from three primary sources: the MERC dataset from eukaryotic Tara Oceans metatranscriptomic datasets, the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP), and the Uniclust50 database (https://wwwuser.gwdguser.de/~compbiol/metaeuk/2019_11/) 105. The comparison results revealed that we retrieved 2716 fungal sequences by searching against the NCBI-nr database, which was twice more as MetaEuk-based contig/predicted-gene classifications (1011 fungal sequences). Moreover, 86.7% (877/1,011) of the fungal sequences identified in the MERC-MMETSP-Uniclust50 database were also present in the NCBI-nr database (Fig. S9), suggesting a comprehensive coverage of the NCBI-nr database. Thus, searching against the NCBI-nr database provides a robust alternative to the taxonomic annotations from MetaEuk-based contig/predicted-gene classifications 105.

To evaluate the relative abundance of genes in the metagenomic data, reads from each metagenome were mapped to the CAZyme and peptidase gene categories derived from the metatranscriptomic assembly using the BWA algorithm (0.7.17) 106. This approach allowed for a direct comparison of metagenomic data to the functional genes identified in the metatranscriptomic data without the complexities and potential biases associated with metagenome assembly, especially given the high proportion of prokaryotic sequences in the metagenomes.

Statistical analysis and visualization
All the statistics and visualization were performed using specific packages in R 4.3.1 (www.r-project.org/). We applied a custom random forest classification (using randomForest package; see also https://zenodo.org/records/13301199) 112,114,115 to predict peptidase and CAZyme family distributions between polar and non-polar regions, given the absence of clear functional variation across size fractions. Random forest classification was carried out with a relative abundance table of peptidase / CAZyme transcript families. The relative abundance table of peptidase / CAZyme transcript families was randomly split into “training data” (containing 70% of the samples, 37 out of 53 metatranscriptomics samples) and “testing data” (containing 30% of the samples, 16 out of 53 metatranscriptomics samples). The final model was optimized and trained on the complete dataset with the following configuration: importance=TRUE, ntree=500, and nrep=1000. Model significance and cross-validated R² values were evaluated through 1000 permutations using the ‘rfPermute’ package in R. Within the Random Forest framework, predictor variable importance was quantified by the percentage increase in mean squared error (% IncMSE), where higher values indicate greater explanatory power of the respective variable 116. The %IncMSE for each decision tree was computed using out-of-bag (OOB) estimates, enabling robust assessment of the relative contribution of individual predictors to the model’s performance.

All the statistics and visualization were performed using specific packages in R. In detail, the Maps 117 package was utilized to generate maps illustrating the spatial distribution of samples across both the Atlantic Ocean and the Southern Ocean. The correlations between Bray–Curtis dissimilarity of fungal CAZyme and peptidase function and Euclidean distances of environmental variables were assessed using the Mantel test with the linkET package 118. Diversity calculations were conducted with the Vegan package 119, while ggplot2 120 was utilized for visualizing fungal taxonomy, CAZyme, and peptidase classification.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper reports the analysis of a set of metagenomes and metatranscriptomes taken in two cruises in the Atlantic and Southern Oceans, to identify the presence and expression of carbohydrate-degrading (CAZYmes) and peptidase genes from marine fungi. The main idea is to develop a better understanding of the abundance and function of fungi in marine systems and to highlight functional differences in different regions. The paper presents a substantial amount of work, resulting in many plots and figures, but the obtained results are not properly digested, so it is very difficult to extract a clear picture of the data. Moreover, some of the results are unexpected and little discussed, and an important step of the bioinformatic pipeline is inappropriate, as a tool for predicting prokaryotic genes has been used to find fungal genes.\nThe senior author of this paper has many publications on marine fungi, in some cases also using metagenomes and metatranscriptomes. In the introduction one expects to learn what has been done before, and how this paper contributes with new information.\nFor each station there are two size fraction analyzed, one including all organisms (>0.2 µm) and a second including all organisms except the very small ones (>3 µm). I am surprised by the way the authors label these two samples, as there are many organisms in common and, most importantly, the size range of fungi does not need to be exclusively below 3 µm. The definition of free-living and particle-attached somewhat derives from the prokaryotic literature, but here the targets are not prokaryotes but fungi, that may cover a much wider range of sizes. So, I find this labelling misleading.\nThe data presentation is largely based on primary data, with many different categories (metaG/metaT; polar/non-polar, peptidase/CAZy genes; total/secretory enzymes) that are displayed together, being extremely difficult to follow the message. This would need some kind of processing of the data. Moreover, when presenting the data, the authors should first present a general overview of what they found and then focus in the particular differences among the categories. Right now, all the explanation is based in these differences, being extremely difficult to grasp the message.\nThe authors present the results they obtained with very little critical insights. When studying the relative abundance of fungi they found the following order of group abundance, Ascomycota, Mucoromycota, Chytridiomycota, Basidiomycota and Zoopagomycota. How this data compares with previous descriptions of fungal diversity in the open ocean? Is this what it was expected? Taxonomic data derived from metagenomes and metatranscriptomes depend largely on the taxa present in reference databases, has this been taken into account?\nThis work is based in genes predicted from metagenomes and metatranscriptomes. However, in this critical step the authors use Prodigal, a program designed to predict prokaryotic genes in metagenomics reads (Prodigal: PROkaryotic DYnamic programming Gene-finding ALgorithm). As fungi are eukaryotic organisms, this does not seem the best option, and I wonder how this can severely affect the results obtained. This is a major drawback of the manuscript.\nLine 41. Why non-polar? Wouldn't be better to label this category as \"Atlantic\"??\nLines 39-42 . It is not enough to say that there are functional changes, would be interesting to explain these changes.\nLine 93-100. The first sentence says that there is few data on carbohydrate and protein degradation by marine fungi, and the second sentence says that this is the first report.\nLines 155-159. I guess there are expected (mostly methodological) reasons to explain the order of magnitude difference in the number of CAZY genes found in metagenomes and metatranscriptomes. But these are not explained nor discussed.\nLine 219-220. The groups dominating in peptidases genes (Chytridiomycota-Basidiomycota) are very different from the groups dominating the community (Ascomycota-Mucoromycota). While this is possible, it is a bit unexpected, and it could also be due to methodological reasons (mostly related to database completeness for taxonomy). At any rate, I am surprised that the authors did not try to explain these very obvious differences.\nLine 313. Results show that the most prevalent fungal CAZYme were GT. However, there is no mention in the next paragraph on what they are doing (they do not seem to be directly involved in degradation) and why they are the most important. They are just ignored.\nLines 406. I do not see why detecting diverse fungal genes in the omics data supports the conclusion that fungi play a \"profound and significant role\" in governing biogeochemical cycles. This depends on how much fungi contribute to these cycles (as compared with other taxa), which cannot be derived from the presented data.\nLines 416-418. Comparing fungal peptidase and CAZyme genes against non-fungal peptidases/CAZYmes would be a first step to interpret the importance and role of fungi in marine ecosystems\nLine 430-431. Here it appears temperature, but when this has been analyzed??\nLine 442- I guess it should be \"latitudinal\"\nLines 504-505. It is not very clear how the metagenomes were treated. I guess no assembly was attempted, right? Then read mapping to the transcripts, resulted in a low number of genes found. Can you explain this, please (I guess most signal was prokaryotic), And how this could impact the results found?\nFigure 1. Some of the graphs do not have the y-axis legend. Also, what is the x-axis of panel C? Are individua stations? How about water depth? This seems just a compilation of data, with very little effort to convey a message to the reader.\nLine 545. I do not see the category \"Others\"\nFig. 3. Again, this figure includes a lot of categories, plots, and colors. Very, very complicated to follow. The same for Figure 4", "reviewer_2": "Whilst more data on marine fungi are needed and I understand the exploration of the polar region is interesting (though not totally novel), I miss the significance of the contribution to the field to warrant publication in Nature Communications or similar level journal. Similar work has already been conducted that is already in the established literature, including on marine fungal Cazymes and in the Atlantic region, for example see work from Orsi Group. Some of these studies in the established literature (particularly studies not from the author team) appear missing from the manuscript.\nOrsi et al (2022) Carbon assimilating fungi from surface ocean to subseafloor revealed by coupled phylogenetic and stable isotope analysis. The ISME Journal 16 1245–1261.\nChrismas et al (2020) Depth-dependent mycoplankton glycoside hydrolase gene activity in the open ocean—evidence from the Tara Oceans eukaryote metatranscriptomes. The ISME Journal 14 2361–2365.\nBaltar et al (2021) Potential and expression of carbohydrate utilization by marine fungi in the global ocean. Microbiome 9 106.\nBreyer et al (2022) Global contribution of pelagic fungi to protein degradation in the ocean. Microbiome 10 143.\nThe study is largely descriptive, more a catalogue of genes/mRNA, greater attempt should have been made to synthesise the work.\nThe use of DNA/RNA metagenomes and metatranscriptomes can only go so far. Function could have been explored further with stable isotope analysis as the Orsi Group have done (see Orsi et al (2022) Carbon assimilating fungi from surface ocean to subseafloor revealed by coupled phylogenetic and stable isotope analysis. The ISME Journal 16 1245–1261).\nThe presentation of the data in the current figures in not very useful.\nWas any attempt made to explore differences across the Atlantic. A range of biomes were sampled but not obvious differences.\nI can see a potential issue in the data analysis, interpretation and conclusions. The study is based on two cruises conducted in two different years. I cannot find any information the dates when samples were collected. Where the different cruises one Atlantic and one Arctic? How do you separate the effect of time?\nAll sequence data should be made available in a public database.", "reviewer_3": "The authors used multi-omics techniques (metagenomics and metatranscriptomics) for high-resolution spatial analysis of functional fungal diversity and metabolic potential in the open Atlantic and Southern Ocean from subtropical to polar regions. Using these techniques, the researchers successfully uncover the significant role of fungi in organic matter degradation, with a focus on their preferential role in carbohydrate degradation. The differentiation between free-living and particle-attached communities, along with the high-resolution spatial analysis across diverse marine regions, adds considerable depth to the study's findings.\nThe introduction is comprehensive, with pertinent references cited, and clearly articulates the novelty and necessity of the study. The materials and methods section are detailed and well-written. The results and discussion provide a comprehensive and insightful analysis, the findings are presented in detail, with nuanced data interpretation, highlighting the study's rigor and depth. Although the authors provided enough data to conclude this study, some questions and errors must be addressed. Additionally, the discussion section could be expanded to improve the clarity and depth of the interpretation of the findings.\n1. How do the functional roles of the identified fungal taxa (Ascomycota, Basidiomycota, Mucoromycota, etc.) differ between free-living and particle-attached communities? Please thoroughly elaborate and include in result and discussion section.\n2. How does the association with particles influence the functional roles and gene expression profiles of fungi in marine environments?\n3. What is the main reason for the higher proportion of peptidase genes in polar water compared to non-polar water?\n4. What are the specific ecological roles of different fungal species in polar versus non-polar regions?\n5. What are the ecological implications of the dominant presence and higher expression of Serine peptidases (SP) by Chytridiomycota in polar oceans compared to Basidiomycota and Ascomycota in non-polar regions?\n6. How does the functional diversity of fungal CAZymes, particularly the differences between glycoside hydrolases (GHs), carbohydrate-binding modules (CBMs), and glycosyltransferases (GTs), influence carbon cycling in polar versus non-polar marine environments?\n7. Why there is a vast difference in the number of metagenomics and metatranscriptomics samples, it won't be same?\nPlease justify.\n8. When contradictory results are observed in a manuscript, authors should provide the most probable reason and possible mechanism to aid understanding of the findings.\n9. Please reframe the sentence from lines 31 to 36 of the abstract because it is too long.\n10. In line number 195 “(Figure S1C)).” Remove one “)”.\n11. Please include a few lines in the appropriate section explaining why the metagenomic and metatranscriptomics analysis results differ.\n12. Please reframe the sentence in lines 65-67, as \"such as\" was used twice and should be replaced with another word.\n13. A thorough proofreading would be necessary to avoid grammatical and typographical errors throughout the manuscript.\n14. No information is provided about how to download the sequence data in a public database such as NCBI or EMBL-EBI."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "This is the second time I analyze this manuscript. In my first review I raise a set of methodological questions (bioinformatic tools used) and formal questions (how data is presented). Now I have carefully read the answers to my previous questions and although I recognize that the manuscript seems to be improved, I am not completely satisfied with all the answers.\nOne substantial criticism still remains, why using a program designed for gene prediction in prokaryotes to perform gene prediction in eukaryotes. Eukaryotic genes have introns and alternative splicing, which make their prediction substantially different than that of prokaryotic genes. Programs for predicting eukaryotic genes from metagenomic or metatranscriptomic data already exist, such as MetaEuk and others, and these should be used together with a complete reference database of eukaryotes. Perhaps the predictions will not be extremelly different, but the authors should use the best tools available.\nAnother concern is about size fractionation. Initially samples were divided as free-living and particle attached, ignoring the fact that fungi can cover a wide spectra of cell sizes. So, in the fraction >3 µm there could be small or large fungi attached to particles or simply fungi larger than 3 µm (for instance making hypha). This has been assumed in some parts (i.e. lines 268-279), but in other parts the assumption that fungi on the >3 µm are particle attached still remains (i.e. lines 117-120, 136-139). The reality can include the two aspects, but this needs to be explicitly explained.\nThere was the fact that the taxonomic groups sometimes were against the initial expectations. A sentence in the manuscript says \" Previous research primarily documented Dikarya\", and here other groups were found dominating gene expression.\nFor instance, the majority of fungal peptidases derive from Chytridiomycota, both in Atlantic and Antarctic waters. Also, the dominance of some groups does not match the dominance of genes expressed. My point was, how much of these inconsistencies are due to inappropriate taxonomic classification of the retrieved transcripts because incomplete reference genomic resources? This particular concern has not been addressed.\nRegarding the presentation of the data, I see that there has been an effort to simplify a bit some of the most complex figures, which I appreciate. Still, I do not see and effort to condense the results to convey clear messages from the figures.\nFinally, the text is very long and convoluted, and it is not clear how it is organized, with apparently lot of redundancies that are difficult to completely follow. For instance, I note here the second and third headings of the Results and Discussion section, which seem very similar: 2) Biogeography and functional diversity of fungal peptidases and CAZymes, 3) Taxonomic affiliation and functional diversity of fungal peptidases genes and transcripts.", "reviewer_2": "Guo et al and Baltar present an apparently revised manuscript. Despite efforts to make changes, which are much appreciated, fundamental concerns remain the same.\nThis is interesting work but builds on several previous similar studies including those from one of the lead authors. This has not changed. I still do not see a genuine Nature Communications level step change in this work beyond what have been previously conducted e.g. see Baltar’s previous papers and from Orsi et al.\nThe study as presented in the revised manuscript remains descriptive and more a catalogue of genes/mRNA. More is needed to this level of manuscript including validating process measurements e.g. see Orsi et al 2022 ISME Journal 16 1245-1261.\nThe study still overinterprets DNA/RNA metagenome and metatranscriptome data. The lack of other evidence (e.g. stable isotope analysis) continues to limit the scope and only shows what has been shown before.\nKey similar and previous studies remain missing from the revised manuscript. Irrespective of the reasons for omitting previous studies (strategic or carelessness) it is not acceptable to ignore previous similar/same work in this way. This is disappointing for any journal but especially so for a leading journal such as Nature Communications.", "reviewer_3": "I appreciate the authors efforts in addressing my previous comments and revising the manuscript accordingly. The authors have effectively addressed the scientific concerns raised in the initial review, improving the overall structure and depth of the study. The representation and discussion of the data now offer a more thorough and comprehensive understanding of the observations and findings.\nHowever, before the manuscript can be accepted for publication, I would strongly recommend a final round of careful proofreading. The authors should check for any remaining grammatical errors and ensure accuracy in tense usage throughout the text. Consistency in language is crucial for maintaining the scientific integrity and readability of the paper. I also suggest that the authors confirm all technical terms are used correctly to avoid any potential misinterpretation of the findings.\nOverall, I am pleased with the improvements made in this revised manuscript, and believe that it offers valuable insights into fungal-mediated organic matter degradation in marine environments and contributes significantly to the field of oceanic fungal ecology, making it suitable for publication in Nature Communications."}, "author_response": "Point-by-point response to the Reviewer`s comments\nWe greatly appreciate the Nature Communications’ reviewers for their comments\nthat are crucial for improving and refining this manuscript. We include this in our\nAcknowledgement section. We have addressed and incorporated all the reviewers’\nsuggestions. Below, we detail our responses to each of the reviewers’ comments, as\nwell as the actions taken in response to the concerns. In our response, we refer to\nthe line numbering of the revised version in which modified text is highlighted in\nyellow.\nReviewer #1:\nThis paper reports the analysis of a set of metagenomes and metatranscriptomes\ntaken in two cruises in the Atlantic and Southern Oceans, to identify the presence\nand expression of carbohydrate-degrading (CAZYmes) and peptidase genes from\nmarine fungi. The main idea is to develop a better understanding of the abundance\nand function of fungi in marine systems and to highlight functional differences in\ndifferent regions. The paper presents a substantial amount of work, resulting in many\nplots and figures, but the obtained results are not properly digested, so it is very\ndifficult to extract a clear picture of the data. Moreover, some of the results are\nunexpected and little discussed, and an important step of the bioinformatic pipeline is\ninappropriate, as a tool for predicting prokaryotic genes has been used to find fungal\ngenes.\nThe senior author of this paper has many publications on marine fungi, in some\ncases also using metagenomes and metatranscriptomes. In the introduction one\nexpects to learn what has been done before, and how this paper contributes with\nnew information.\nWe sincerely appreciate the thoughtful comments and detailed review of our\nmanuscript and data. Thank you also for recognizing the senior author's prior\ncontributions to marine fungal research. We appreciate your suggestion to provide a\ncomprehensive introduction that situates our study within the broader scientific\n1\ncontext. In response, we have expanded the introduction to emphasize existing\nresearch on the diversity, activity, and functional potential of bacteria across different\nsize fractions. This enhanced background highlights a research gap: the lack of\ncomprehensive studies focusing on fungal communities within smaller size fractions.\nOur study addresses this gap by exploring how biogeographical differences between\npolar and non-polar oceans, as well as size fractions, influence the composition and\nactivities of oceanic fungal communities, thereby offering new insights to the field.\nPlease refer to page 4, lines 86–96 for further details, where the answers to the next\ncomment are also addressed.\nFor each station there are two size fraction analyzed, one including all organisms\n(>0.2 µm) and a second including all organisms except the very small ones (>3 µm).\nI am surprised by the way the authors label these two samples, as there are many\norganisms in common and, most importantly, the size range of fungi does not need\nto be exclusively below 3 µm. The definition of free-living and particle-attached\nsomewhat derives from the prokaryotic literature, but here the targets are not\nprokaryotes but fungi, that may cover a much wider range of sizes. So, I find this\nlabelling misleading.\nThank you very much for this insightful comment. We used this size fractionation\napproach to contextualize our results with previous studies on the size fractionation\nof pelagic microbes, which primarily focus on prokaryotes, as fungi remain largely\nunderstudied in the pelagic ocean. In response to comments of Reviewer #3, and to\navoid confusion with the terminology commonly used in prokaryotic studies, we have\nrelabeled the sample fractions as small size (0.2-3 µm, SF) and large size (>3 µm,\nLF) fractions throughout manuscript and Figures.\nWe have expanded the introduction to better explain this point. Please refer to page\n4, lines 86-96:\n“The size of cells and of particle is crucial for the adaptability of pelagic prokaryotes to\nchanges in their microenvironment and nutrient conditions 1, 2, 3, 4, 5, 6. Typically,\nplanktonic microorganisms are divided into free-living (FL) and particle-attached (PA)\n2\ncommunities 7. PA pelagic prokaryotes frequently develop into dense clusters\ncharacterized by high extracellular enzyme activity 8. In contrast, FL microorganisms\nwith small genomes are optimized for environments with low substrate availability, and\nthey tend to express membrane transporter genes at high levels 3, 9, 10. Given these\ndifferences, it is likely that the abundance and functional diversity of oceanic fungal\ntaxa also differ between various size fractions. Previous research has indeed indicated\nthat size fractions influence the composition of microbial eukaryote communities 11.\nHowever, there remains a lack of comprehensive studies focusing on the fungal\ncomponent, especially in smaller size fractions.”\nAnd page 5, lines 117-121:\n“To fill the knowledge gap and to distinguish fungi colonizing particles of varying\nsizes—which might offer different micro-environments and nutrient availability—we\nanalyzed two size fractions at each station: a small (0.2-3 µm, SF) and a large (>3\nµm, LF) size fraction. We also explore the principal environmental factors influencing\nthese fungal functional roles.”\nThe data presentation is largely based on primary data, with many different\ncategories (metaG/metaT; polar/non-polar, peptidase/CAZy genes; total/secretory\nenzymes) that are displayed together, being extremely difficult to follow the\nmessage. This would need some kind of processing of the data. Moreover, when\npresenting the data, the authors should first present a general overview of what they\nfound and then focus in the particular differences among the categories. Right now,\nall the explanation is based in these differences, being extremely difficult to grasp the\nmessage.\nTo address the concern about clarity of data presentation, we have thoroughly\nrevised both the main Figures and Supplementary Figures to provide a clearer, more\nstructured narrative.\nThe original Figure 1 has been revised by deleting original Figure 1E and H. These\npanels were removed as they did not contribute significantly to the overall\nunderstanding. We moved panel 1B to the lower section, facilitating a more intuitive\n3\ncomparison. This new layout clarifies how fungi contribute to eukaryotic diversity and\nactivity.\nThe previous Figure 3 has been reorganized into the current main Figure 3 and\nSupplementary Figure S4, while the original Figure 4 has been divided as the new\nmain Figure 4 and Supplementary Figure S5. Additionally, we have revised the main\nFigure 6, along with Supplementary Figures S3-S7, now labled as Figures S6-S10,\nto ensure consistency and improve the interpretability of the data. In the various\nsubplots across Figures S6 and S9, we ensured that the same fungi species are\nrepresented using the same color across all panels. This consistency helps readers\ntrack the contribution of specific species more easily. For the heatmaps depicting\npeptidase and CAZyme functions (Figures S7 and S10), we adopted a color scheme\nwith stronger contrast. We grouped samples by ocean regions, allowing the\nheatmaps to effectively show the variation in peptidase and CAZyme functions\nacross different oceanic regions and size fractions. This grouping emphasizes the\ndifferences between polar and non-polar oceans in a more structured manner.\nWe have also added a new Supplementary Figure S2, which highlights the spatial\ndistribution of genes and transcripts encoding fungal peptidases and CAZymes\nacross a latitudinal gradient, providing a broader context for the observed patterns.\nFigure numbers throughout the manuscript have been updated accordingly.\nThe authors present the results they obtained with very little critical insights. When\nstudying the relative abundance of fungi they found the following order of group\nabundance, Ascomycota, Mucoromycota, Chytridiomycota, Basidiomycota and\nZoopagomycota. How this data compares with previous descriptions of fungal\ndiversity in the open ocean? Is this what it was expected? Taxonomic data derived\nfrom metagenomes and metatranscriptomes depend largely on the taxa present in\nreference databases, has this been taken into account?\nWe have carefully compared our findings with the existing literature on pelagic fungi\nomics, specifically two recent studies utilizing the Tara Oceans dataset (Baltar et al.,\n2021; Breyer et al., 2022), as well as the works of Chrismas et al. (2020) and Orsi et\nal. (2018, 2022) 5, 6, 12, 13, 14. Figure 7 provides a detailed comparison of fungal\n4\nfunctional diversity, biogeography, and activity in the open ocean, highlighting the\nsimilarities and differences with these prior studies.\nPrevious research predominantly identified Ascomycota and Basidiomycota\n(Dikarya) as the major fungal phyla. In contrast, our study revealed a notable\npresence of early-diverging fungal lineages, such as Mucoromycota,\nChytridiomycota, Zoopagomycota, Blastocladiomycota, Cryptomycota, and\nMicrosporidia—groups that were either scarcely identified or not discussed in earlier\nstudies. This finding is consistent with Peng et al. (2021), who reported that early-\ndiverging fungal phyla comprised about one-third of the fungal community in\nmetagenomes from an Oceanic Oxygen Minimum Zone 15. This suggests that fungal\ndiversity in the open ocean may be broader and more complex than previously\nrecognized.\nYes, we acknowledge that taxonomic annotation depends heavily on the reference\ndatabases used, as the accuracy is influenced by the evolutionary distances\nbetween environmental taxa and those in genomic sequence databases 16.\nMicroeukaryotic sequences can be annotated using various databases, including\nMMETSP + MarRef, PhyloDB, EukProt, and the NCBI nr database 17, 18, 19. Our\nanalyses utilized the NCBI nr database, which is comprehensive and continually\nupdated, allowing us to identify a greater diversity of fungal taxa in our samples. In\nalignment with Cohen et al. (2021, Nature Microbiology; 2024, Nature\nCommunications), who highlighted the advantages of using custom and combined\ndatabases, we employed the NCBI nr database along with DIAMOND and the\nLowest Common Ancestor (LCA) algorithm 18, 19. This approach was chosen to\nenhance the accuracy and reliability of our taxonomic annotations. Incorporating a\nbroad and high-quality database, such as NCBI nr, ensures that our annotations are\nrobust and comprehensive. The usage of NCBI nr data base also enables systematic\ncomparison with previsous result from our group 5, 6.\nThis work is based on genes predicted from metagenomes and metatranscriptomes.\nHowever, in this critical step, the authors use Prodigal, a program designed to\npredict prokaryotic genes in metagenomics reads (Prodigal: PROkaryotic DYnamic\nprogramming Gene-finding ALgorithm). As fungi are eukaryotic organisms, this does\n5\nnot seem the best option, and I wonder how this can severely affect the results\nobtained. This is a major drawback of the manuscript.\nThank you for this critical comment which allows to better express our approach in\nthis matter.\nWe acknowledge that Prodigal was originally developed for prokaryotic gene\nprediction and is optimized for prokaryotic genomes. However, Tang et al. (2015)\nfound that Prodigal, along with other tools like GeneMarkS-T and TransDecoder, can\neffectively predict coding regions in eukaryotic transcripts, despite its primary design\nfor prokaryotes 20. Prodigal has demonstrated comparable performance to\nTransDecoder, especially in predicting continuous, intronless coding sequences and\nidentifying translation initiation sites in eukaryotic metatranscriptomic data 20.\nIn our study, we assembled the metatrascriptomic reads into conting and then used\nProdigal to predict protein functions in eukaryotic metatranscriptomic data from\ncontigs longer than 200 base pairs 5, 6, 20. To obtain the corresponding gene\nabundance in the metagenome, we mapped reads to gene categories derived from\nthe metatranscriptomic assembly using the BWA algorithm 21, and extracted the\ngene abunance of CAZyme and peptides. This approach enabled us to compare\nmetagenomic data with functional genes identified in the metatranscriptomic data,\nbypassing the complexities and potential biases of metagenome assembly,\nespecially given the predominance of prokaryotic sequences in metagenomes 5, 6, 17,\n22.\nLine 41. Why non-polar? Wouldn't be better to label this category as \"Atlantic\"??\nThanks for the comment. The term \"polar\" refers to regions of the Southern Ocean,\nwhich are closer to the Antarctic, whereas \"non-polar\" refers to regions of the Atlantic\nOcean that are farther from the Antarctic. This distinction helps to emphasize the\nenvironmental differences we are investigating, which is reasonable given the large\nrange of latitudes covered in our study.\ninteresting to explain these changes.\nWe have expanded the description of our results/outcomes accordingly. Please see\npage 2, lines 38-44:\n“We also found that fungal CAZyme genes is more abundant in non-polar regions\nbut peptidase genes are dominating in polar waters. In non-polar regions, distinct\nsize fractionation patterns were observed among fungal groups, with Basidiomycota\npredominantly contributing to total peptidase genes in the LF, whereas\nChytridiomycota exhibited a significant contribution in the SF. These findings\nunderscore the critical role of size fractionation in elucidating the ecological functions\nof marine fungi.”\nLine 93-100. The first sentence says that there is few data on carbohydrate and\nprotein degradation by marine fungi, and the second sentence says that this is the\nfirst report.\nSorry for the confusion. We have adjusted the description to reflect the research as a\n\" systematic analyses \" rather than making absolute claims. Please see page 4, line\n111.\nLines 155-159. I guess there are expected (mostly methodological) reasons to\nexplain the order of magnitude difference in the number of CAZY genes found in\nmetagenomes and metatranscriptomes. But these are not explained nor discussed.\nThanks for this comment, that also allows us to better explain our approach. In the\nrevised manuscripts, we have added sentences to explain the differences between\nthe number of CAZyme and peptidase genes found in metagenomes and\nmetatranscriptomes.\nPlease refer to page 6 in lines 172-176:\n7\n“In our study, we generated a total of 455.1 Gb of raw data from 42 metagenomes,\nwith each DNA library yielding an average of 10.8 Gb of raw data (Table S2). This\nenabled us to identify 943 fungal peptidase sequences and 1,320 CAZyme\nsequences (Table S4). A total of 1959,9 Gb of raw data were generated from 53\nmetatranscriptomes using magnetic Oligo(dT) beads to isolate poly(A)+ mRNA, with\nan average of 37.0 Gb raw data per sample (Table S3).”\nAnd lines 185-192:\n“The methodological differences—particularly the significantly greater sequencing\ndepth and the poly(A)+ enrichment in the metatranscriptomic approach—explain the\nhigher number of peptidase and CAZyme genes detected in the metatranscriptomes\ncompared to the metagenomes. Given the more comprehensive dataset provided by\nthe metatranscriptomic approach, our subsequent analyses will primarily focus on\nthe metatranscriptomic data to further explore the functional diversity and secretory\npotential of fungal peptidases and CAZymes.”\nLine 219-220. The groups dominating in peptidases genes (Chytridiomycota-\nBasidiomycota) are very different from the groups dominating the community\n(Ascomycota-Mucoromycota). While this is possible, it is a bit unexpected, and it\ncould also be due to methodological reasons (mostly related to database\ncompleteness for taxonomy). At any rate, I am surprised that the authors did not try\nto explain these very obvious differences.\nThanks for the comment. This phenomenon is indeed observed not only in peptidase\ngenes but also in CAZyme genes. In the revised manuscript, we have expanded our\ndiscussion to better address these differences. Please see lines 328-345:\n“Ascomycota dominated the total fungal genes (34.2%) and transcripts (67.1%), but\nonly contributed ~26% of fungal CAZyme genes and transcripts (Figures 1C vs. 3C\nand 4C). The total CAZyme genes and transcripts from Ascomycota and\nBasidiomycota were more abundant in non-polar environments compared to polar\nones (Figures 3C and 4C). In contrast, Chytridiomycota CAZyme genes and\ntranscripts were more prevalent in polar environments (28.7% - 44.5%) compared to\n8\nnon-polar ones (16.1% - 21.1%) (Figures 3C and 4C). A similar trend was observed\nin total fungal peptidases and secretory CAZyme encoding genes and transcripts\n(Figures 3A, 4A, 5B and S5C). This pattern reflects a taxonomic shift in ecological\nniches, where the proportion of fungal CAZymes from other groups (i.e., Ascomycota\nand Basidiomycota) increases in non-polar oceans compring to Chytridiomycota. In\naddition to their differences in cell size 23, 24, 25, 26, Ascomycota and Basidiomycota\nare known to have spore adaptations, which help them remain in the water column\nfor longer periods 27. Chytrids have been found in cold environments such as high-\narctic tundra soils 28, in soils under persistent snow packs 29, in high-mountain lakes\n30, and in sea ice and Arctic waters 31, 32, 33. The high relative abundance of the\nCAZyme gene and transcripts from chytrids in the polar ocean agrees well with their\nphysiological advantages for inhabiting aquatic ecosystems, including their mobility\nand capacity to parasite diatoms which are known to dominate marine phytoplankton\nin polar oceans 34, 35, 36.”\nLine 313. Results show that the most prevalent fungal CAZYme were GT. However,\nthere is no mention in the next paragraph on what they are doing (they do not seem\nto be directly involved in degradation) and why they are the most important. They are\njust ignored.\nIntroduction and discussion of GTs were included in lines 377-378 and lines 415-\n422:\n“Although GTs, responsible for glycosidic bond formation, constitute a high\nproportion of total CAZyme genes (62%) in polar oceans 37, 38, CBMs are more\nprevalent (41.2%) in the LF of non-polar regions, underscoring their critical role in\nsubstrate targeting (Figure 3D).”\n“Among the identified GT families, GT8 was the most abundant in polar\nenvironments compared to non-polar ones. GT8 enzymes are involved in\nglycoprotein folding quality control and cell wall biosynthesis 39. The active\nexpression of GT8 may influence fungal adaptation to environmental stress by\nregulating the composition and structure of the cell wall. In contrast, GT62 and\nGT68, which participate in the modification of glycan structures 40, were relatively\n9\nmore abundant in non-polar oceans (Figures 6 and S10). This distribution pattern is\nconsistent with that of GH128, GH72, and GH45, which utilize beta-glucan as a\nsubstrate (Figures 6 and S10).”\nLines 406. I do not see why detecting diverse fungal genes in the omics data\nsupports the conclusion that fungi play a \"profound and significant role\" in governing\nbiogeochemical cycles. This depends on how much fungi contribute to these cycles\n(as compared with other taxa), which cannot be derived from the presented data.\nWe have changed \"profound and significant role\" to “overlooked role”.\nLines 416-418. Comparing fungal peptidase and CAZyme genes against non-fungal\npeptidases/CAZYmes would be a first step to interpret the importance and role of\nfungi in marine ecosystems.\nThank you for this comment. We have now included this comparison in lines 210-216:\n“Notably, more than 13% of fungal CAZyme transcripts in both polar and non-polar\nwaters displayed secretory capability (Figure 1G), a rate significantly higher than <10%\nobserved in prokaryotes from open oceanic and deep-sea environments 2, 12. In\ncontrast, less than 0.5% of fungal peptides in both non-polar and polar waters showed\nsecretory capability (Figure 1E), compared to the 2 - 9% secretory capability found in\nprokaryotic peptides 2. These findings suggest that fungi exhibit a substantial capacity\nfor extracellular carbohydrate degradation, characteristic of a saprotrophic lifestyle 41.”\nLine 430-431. Here it appears temperature, but when this has been analyzed??\nPlease see line 237-239, where we mentioned that “funtional composition of fungal\nCAZyme transcripts were linked to temperature (Figure S3, mantel test, p<0.05).”\nLine 442- I guess it should be \"latitudinal\"\nLines 504-505. It is not very clear how the metagenomes were treated. I guess no\nassembly was attempted, right? Then read mapping to the transcripts, resulted in a\nlow number of genes found. Can you explain this, please (I guess most signal was\nprokaryotic), And how this could impact the results found?\nThank you for this insightful comment. The revised method can be found in lines\n585-591 of page 19:\n“To evaluate the relative abundance of genes in the metagenomic data reads from\neach metagenome were mapped to the CAZyme and peptidase gene categories\nderived from the metatranscriptomic assembly using the BWA algorithm (0.7.17) 21.\nThis approach allowed for a direct comparison of metagenomic data to the functional\ngenes identified in the metatranscriptomic data without the complexities and potential\nbiases associated with metagenome assembly, especially given the high proportion of\nprokaryotic sequences in the metagenomes.”\nWhile mapping reads directly to metatranscriptomic gene categories can effectively\nhighlight functionally relevant sequences, this approach may result in identifying a\nrelatively low number of genes from the metagenomes due to the predominance of\nprokaryotic sequences 42, 43. Consequently, there is a risk of underestimating the\ndiversity and abundance of eukaryotic CAZyme and peptidase genes in the\nmetagenomes. Additionally, genes present in the metagenome may not be\nexpressed in the metatranscriptome, introducing another layer of potential bias 44.\nFuture studies could benefit from integrating both assembly and read mapping\nstrategies to capture a more comprehensive view of the microbial community and its\nfunctional dynamics 45, 46, 47.\nFigure 1. Some of the graphs do not have the y-axis legend. Also, what is the x-axis\nof panel C? Are individua stations? How about water depth? This seems just a\ncompilation of data, with very little effort to convey a message to the reader.\nThanks for the comment. Modified as suggested. Please see the revised Figure 1.\nNow all phylum were included in the figure legend. “Others” was deleted in the\nrevised manuscript.\nFig. 3. Again, this figure includes a lot of categories, plots, and colors. Very, very\ncomplicated to follow. The same for Figure 4\nWe have revised Figures 3 and 4 to address the concerns about complexity. The\noriginal Figure 3 has been reorganized and divided into the new main Figure 3 and\nSupplementary Figure S4. Similarly, the original Figure 4 has been updated and\ndivided into the new main Figure 4 and Supplementary Figure S5.\nReviewer #2 (Remarks to the Author):\nWhilst more data on marine fungi are needed and I understand the exploration of the\npolar region is interesting (though not totally novel), I miss the significance of the\ncontribution to the field to warrant publication in Nature Communications or similar\nlevel journal. Similar work has already been conducted that is already in the\nestablished literature, including on marine fungal Cazymes and in the Atlantic region,\nfor example see work from Orsi Group. Some of these studies in the established\nliterature (particularly studies not from the author team) appear missing from the\nmanuscript.\nOrsi et al (2022) Carbon assimilating fungi from surface ocean to subseafloor\nrevealed by coupled phylogenetic and stable isotope analysis. The ISME Journal 16\n1245–1261.\nChrismas et al (2020) Depth-dependent mycoplankton glycoside hydrolase gene\nactivity in the open ocean—evidence from the Tara Oceans eukaryote\nmetatranscriptomes. The ISME Journal 14 2361–2365.\nBaltar et al (2021) Potential and expression of carbohydrate utilization by marine\nfungi in the global ocean. Microbiome 9 106.\nocean. Microbiome 10 143.\nThank you for your valuable feedback. We have now included all the citations\nmentioned by the reviewer.\nOrsi et al has indeed made significant contributions to the study of both soil and\nocean fungal biodiversity, and their work is crucial to our research. We would like to\nclarify that we have already cited several of Orsi et al.'s important publications in our\nstudy. In addition to the reference reviewer mentioned (Orsi et al., 2022), we have\ncited:\n1) Orsi WD, Biddle JF, Edgcomb V. Deep Sequencing of Subseafloor Eukaryotic\nrRNA Reveals Active Fungi across Marine Subsurface Provinces. PLOS ONE 8,\ne56335 (2013)\n2) Orsi WD, Edgcomb VP, Christman GD, Biddle JF. Gene expression in the deep\nbiosphere. Nature 499, 205-208 (2013)\n3) Orsi WD, Richards TA, Francis WR. Predicted microbial secretomes and their\ntarget substrates in marine sediment. Nat Microbiol 3, 32-37 (2018)\nThanks to the reviewer for recognizing the significance of Baltar et al. (2021) and\nBreyer et al. (2022), which are references from our lab that had already been cited\nin our original manuscript.\nThe study is largely descriptive, more a catalogue of genes/mRNA, greater attempt\nshould have been made to synthesise the work.\nWe gratefully thank the reviewer for the critical comments. We have addressed\nsimilar concerns raised by reviewers 1# and 3#, as detailed in our responses to their\ncomments. To better synthesize the major findings of this study, we present two key\nexamples:\ntranscripts in both metagenomes and metatranscriptomes. Interestingly, fungi\naccounted for >3% of the eukaryotic CAZyme transcripts but only 0.16% for\nproteases, suggesting a preferential role of pelagic fungi in carbohydrate degradation\nover proteolysis. This highlights the specialized ecological roles fungi play in organic\nmatter degradation in marine environments.\nSecondly, our research reveals significant functional and taxonomic divergence\namong pelagic fungi between polar and non-polar oceanic regions. Fungal CAZyme\ngenes were more abundant in non-polar regions, while peptidase genes dominated\nin polar waters. We also observed distinct size fractionation patterns, with\nBasidiomycota contributing more to peptidase genes in large size fractions, while\nChytridiomycota were prominent in small fractions in non-polar regions. These\nfindings underscore the ecological importance of size fractionation in fungal\ncommunities, despite only minor differences between size fractions in overall fungal\nabundance.\nOverall, our study provides new insights into the ecological and biogeochemical roles\nof pelagic fungi, complementing existing knowledge on marine microbial\ncommunities and emphasizing fungi's unique contributions to organic matter\ndegradation in different oceanic regions.\nThe use of DNA/RNA metagenomes and metatranscriptomes can only go so far.\nFunction could have been explored further with stable isotope analysis as the Orsi\nGroup have done (see Orsi et al (2022) Carbon assimilating fungi from surface\nocean to subseafloor revealed by coupled phylogenetic and stable isotope analysis.\nThe ISME Journal 16 1245–1261).\nWe completely agree that additional approaches, such as stable isotope analysis,\ncould further expand our understanding of oceanic fungi, particularly in their roles in\nbiochemical cycles. However, the scope of the study was to study the expression\nand role of pelagic fungia accross a large range of latitudes, known to exhibit marked\ncontrasting biogeochemical settings. In future studies, based on the findings of the\npresent study, it is now possible to target specific regions, where contrasts in fungal\n14\ngene expression were found, to conduct stable isotopes as well as other techniques,\nto further increase our knowledge on the role of pelagic fungi in organic matter\ndegradation. In response to this suggestion, we have added a sentence to the\ndiscussion emphasizing the importance of stable isotope analysis in revealing fungal\ninvolvement in carbon assimilation. Please see page 15 lines 475-478.\nThe presentation of the data in the current figures in not very useful.\nWe thank the reviewer for the critical comments. In response to the comments made\nby Reviewer 1#, we have intensively modified our main Figures and Supplementary\nFigures. Briefly, the original Figure 1 has been revised by deleting original Figure 1E\nand H. We moved panel 1B to the lower section. This new layout clarifies how fungi\ncontribute to eukaryotic diversity and activity. The previous Figure 3 has been\nreorganized into the current main Figure 3 and Supplementary Figure S4, while the\noriginal Figure 4 has been updated and divided into the new main Figure 4 and\nSupplementary Figure S5. Additionally, we have revised the main Figure 6, along with\nSupplementary Figures S3-S7, now labled as Supplementary Figures S6-S10, to\nensure consistency and improve the interpretability of the data. In the various\nsubplots across Figures S6 and S9, we ensured that the same fungi species are\nrepresented using the same color across all panels. This consistency helps readers\ntrack the contribution of specific species more easily. For the heatmaps depicting\npeptidase and CAZyme functions (Figures S7 and S10), we adopted a color scheme\nwith stronger contrast. We organized the samples by ocean regions and size\nfractions, which allows the heatmaps to effectively display the variation in peptidase\nand CAZyme functions across these categories. This structured grouping enhances\nthe clarity of functional differences between polar and non-polar oceans.\nWas any attempt made to explore differences across the Atlantic. A range of biomes\nwere sampled but not obvious differences.\nWe gratefully thank the reviewer for the critical comments. We did explore the\ndifferences across the Atlantic Ocean, but compared to the obvious difference\nbetween non-polar and polar environments, we did not find a significant difference\n15\nwithin the Atlantic Ocean. We included this part of the result in lines 231-233 and in\nthe Supplementary Materials, Figure S2.\nI can see a potential issue in the data analysis, interpretation and conclusions. The\nstudy is based on two cruises conducted in two different years. I cannot find any\ninformation the dates when samples were collected. Where the different cruises one\nAtlantic and one Arctic? How do you separate the effect of time?\nNow, in lines 514–515, detailed sampling dates have been incorporated as\nrecommended.\n“The samples were collected during the oceanographic research cruises ANTOM-I\n( 15 December - 15. Jannuary, 2020/2021) and ANTOM-II (January 23 - February 6,\n2022).”\nIn oceanographic research, it is often challenging to collect samples at the same\ntime across different locations and expeditions due to logistical constraints, even\nmore so when Antarctic research is performed. For instance, the Tara Oceans\nexpedition, which is the benchmark for multiple marine microbial studies used by\nmultitude of research groups worlwide, collected plankton samples covering multiple\nocceans over several years from 2009 to 2013 43, 48. Similarly, in our study, while the\ntwo cruises were conducted in different years, we carefully scheduled both in the\naustral summer season to minimize the influence of seasonal variations, particularly\ntemperature, on the microbial communities and their enzymatic activities. By\nsampling within the same season, we aim to reduce the confounding effects of\ntemporal variability, allowing a clearer focus on biogeographical patterns and\nfunctional diversity across different oceanic regions. While temporal variability\nremains a factor to consider, this seasonal coordination helps ensure that our\nfindings are robust in interpreting the biogeographical and ecological roles of marine\nfungi across large spatial scales.\nAll sequence data should be made available in a public database.\nNCBI under Bioproject number PRJNA1116066. The metatranscriptomic raw reads\nhave been deposited with the NCBI under Bioproject number PRJNA1115042.\nPlease see lines 604-607.\nReviewer #3 (Remarks to the Author):\nThe authors used multi-omics techniques (metagenomics and metatranscriptomics)\nfor high-resolution spatial analysis of functional fungal diversity and metabolic\npotential in the open Atlantic and Southern Ocean from subtropical to polar regions.\nUsing these techniques, the researchers successfully uncover the significant role of\nfungi in organic matter degradation, with a focus on their preferential role in\ncarbohydrate degradation. The differentiation between free-living and particle-\nattached communities, along with the high-resolution spatial analysis across diverse\nmarine regions, adds considerable depth to the study's findings.\nWe gratefully thank the reviewer for the positive remarks.\nThe introduction is comprehensive, with pertinent references cited, and clearly\narticulates the novelty and necessity of the study. The materials and methods\nsection are detailed and well-written. The results and discussion provide a\ncomprehensive and insightful analysis, the findings are presented in detail, with\nnuanced data interpretation, highlighting the study's rigor and depth. Although the\nauthors provided enough data to conclude this study, some questions and errors\nmust be addressed. Additionally, the discussion section could be expanded to\nimprove the clarity and depth of the interpretation of the findings.\nWe appreciate the reviewer’s detailed and constructive comments. We have\nthoroughly revised the manuscript in response to these suggestions. Below, we\noutline our responses and the corresponding changes made in the revised\nmanuscript.\n17\n1. How do the functional roles of the identified fungal taxa (Ascomycota,\nBasidiomycota, Mucoromycota, etc.) differ between free-living and particle-attached\ncommunities? Please thoroughly elaborate and include in result and discussion\nsection.\nThank you for the reviewer’s comments. We have identified differences in fungal\ntaxa associated with peptidases and CAZymes between polar and non-polar ocean\nregions at both the gene and transcript levels. Additionally, we change the definition\nof PA and FL to SF and LF to avoid the confusion with prokaryotic lifestyles as\ncomented by reviewer #1, in this way, we found distinctions between small and\nlarge fraction communities at the gene level, though these differences were not\nobserved in the metatranscriptomes. These findings, along with a summary of\nspecies differences in small and large size fraction communities and in polar versus\nnon-polar environments, are presented in Figure 7. We also compared these results\nwith functional species identified in marine environments from the Tara Oceans\nproject 5, 6. The manuscript has been revised accordingly in the Results and\nDiscussion sections.\nPlease see page 8 in lines 234-236:\n“Moreover, beyond regional disparities, genes encoding fungal peptidases and\nCAZymes also exhibited distinct differences between the SF and LF size fractions in\nthe non-polar ocean (Figures 2, A and B, pairwise adonis, p < 0.01).”\nLines 268-279:\n“The taxonomic shift was evident across different size fractions in non-polar\nenvironments, where the contributions of Basidiomycota and Chytridiomycota to total\npeptidase genes varied. Basidiomycota dominated the LF (>50%), while\nChytridiomycota contributed more to the SF (Figure 3A). The predominance of\nChytridiomycota in the SF aligns with their typically unicellular or simple multicellular\nmorphology 23, 24. Their smaller size and the production of motile zoospores likely\nfacilitate their presence in the SF, allowing them to effectively locate and potentially\nexploit smaller particulate matter and dissolved organic substrates once they find a\n18\nsuitable host 49. In contrast, Basidiomycota were more abundant in the LF (>3 µm),\nconsistent with their more complex, filamentous structure 25, 26. These results\nhighlight the critical role of size fractionation in understanding the ecological niches\nand functional roles of different fungal groups in marine environments.”\n2. How does the association with particles influence the functional roles and gene\nexpression profiles of fungi in marine environments?\nIn lines 136-141 of page 5, we have included information in the text to emphasize the\nimpact of size fraction on the function of marine fungi:\n“Our results show a higher relative abundance of fungal genes in the large size\nfraction (1.5 to 2.0%) compared to the small size fraction (1.0 to 1.5%) across both\npolar and non-polar regions, indicating that fungi prefer inhabitating on larger\naggregate environments such as marine snow and algal detritus 12, 50, 51, 52. These\n(micro)environments of particles provide greater nutrient availability than the ambient\nwater, supporting their roles in nutrient cycling and degradation processes.”\n3. What is the main reason for the higher proportion of peptidase genes in polar\nwater compared to non-polar water?\nDisscussion included in line 204-210 of page 7:\n“These spatial variations highlight that higher proportions of peptidases in polar waters\nlikely reflect fungi's adaptation to efficiently degrade proteins in cold, nutrient-limited\nenvironments 53, 54. Conversely, the increased capability of fungal CAZymes in non-\npolar regions reflects the higher availability of complex carbohydrates and warmer\ntemperatures, which favor the breakdown of polysaccharides and other carbohydrate-\nrich substrates, a primary function of CAZymes 55, 56.”\n4. What are the specific ecological roles of different fungal species in polar versus\nnon-polar regions?\nspecies in polar versus non-polar regions in lines 328-331:\n“Ascomycota dominated the total fungal genes (34.2%) and transcripts (67.1%), ,\nbut only contributed ~26% of fungal CAZyme genes and transcripts (Figures 1C vs. 3C\nand 4C). The total CAZyme genes and transcripts from Ascomycota and\nBasidiomycota were more abundant in non-polar environments compared to polar\nones (Figures 3C and 4C).”\nLines 333 - 339:\n“A similar trend was observed in total fungal peptidases and secretory CAZyme\nencoding genes and transcripts (Figures 3A, 4A, 5B and S5C). This pattern reflects a\ntaxonomic shift in ecological niches, where the proportion of fungal CAZymes from\nother groups (i.e., Ascomycota and Basidiomycota) increases in non-polar oceans\ncompring to Chytridiomycota. In addition to their differences in cell size 23, 24, 25, 26,\nAscomycota and Basidiomycota are known to have spore adaptations, which help\nthem remain in the water column for longer periods 27.”\nLines 341 - 345:\n“The high relative abundance of the CAZyme gene and transcripts from chytrids in\nthe polar ocean agrees well with their physiological advantages for inhabiting aquatic\necosystems, including their mobility and capacity to parasite diatoms which are\nknown to dominate marine phytoplankton in polar oceans 34, 35, 36.”\nAnd lines 361 - 364:\n“Our findings further confirm that fungi play active roles in specific functions that are\ninfluenced by environmental conditions in the ocean, thereby reinforcing the\nconcepts of niche specialization and resource utilization 57, 58, 59.”\nWe have also thoroughly discussed the specific ecological roles of various fungal\nspecies in polar and non-polar regions in the revised manuscript, incorporating\nfeedback from the other comments.\n20\n5. What are the ecological implications of the dominant presence and higher\nexpression of Serine peptidases (SP) by Chytridiomycota in polar oceans compared\nto Basidiomycota and Ascomycota in non-polar regions?\nDisscussion included in lines 296 – 307 of page 10:\n“In this study, the fungal composition specifically contributing to serine peptidases\naligns well with the high contributions of Basidiomycota and Ascomycota in non-polar\noceans, contrasted with the dominant role of Chytridiomycota in polar oceans. This\ntrend is consistently observed across both gene and transcript levels of total fungal\npeptidases (Figures 3A, 4A, 5B). The distribution patterns reflected ecological\ncompetition and resource partitioning among fungi. The high prevalence of\nChytridiomycota-related serine peptidase transcripts in polar regions indicates their\ncrucial role in breaking down polypeptides and proteins into smaller peptides or\namino acids at low temperatures. As climate change progresses, polar regions are\nwarming and experiencing shifts in nutrient availability and primary production 60, 61.\nThese changes could impact fungal community distribution and metabolism,\npotentially altering the balance of organic matter storage and cycling in these\nsensitive environments.”\n6. How does the functional diversity of fungal CAZymes, particularly the differences\nbetween glycoside hydrolases (GHs), carbohydrate-binding modules (CBMs), and\nglycosyltransferases (GTs), influence carbon cycling in polar versus non-polar\nmarine environments?\nDisscussion included in lines 384-386 of page 13 and lines 438-442 of page 14 :\n“The increased transcripts abundance of CBMs and GHs in non-polar environments,\ncompared to polar regions, confirms the greater availability of complex\ncarbohydrates in these areas 55, 56.”\n“Thus, the presence and transcript expression of fungal clades in specific functional\nroles is pivotal in shaping the diversity and abundance of CAZymes. Our findings\n21\nfurther reveal that distinct fungal clades dominate the production of different CAZyme\nfamilies (CBMs, GTs, and GHs), suggesting potential divergent strategies for\ncarbohydrate degradation and utilization by pelagic fungi in the ocean.”\n7. Why there is a vast difference in the number of metagenomics and\nmetatranscriptomics samples, it won't be same? Please justify.\nThank you for this comment. We have added a paragraph in the Materials and\nmethods section to clarify the difference in sample numbers. Please refer to lines\n557-562:\n“The primary reason for the difference in sample numbers (42 metagenomic vs. 53\nmetatranscriptomic) is due to logistical challenges during sample collection and our\nfocus on obtaining high-quality RNA for metatranscriptomic analyses. The limited\nseawater volume collected restricted the available biomass for both DNA and RNA\nextractions. Prioritizing the required RNA input (>700 ng) for metatranscriptomics, we\nhad fewer samples available for metagenomic sequencing (42 DNA samples).”\n8. When contradictory results are observed in a manuscript, authors should provide\nthe most probable reason and possible mechanism to aid understanding of the\nfindings.\nWe thoroughly revised our manuscripts as suggested.\n9. Please reframe the sentence from lines 31 to 36 of the abstract because it is too\nlong.\nThe sentence was rephrased as follows:\n“In this study, we employed metagenomic and metatranscriptomic approaches, to\nexplore the fungi mediated organic matter degradation in the sunlit ocean. Samples\ncollected from subtropical Atlantic Ocean (non-polar) to Southern Ocean (polar\nregions) represent spatial differences and size fracnation between small (0.2 - 3 µm,\ncommunities.”\n10. In line number 195 “(Figure S1C)).” Remove one “)”.\nCorrected accordingly.\n11. Please include a few lines in the appropriate section explaining why the\nmetagenomic and metatranscriptomics analysis results differ.\nWe have added sentences addressing this point. Please refer to lines 158-162:\n“The changes between metagenomic and metatranscriptomic results suggest that\nthe metabolic activity of marine fungi may vary among taxonomic groups, where\nfungal species with higher contributions to the transcripts pool may play a more\nprofound role in marine biogeochemical cycles because the expression level of\ntranscripts reflects metabolic response to the changing environment.”\nLines 220-223:\n“These findings indicate clear disparities in the genomic potential (genes) and\nexpression profiles (transcripts) of fungi involved in carbohydrate and protein\ndegradation, as has been recently shown for micobial communities from marine and\nsoil ecosystems as well as in hunman gut 44, confirming the importance of studying\nfungal transcripts in addition to genes.”\nIn addition, the original introduction includes a sentence in lines 99-102 that explains\nthe rationale behind using both metagenomic and metatranscriptomic approaches:\n“We investigated both the metabolic potential (metagenomics) and the gene\nexpression (metatranscriptomics) of pelagic fungi since DNA-based results might\ndiffer dramatically from the actual transcription and function of microorganisms in the\nenvironment 44”. This statement establishes the basis for comparing genomic\npotential with transcriptional activity.\n23\n12. Please reframe the sentence in lines 65-67, as \"such as\" was used twice and\nshould be replaced with another word.\nWe have replaced the first instance of 'such as' with 'including' to avoid repetition in\nthe sentence.\n13. A thorough proofreading would be necessary to avoid grammatical and\ntypographical errors throughout the manuscript.\nReviesed thoroughly as suggested.\n14. No information is provided about how to download the sequence data in a public\ndatabase such as NCBI or EMBL-EBI.\nDone as suggested. The metagenomic raw reads have been deposited with the\nNCBI under Bioproject number PRJNA1116066. The metatranscriptomic raw reads\nhave been deposited with the NCBI under Bioproject number PRJNA1115042.\nPlease see lines 604-607.\nReferences\n1. Zhao Z, Amano C, Reinthaler T, Orellana MV, Herndl GJ. 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J Fungi (Basel) 8, (2022).\n28\n53. Kasana RC. Proteases from Psychrotrophs: An Overview. Critical Reviews in\nMicrobiology 36, 134-145 (2010).\n54. Bruno S, Coppola D, di Prisco G, Giordano D, Verde C. Enzymes from Marine\nPolar Regions and Their Biotechnological Applications. Marine Drugs 17, 544\n(2019).\n55. Arnosti C, Steen AD, Ziervogel K, Ghobrial S, Jeffrey WH. Latitudinal Gradients\nin Degradation of Marine Dissolved Organic Carbon. PLOS ONE 6, e28900\n(2011).\n56. Priest T, Vidal-Melgosa S, Hehemann J-H, Amann R, Fuchs BM.\nCarbohydrates and carbohydrate degradation gene abundance and\ntranscription in Atlantic waters of the Arctic. ISME Communications 3, 130\n(2023).\n57. Louca S, et al. Function and functional redundancy in microbial systems. Nature\nEcology & Evolution 2, 936-943 (2018).\n58. Nguyen TTH, Myrold DD, Mueller RS. Distributions of Extracellular Peptidases\nAcross Prokaryotic Genomes Reflect Phylogeny and Habitat. Front Microbiol\n10, 413 (2019).\n59. Muszewska A, Stepniewska-Dziubinska MM, Steczkiewicz K, Pawlowska J,\nDziedzic A, Ginalski K. Fungal lifestyle reflected in serine protease repertoire.\nSci Rep 7, 9147 (2017).\n60. Willis MD, et al. Polar oceans and sea ice in a changing climate. Elementa:\nScience of the Anthropocene 11, (2023).\n61. Grau O, Geml J, Pérez-Haase A, Ninot JM, Semenova-Nelsen TA, Peñuelas J.\nAbrupt changes in the composition and function of fungal communities along\nan environmental gradient in the high Arctic. Molecular Ecology 26, 4798-4810\n(2017).\nfeedback. We are particularly grateful for the opportunity to address the reviewers' concerns\nand submit a revised version of our manuscript.\nWe have carefully considered all the comments raised by the three reviewers. While we note\nthat Reviewer #3 expressed overall enthusiasm for the study, we also recognize and appreciate\nthe critical feedback from Reviewers #1 and #2, particularly regarding the bioinformatics\nanalysis and the need for further validation using additional data. In response, we have\nsubstantially expanded our analyses and revised the manuscript accordingly to strengthen the\nrobustness and clarity of our conclusions.\nBelow, we provide a detailed point-by-point response to each of the reviewers' comments. In\nour responses, references to the revised manuscript are provided with corresponding line\nnumbers, and all changes are highlighted in yellow for ease of review.\nReviewer #1 (Remarks to the Author):\nThis is the second time I analyze this manuscript. In my first review I raise a set of\nmethodological questions (bioinformatic tools used) and formal questions (how data is\npresented). Now I have carefully read the answers to my previous questions and although I\nrecognize that the manuscript seems to be improved, I am not completely satisfied with all\nthe answers.\nWe appreciate the positive comment of the reviewer about the improvement of the second\nversion. In response to the reviewer’s comments, we have now carefully addressed each point\nraised in the initial and second reviews, particularly regarding methodological and formal\naspects of the study. In brief, now we have applied the exact methodology suggested by the\nreviewer, and compared it to our previous methodology, reaching very similar results,\nsupporting the strenght of our study. So we appreaciate also the suggestion of the reviewer\nconcerning this methodological aspect. We have provided detailed explanations and made\nsubstantial revisions to the manuscript. Below, we outline our responses to the specific\nconcerns raised, and we believe that these revisions meet the reviewer’s expectations.\nOne substantial criticism still remains, why using a program designed for gene prediction in\nprokaryotes to perform gene prediction in eukaryotes. Eukaryotic genes have introns and\nalternative splicing, which make their prediction substantially different than that of\nprokaryotic genes. Programs for predicting eukaryotic genes from metagenomic or\nmetatranscriptomic data already exist, such as MetaEuk and others, and these should be\nused together with a complete reference database of eukaryotes. Perhaps the predictions\nwill not be extremelly different, but the authors should use the best tools available.\nIn our initial response, we explained our rationale for using Prodigal instead of MetaEuk for\neukaryotic gene prediction, based on two key considerations. First, several gene prediction\ntools, such as GeneMarkS-T and TransDecoder, have demonstrated robust performance in\npredicting genes from eukaryotic transcriptomic data, particularly for intronless or minimally\nspliced sequences. While these tools were originally designed for prokaryotic gene prediction,\ntheir algorithms are adaptable to eukaryotic data due to shared features in translation initiation,\nsuch as the use of start codons (AUG) and open reading frame (ORF) identification. Second,\nin our case, Prodigal was employed to predict protein-coding sequences from eukaryotic\nmetatranscriptomic data, which are typically continuous and intronless, using contigs longer\nthan 200 base pairs 1, 2, 3. To link metagenomic data to functional annotations, we mapped gene\nabundances from the metagenome to gene categories derived from the metatranscriptomic\nassembly using the BWA algorithm 4. This approach allowed us to extract abundances of\nCAZymes and peptidases while avoiding the complexities and potential biases associated with\nmetagenome assembly, particularly given the dominance of prokaryotic sequences in\nmetagenomic datasets 2, 3, 5, 6.\nNevertheless, in the current revision, we have addressed the reviewer’s concern by re-analyzing\nthe data using MetaEuk, a tool specifically designed for eukaryotic gene prediction. MetaEuk\nwas run against a comprehensive protein reference database comprising approximately 88\nmillion curated sequences from three primary sources: the MERC dataset (derived from Tara\nOceans eukaryotic metatranscriptomes), the Marine Microbial Eukaryote Transcriptome\nSequencing Project (MMETSP), and the Uniclust50 database\n( https://wwwuser.gwdg.de/~compbiol/metaeuk/2019_11/)7. All subsequent steps, including\npeptidase and CAZyme functional annotation and taxonomic assignment, were re-performed\nbased on MetaEuk-predicted genes.\nConsistent with both our original findings and the reviewer's expectations, the re-analysis\nconfirmed that our core conclusions remain robust. Specifically, the functional and taxonomic\ndistributions of total genes/transcripts—as well as peptidase- and CAZyme-associated fungi—\nshowed minimal changes. The taxonomic patterns of fungal peptidases and CAZymes\nconsistently differed from those of overall fungal genes/transcripts. Ascomycota,\nBasidiomycota, and Chytridiomycota were the primary contributors to peptidase and CAZyme\ngenes, with Mucoromycota additionally prominent in CAZyme production (Additional Figure\n2).\nTo facilitate a direct comparison, we have included two representative sets of comparative\nanalyses in this response letter:\nAdditional Figure 1. Taxonomic assignment comparisons of total fungal community\ncomposition in the metagenome and metatranscriptome based on the two prediction methods\n(original Fig.1 vs. new Fig. 1)\nAdditional Figure 2. Comparative analysis of functional annotations: Prodigal vs. MetaEuk\nfor peptidase and CAZyme transcript predictions (original Fig. 4 vs. new part of Fig. 3)\nAnother concern is about size fractionation. Initially samples were divided as free-living and\nparticle attached, ignoring the fact that fungi can cover a wide spectra of cell sizes. So, in the\nfraction >3 µm there could be small or large fungi attached to particles or simply fungi larger\nthan 3 µm (for instance making hypha). This has been assumed in some parts (i.e. lines 268-\n279), but in other parts the assumption that fungi on the >3 µm are particle attached still\nremains (i.e. lines 117-120, 136-139). The reality can include the two aspects, but this needs\nto be explicitly explained.\nWe appreciate the reviewer’s insightful comment regarding the complexity of size fractionation\nand its implications for fungal ecology. We agree that the distinction between large size (>3\nµm) and small size (0.2–3 µm) fractions may not fully capture the diversity of fungal lifestyles,\nas fungal cells and structures (e.g., hyphae) can span a wide size range and exhibit varied\necological behaviours.\nIn our revised manuscript, we have explicitly addressed this issue by clarifying that the large\nsize fraction (>3 µm) may include both fungi associated with particles and larger fungal cells\nor structures (e.g., hyphae) that are not necessarily particle-bound. We have modified the text\nin\nthe text in lines 121- 124 (originally lines 117-120) and 146-151 (originally lines 136-139) to reflect\nthis nuance, emphasizing that our size fractionation approach captures a mixed population\nrather than exclusively particle-associated fungi. Additionally, we have expanded the\ndiscussion in lines 250-265 (originally 268-279) and lines 419-426.\nFurthermore, our updated analyses reveal minimal functional and taxonomic differences\nbetween the large and small size fractions. This lack of significant differences can be attributed\nto the morphological plasticity and ecological adaptability of marine fungi. Many fungal taxa\nare capable of existing in both small (e.g., yeast cells, spores, or hyphal fragments) and large\n(e.g., hyphae or particle-associated aggregates) forms, depending on environmental conditions\nand life cycle stages. This morphological flexibility likely results in overlapping functional\nprofiles across size fractions, as the same fungal species may contribute to similar ecological\nprocesses (e.g., organic matter degradation) regardless of their size-based categorization.\nThere was the fact that the taxonomic groups sometimes were against the initial\nexpectations. A sentence in the manuscript says \" Previous research primarily documented\nDikarya\", and here other groups were found dominating gene expression.\nFor instance, the majority of fungal peptidases derive from Chytridiomycota, both in Atlantic and Antarctic waters. Also, the\ndominance of some groups does not match the dominance of genes expressed. My point was,\nhow much of these inconsistencies are due to inappropriate taxonomic classification of the\nretrieved transcripts because incomplete reference genomic resources? This particular concern\nhas not been addressed.\nWe thank the reviewer for raising this point again regarding the taxonomic distribution of\nfungal genes and transcripts, as well as the potential impact of incomplete reference genomic\nresources on taxonomic classification. To address this concern, we have conducted a re-\nanalysis of taxonomic assignments using multiple reference databases.\nWhen comparing taxonomic assignments between the MERC-MMETSP-Uniclust50 database\n(https://wwwuser.gwdguser.de/~compbiol/metaeuk/2019_11/) and the NCBI-nr database\n(release date: April 2023), we found that 86.7% (877/1,011) of the taxa identified by the\nMERC-MMETSP-Uniclust50 database were also present in the NCBI nr database. This high\ndegree of overlap indicates strong support for the majority of taxa identified by the MERC-\nMMETSP-Uniclust50 database within the more comprehensive NCBI-nr database (Additional\nFigure 3).\nAdditional Figure 3. Overlap and taxonomic consistency between the MERC-MMETSP-Uniclust50\nand NCBI nr databases. (A) Majority (86.7%) of taxa identified by the MERC-MMETSP-Uniclust50\ndatabase are also present in the NCBI nr database. (B) Taxonomic classification at the phylum level\nshows high consistency between the two databases.\nThe lower number of taxa identified by the MERC-MMETSP-Uniclust50 database can be\nattributed to several factors. First, lack of taxonomic annotations in the MERC dataset. The\nmajority (77%) of MetaEuk predictions were based on homologies to the MERC dataset, which\nlacks taxonomic annotations. When we queried the MetaEuk marine protein collection against\nthe Uniclust90 and MMETSP datasets, approximately 49% of the predictions could not be\nassigned any taxonomy, significantly reducing the number of identifiable taxa 7. Second,\ncompared to the NCBI-nr database, the MERC-MMETSP-Uniclust50 database is more\nspecialized and may not capture the full taxonomic diversity present in marine environments.\nThis limitation is further compounded by the fact that the MMETSP and Uniclust90 datasets,\nalthough annotated using NCBI taxonomy, are themselves subsets of the broader NCBI-nr\ndatabase.\nTherefore, we decided to use the NCBI-nr database to assign fungal taxonomy to the predicted\nfungal contigs. To enhance the reliability of taxonomic classification and minimize false-\npositive annotations, we employed the Last Common Ancestor (LCA) algorithm implemented\nin DIAMOND 8. This approach assigns an NCBI taxonomic identifier (TaxID) based on the\nlast common ancestor of all hits whose alignment scores fall within 10% of the best hit score,\nusing the NCBI-nr database (release date: April 2023) as the reference. By integrating multiple\nhigh-scoring matches, the LCA algorithm reduces the risk of erroneous annotations and\nprovides a more accurate representation of taxonomic diversity.\nThe Methods section has now been revised; please see lines 559 and 573-593.\nOur new results confirm that Ascomycota and Basidiomycota (Dikarya) remain the dominant\nfungal phyla in terms of overall gene and transcript expression across both polar and non-polar\nocceans (Fig. 1G). However, the relative contributions of these groups vary depending on the\nfunctional category (CAZymes vs. peptidases) and the oceanic region (Figs. 3, S3). For\nexample, while Dikarya dominate CAZyme expression in non-polar regions, Chytridiomycota\nplay a important role in polar regions (Fig. 3E). Similarly, Chytridiomycota contribute\nsubstantially to the expression of key peptidase families, including serine peptidases and\nmetallo peptidases in both Atlantic and Southern Ocean waters (Fig. S6). These findings\nsuggest that the observed taxonomic patterns are not solely due to incomplete reference\ngenomic resources but rather reflect genuine ecological and functional differences among\nfungal groups. As we mention in the manuscript, the increased importance of Chytridiomycota\nin polar regions, for instance, may be linked to their adaptation to cold environments and their\nability to degrade specific substrates (e.g., chitin).\nRegarding the presentation of the data, I see that there has been an effort to simplify a bit\nsome of the most complex figures, which I appreciate. Still, I do not see and effort to\ncondense the results to convey clear messages from the figures.\nIn response to the reviewer’s comment, we have thoroughly revised all figures in the\nmanuscript to further improve clarity and ensure that each figure conveys a clear and concise\nmessage. We hope that these revisions address the reviewer’s concerns and enhance the overall\nreadability and impact of our results.\nFinally, the text is very long and convoluted, and it is not clear how it is organized, with\napparently lot of redundancies that are difficult to completely follow. For instance, I note here\nthe second and third headings of the Results and Discussion section, which seem very similar:\n2) Biogeography and functional diversity of fungal peptidases and CAZymes, 3) Taxonomic\naffiliation and functional diversity of fungal peptidases genes and transcripts.\nWe appreciate the point raised by the reviewer here, and after going back to it we agree that\nthe text could have been written more clearly. In response to the reviewer’s comment, we have\nthoroughly reorganized the manuscript to improve its structure and flow. Specifically, we have\nstreamlined the text, reducing unnecessary details and we have also added subheadings to\nenhance readability and merged related sections to eliminate redundancies. For example, in the\nsections of Results and discussion, we added subheadings:\nUbiquitous fungal proteinases and CAZymes in the ocean\nLatitudinal and temperature-driven variations in geographic clustering patterns of marine\nfungal peptidases and CAZymes\nConserved of extracellular enzymatic strategies for protein and carbohydrate degradation in\nmarine fungal communities\nTaxonomic shift in pelagic fungi linked to functional changes in peptidases and CAZymes\nCarbohydrate degradation and specific utilization strategies in pelagic fungi differ between\npolar and non-polar oceans\nWe hope that these adjustments greatly improve the overall readability of the manuscript.\nReviewer #2 (Remarks to the Author):\nGuo et al and Baltar present an apparently revised manuscript. Despite efforts to make\nchanges, which are much appreciated, fundamental concerns remain the same.\nThis is interesting work but builds on several previous similar studies including those from one\nof the lead authors. This has not changed. I still do not see a genuine Nature Communications\nlevel step change in this work beyond what have been previously conducted e.g. see Baltar’s\nprevious papers and from Orsi et al.\nWe appreciate the opportunity to clarify how our study advances beyond previous work,\nincluding those by Chrismas, Baltar and Orsi et al. While we acknowledge the groundbreaking\ncontributions of Chrismas’s, Baltar’s and Orsi et al.’s work, our study fills a critical gap in\nunderstanding fungal contributions to marine biogeochemical cycles. Building on foundational\nresearch in marine microbial ecology from our community, we firmly believe our study\nprovides several novel and significant insights that justify its publication in Nature\nCommunications. First, unlike previous studies that primarily focused on bacterial and archaeal\ncommunities, our work provides a comprehensive exploration of fungal functional roles in\nmarine organic matter degradation. This represents a significant expansion of the scope of\nmarine microbial ecology research, since the data driven articles on marine pelagic fungi are\nextremely limited. Although there are only a handful of articles on this topic, this is not the first\nstudy on fungal functional omics on the ocean; however, it is the first study to systematically\ninvestigate and characterize the functional diversity of pelagic fungi in the Southern Ocean\n(and in any polar ocean). Just to put it into perspective, it might be a good excercise to imagine,\nif we would be focusing on marine bacteria, if we would say that this is the first study to\ncharacterize the functional role of bacteria in the Southern Ocean or in any polar ocean: it\nwould be reasonable to assume that this would be worth of a paper in Nature Communications.\nFurthermore, it is worth mentioning, that the level of detail of the analyses and findings,\nspecifically on the fungal CAZymes families is unprecedented, providing a new vision on the\nspecific strategy used by pelagic fungi in the use of specific enzymes in the ocean. If all this is\nnot already relevant enough, while doing this first characterization of the functional role of\npolar pelagic fungi we also do an extensive comparisson to non-polar regions, placing our\nfindings into a global settings and thereby discovering some novel environmental drivers and\nsusceptibilities of the functional role of oceanic pelagic fungi. Given the sensitivity of polar\necosystems to climate change, our findings provide critical insights into how warming may\nimpact marine fungal ecology and their functional contributions to biogeochemical cycles.\nThe study as presented in the revised manuscript remains descriptive and more a catalogue\nof genes/mRNA. More is needed to this level of manuscript including validating process\nmeasurements e.g. see Orsi et al 2022 ISME Journal 16 1245-1261. The study still\noverinterprets DNA/RNA metagenome and metatranscriptome data. The lack of other\nevidence (e.g. stable isotope analysis) continues to limit the scope and only shows what has\nbeen shown before.\nWe thank the reviewer for their constructive feedback and for pointing us to the relevant study\nby Orsi et al. (2022, ISME Journal). The study by Orsi et al. (2022) exemplifies how integrating\nITS and 16S rRNA amplicon sequencing with metatranscriptomic data and DNA-SIP based\ngenome-centric metagenomics can provide a comprehensive understanding of microbial\nactivity and nutrient cycling in marine environments. Indeed, the Orsi lab has consistently\ndemonstrated the power of integrating multiple methodologies, including SIP, to validate\nfunctional predictions and elucidate mechanistic insights across a wide range of studies (e.g.,\nVuillemin et al., 2022, Trejos-Espeleta et al., 2024) 9, 10. This approach has set a high standard\nfor linking microbial identity to function in complex ecosystems. While we agree that process\nmeasurements, such as stable isotope analysis, can provide valuable insights into microbial\nactivity, do to their nature, these tools are usually applied to a limited number of samples (and\nusually more devoted to more local studies). The SIP results from Orsi et. al 2022 demonstrated\ncorrelated relationships bwtween fungal CAZyme gene expression (metatranscriptome) and\nthe 13C incorporation, this results provide the support to our finding that metatranscriptomic\ndata of CAZymes and peptidase can be used as maker for substrate utilization. We addressed\nthis link in lines 450-456. However, SIP-based experiments are not very feasible for large scale\nsampling due to cruise logistic limitations. For instance, in the Orsi et al. 2022 article, they\ncovered 5 stations. In contrast, our study is primarily focused on leveraging metagenomic and\nmetatranscriptomic approaches to explore the potential functional roles of fungi in marine\norganic matter degradation on a large scale. In our study we covered more than 11000\nkilometers and 25 stations. We believe that this foundational work is an important step toward\nidentifying key fungal players and their potential ecological roles, which can guide future\nhypothesis-driven research incorporating process measurements. In fact, the current validity of\nour approach is clearly justified by the many recent high-impact publication following these\napproaches to advance our understanding of microbial ecology, in the absence of so-called\nadditional experimental validation: for instance, only in Nature Communications in 2024, is\npossible to find many examples such as Cohen et al., 2024; Zhao et al., 2024, Becsei et al.,\n2024 6, 11, 12. Also the cruise sampling happend in 2020 and 2021, by that time, we cannot\nforsee the methodological advances.\nNevertheless, in the revised manuscript, we have taken care to avoid overinterpretation of the\ndata and have explicitly discussed the limitations of our approach and have acknowledged that\nfurther validation (e.g., through stable isotope analysis or targeted experiments) is\nrecommeneted to fully elucidate the mechanisms underlying fungal contributions to marine\nbiogeochemical cycles. Please see lines 498-501.\nKey similar and previous studies remain missing from the revised manuscript. Irrespective of\nthe reasons for omitting previous studies (strategic or carelessness) it is not acceptable to\nignore previous similar/same work in this way. This is disappointing for any journal but\nespecially so for a leading journal such as Nature Communications.\nWe apologize for any oversight. We would like to clarify that it was not our intention to\noverlook any important prior research. In the revised manuscript, we had included references\nto several key studies, such as those by Orsi et al., to provide a comprehensive context for our\nwork. To be honest, we are not aware of any other study that is missing. If the reviewer could\nbe more specific and mention which specific study is the one missing, instead of just assuming\nthat we are omitting to cite papers for ‘strategic or careleness’ reasons, we would be more than\nhappy to include those papers in the manuscript. In fact, we would greatly appreciate it if the\nspecific studies could be pointed out. We are committed to ensuring that our manuscript fully\nacknowledges and builds upon the relevant body of prior work, and we will gladly incorporate\nany additional references as needed.\nReviewer #3 (Remarks to the Author):\nI appreciate the authors efforts in addressing my previous comments and revising the\nmanuscript accordingly. The authors have effectively addressed the scientific concerns raised\nin the initial review, improving the overall structure and depth of the study. The\nrepresentation and discussion of the data now offer a more thorough and comprehensive\nunderstanding of the observations and findings.\nHowever, before the manuscript can be accepted for publication, I would strongly\nrecommend a final round of careful proofreading. The authors should check for any remaining\ngrammatical errors and ensure accuracy in tense usage throughout the text. Consistency in\nlanguage is crucial for maintaining the scientific integrity and readability of the paper. I also\nsuggest that the authors confirm all technical terms are used correctly to avoid any potential\nmisinterpretation of the findings.\nOverall, I am pleased with the improvements made in this revised manuscript, and believe\nthat it offers valuable insights into fungal-mediated organic matter degradation in marine\nenvironments and contributes significantly to the field of oceanic fungal ecology, making it\nsuitable for publication in Nature Communications.\nWe honestly appreciate the positive feedback and constructive suggestions of the reviewer; we\nhave carefully addressed the remaining language and technical issues in this revised version.\nReferences\n1. Tang S, Lomsadze A, Borodovsky M. Identification of protein coding regions in RNA\ntranscripts. Nucleic Acids Res 43, e78 (2015).\n2. Baltar F, Zhao Z, Herndl GJ. Potential and expression of carbohydrate utilization by marine\nfungi in the global ocean. Microbiome 9, 106 (2021).\n3. Breyer E, Zhao Z, Herndl GJ, Baltar F. Global contribution of pelagic fungi to protein\ndegradation in the ocean. Microbiome 10, 143 (2022).\n4. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.\nBioinformatics 25, 1754-1760 (2009).\n5. Carradec Q, et al. A global ocean atlas of eukaryotic genes. Nature Communications 9, 373\n(2018).\n6. Zhao Z, Amano C, Reinthaler T, Baltar F, Orellana MV, Herndl GJ. Metaproteomic analysis\ndecodes trophic interactions of microorganisms in the dark ocean. Nature Communications\n15, 6411 (2024).\n7. Levy Karin E, Mirdita M, Söding J. MetaEuk—sensitive, high-throughput gene discovery,\nand annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020).\n8. Belliardo C, et al. Improvement of eukaryotic protein predictions from soil metagenomes. Sci\nData 9, 311 (2022).\n9. Trejos-Espeleta JC, Marin-Jaramillo JP, Schmidt SK, Sommers P, Bradley JA, Orsi WD.\nPrincipal role of fungi in soil carbon stabilization during early pedogenesis in the high Arctic.\nProceedings of the National Academy of Sciences 121, e2402689121 (2024).\n10. Vuillemin A, Coskun Ömer K, Orsi William D. Microbial Activities and Selection from\nSurface Ocean to Subseafloor on the Namibian Continental Shelf. Applied and Environmental\nMicrobiology 88, e00216-00222 (2022).\n11. Becsei Á, et al. Time-series sewage metagenomics distinguishes seasonal, human-derived and\nenvironmental microbial communities potentially allowing source-attributed surveillance.\nNature Communications 15, 7551 (2024).\n12. Cohen NR, et al. Microeukaryote metabolism across the western North Atlantic Ocean\nrevealed through autonomous underwater profiling. Nature Communications 15, 7325 (2024)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Point-by-point response to the Reviewer`s comments\nWe greatly appreciate the Nature Communications’ reviewers for their comments\nthat are crucial for improving and refining this manuscript. We include this in our\nAcknowledgement section. We have addressed and incorporated all the reviewers’\nsuggestions. Below, we detail our responses to each of the reviewers’ comments, as\nwell as the actions taken in response to the concerns. In our response, we refer to\nthe line numbering of the revised version in which modified text is highlighted in\nyellow.\nReviewer #1:\nThis paper reports the analysis of a set of metagenomes and metatranscriptomes\ntaken in two cruises in the Atlantic and Southern Oceans, to identify the presence\nand expression of carbohydrate-degrading (CAZYmes) and peptidase genes from\nmarine fungi. The main idea is to develop a better understanding of the abundance\nand function of fungi in marine systems and to highlight functional differences in\ndifferent regions. The paper presents a substantial amount of work, resulting in many\nplots and figures, but the obtained results are not properly digested, so it is very\ndifficult to extract a clear picture of the data. Moreover, some of the results are\nunexpected and little discussed, and an important step of the bioinformatic pipeline is\ninappropriate, as a tool for predicting prokaryotic genes has been used to find fungal\ngenes.\nThe senior author of this paper has many publications on marine fungi, in some\ncases also using metagenomes and metatranscriptomes. In the introduction one\nexpects to learn what has been done before, and how this paper contributes with\nnew information.\nWe sincerely appreciate the thoughtful comments and detailed review of our\nmanuscript and data. Thank you also for recognizing the senior author's prior\ncontributions to marine fungal research. We appreciate your suggestion to provide a\ncomprehensive introduction that situates our study within the broader scientific\n1\ncontext. In response, we have expanded the introduction to emphasize existing\nresearch on the diversity, activity, and functional potential of bacteria across different\nsize fractions. This enhanced background highlights a research gap: the lack of\ncomprehensive studies focusing on fungal communities within smaller size fractions.\nOur study addresses this gap by exploring how biogeographical differences between\npolar and non-polar oceans, as well as size fractions, influence the composition and\nactivities of oceanic fungal communities, thereby offering new insights to the field.\nPlease refer to page 4, lines 86–96 for further details, where the answers to the next\ncomment are also addressed.\nFor each station there are two size fraction analyzed, one including all organisms\n(>0.2 µm) and a second including all organisms except the very small ones (>3 µm).\nI am surprised by the way the authors label these two samples, as there are many\norganisms in common and, most importantly, the size range of fungi does not need\nto be exclusively below 3 µm. The definition of free-living and particle-attached\nsomewhat derives from the prokaryotic literature, but here the targets are not\nprokaryotes but fungi, that may cover a much wider range of sizes. So, I find this\nlabelling misleading.\nThank you very much for this insightful comment. We used this size fractionation\napproach to contextualize our results with previous studies on the size fractionation\nof pelagic microbes, which primarily focus on prokaryotes, as fungi remain largely\nunderstudied in the pelagic ocean. In response to comments of Reviewer #3, and to\navoid confusion with the terminology commonly used in prokaryotic studies, we have\nrelabeled the sample fractions as small size (0.2-3 µm, SF) and large size (>3 µm,\nLF) fractions throughout manuscript and Figures.\nWe have expanded the introduction to better explain this point. Please refer to page\n4, lines 86-96:\n“The size of cells and of particle is crucial for the adaptability of pelagic prokaryotes to\nchanges in their microenvironment and nutrient conditions 1, 2, 3, 4, 5, 6. Typically,\nplanktonic microorganisms are divided into free-living (FL) and particle-attached (PA)\n2\ncommunities 7. PA pelagic prokaryotes frequently develop into dense clusters\ncharacterized by high extracellular enzyme activity 8. In contrast, FL microorganisms\nwith small genomes are optimized for environments with low substrate availability, and\nthey tend to express membrane transporter genes at high levels 3, 9, 10. Given these\ndifferences, it is likely that the abundance and functional diversity of oceanic fungal\ntaxa also differ between various size fractions. Previous research has indeed indicated\nthat size fractions influence the composition of microbial eukaryote communities 11.\nHowever, there remains a lack of comprehensive studies focusing on the fungal\ncomponent, especially in smaller size fractions.”\nAnd page 5, lines 117-121:\n“To fill the knowledge gap and to distinguish fungi colonizing particles of varying\nsizes—which might offer different micro-environments and nutrient availability—we\nanalyzed two size fractions at each station: a small (0.2-3 µm, SF) and a large (>3\nµm, LF) size fraction. We also explore the principal environmental factors influencing\nthese fungal functional roles.”\nThe data presentation is largely based on primary data, with many different\ncategories (metaG/metaT; polar/non-polar, peptidase/CAZy genes; total/secretory\nenzymes) that are displayed together, being extremely difficult to follow the\nmessage. This would need some kind of processing of the data. Moreover, when\npresenting the data, the authors should first present a general overview of what they\nfound and then focus in the particular differences among the categories. Right now,\nall the explanation is based in these differences, being extremely difficult to grasp the\nmessage.\nTo address the concern about clarity of data presentation, we have thoroughly\nrevised both the main Figures and Supplementary Figures to provide a clearer, more\nstructured narrative.\nThe original Figure 1 has been revised by deleting original Figure 1E and H. These\npanels were removed as they did not contribute significantly to the overall\nunderstanding. We moved panel 1B to the lower section, facilitating a more intuitive\n3\ncomparison. This new layout clarifies how fungi contribute to eukaryotic diversity and\nactivity.\nThe previous Figure 3 has been reorganized into the current main Figure 3 and\nSupplementary Figure S4, while the original Figure 4 has been divided as the new\nmain Figure 4 and Supplementary Figure S5. Additionally, we have revised the main\nFigure 6, along with Supplementary Figures S3-S7, now labled as Figures S6-S10,\nto ensure consistency and improve the interpretability of the data. In the various\nsubplots across Figures S6 and S9, we ensured that the same fungi species are\nrepresented using the same color across all panels. This consistency helps readers\ntrack the contribution of specific species more easily. For the heatmaps depicting\npeptidase and CAZyme functions (Figures S7 and S10), we adopted a color scheme\nwith stronger contrast. We grouped samples by ocean regions, allowing the\nheatmaps to effectively show the variation in peptidase and CAZyme functions\nacross different oceanic regions and size fractions. This grouping emphasizes the\ndifferences between polar and non-polar oceans in a more structured manner.\nWe have also added a new Supplementary Figure S2, which highlights the spatial\ndistribution of genes and transcripts encoding fungal peptidases and CAZymes\nacross a latitudinal gradient, providing a broader context for the observed patterns.\nFigure numbers throughout the manuscript have been updated accordingly.\nThe authors present the results they obtained with very little critical insights. When\nstudying the relative abundance of fungi they found the following order of group\nabundance, Ascomycota, Mucoromycota, Chytridiomycota, Basidiomycota and\nZoopagomycota. How this data compares with previous descriptions of fungal\ndiversity in the open ocean? Is this what it was expected? Taxonomic data derived\nfrom metagenomes and metatranscriptomes depend largely on the taxa present in\nreference databases, has this been taken into account?\nWe have carefully compared our findings with the existing literature on pelagic fungi\nomics, specifically two recent studies utilizing the Tara Oceans dataset (Baltar et al.,\n2021; Breyer et al., 2022), as well as the works of Chrismas et al. (2020) and Orsi et\nal. (2018, 2022) 5, 6, 12, 13, 14. Figure 7 provides a detailed comparison of fungal\n4\nfunctional diversity, biogeography, and activity in the open ocean, highlighting the\nsimilarities and differences with these prior studies.\nPrevious research predominantly identified Ascomycota and Basidiomycota\n(Dikarya) as the major fungal phyla. In contrast, our study revealed a notable\npresence of early-diverging fungal lineages, such as Mucoromycota,\nChytridiomycota, Zoopagomycota, Blastocladiomycota, Cryptomycota, and\nMicrosporidia—groups that were either scarcely identified or not discussed in earlier\nstudies. This finding is consistent with Peng et al. (2021), who reported that early-\ndiverging fungal phyla comprised about one-third of the fungal community in\nmetagenomes from an Oceanic Oxygen Minimum Zone 15. This suggests that fungal\ndiversity in the open ocean may be broader and more complex than previously\nrecognized.\nYes, we acknowledge that taxonomic annotation depends heavily on the reference\ndatabases used, as the accuracy is influenced by the evolutionary distances\nbetween environmental taxa and those in genomic sequence databases 16.\nMicroeukaryotic sequences can be annotated using various databases, including\nMMETSP + MarRef, PhyloDB, EukProt, and the NCBI nr database 17, 18, 19. Our\nanalyses utilized the NCBI nr database, which is comprehensive and continually\nupdated, allowing us to identify a greater diversity of fungal taxa in our samples.\nIn alignment with Cohen et al. (2021, Nature Microbiology; 2024, Nature\nCommunications), who highlighted the advantages of using custom and combined\ndatabases, we employed the NCBI nr database along with DIAMOND and the\nLowest Common Ancestor (LCA) algorithm 18, 19. This approach was chosen to\nenhance the accuracy and reliability of our taxonomic annotations. Incorporating a\nbroad and high-quality database, such as NCBI nr, ensures that our annotations are\nrobust and comprehensive. The usage of NCBI nr data base also enables systematic\ncomparison with previsous result from our group 5, 6.\nThis work is based on genes predicted from metagenomes and metatranscriptomes.\nHowever, in this critical step, the authors use Prodigal, a program designed to\npredict prokaryotic genes in metagenomics reads (Prodigal: PROkaryotic DYnamic\nprogramming Gene-finding ALgorithm). As fungi are eukaryotic organisms, this does\n5\nnot seem the best option, and I wonder how this can severely affect the results\nobtained. This is a major drawback of the manuscript.\nThank you for this critical comment which allows to better express our approach in\nthis matter.\nWe acknowledge that Prodigal was originally developed for prokaryotic gene\nprediction and is optimized for prokaryotic genomes. However, Tang et al. (2015)\nfound that Prodigal, along with other tools like GeneMarkS-T and TransDecoder, can\neffectively predict coding regions in eukaryotic transcripts, despite its primary design\nfor prokaryotes 20. Prodigal has demonstrated comparable performance to\nTransDecoder, especially in predicting continuous, intronless coding sequences and\nidentifying translation initiation sites in eukaryotic metatranscriptomic data 20.\nIn our study, we assembled the metatrascriptomic reads into conting and then used\nProdigal to predict protein functions in eukaryotic metatranscriptomic data from\ncontigs longer than 200 base pairs 5, 6, 20. To obtain the corresponding gene\nabundance in the metagenome, we mapped reads to gene categories derived from\nthe metatranscriptomic assembly using the BWA algorithm 21, and extracted the\ngene abunance of CAZyme and peptides. This approach enabled us to compare\nmetagenomic data with functional genes identified in the metatranscriptomic data,\nbypassing the complexities and potential biases of metagenome assembly,\nespecially given the predominance of prokaryotic sequences in metagenomes 5, 6, 17,\n22.\nLine 41. Why non-polar? Wouldn't be better to label this category as \"Atlantic\"??\nThanks for the comment. The term \"polar\" refers to regions of the Southern Ocean,\nwhich are closer to the Antarctic, whereas \"non-polar\" refers to regions of the Atlantic\nOcean that are farther from the Antarctic. This distinction helps to emphasize the\nenvironmental differences we are investigating, which is reasonable given the large\nrange of latitudes covered in our study.\n6\nLines 39-42 . It is not enough to say that there are functional changes, would be\ninteresting to explain these changes.\nWe have expanded the description of our results/outcomes accordingly. Please see\npage 2, lines 38-44:\n“We also found that fungal CAZyme genes is more abundant in non-polar regions\nbut peptidase genes are dominating in polar waters. In non-polar regions, distinct\nsize fractionation patterns were observed among fungal groups, with Basidiomycota\npredominantly contributing to total peptidase genes in the LF, whereas\nChytridiomycota exhibited a significant contribution in the SF. These findings\nunderscore the critical role of size fractionation in elucidating the ecological functions\nof marine fungi.”\nLine 93-100. The first sentence says that there is few data on carbohydrate and\nprotein degradation by marine fungi, and the second sentence says that this is the\nfirst report.\nSorry for the confusion. We have adjusted the description to reflect the research as a\n\" systematic analyses \" rather than making absolute claims. Please see page 4, line\n111.\nLines 155-159. I guess there are expected (mostly methodological) reasons to\nexplain the order of magnitude difference in the number of CAZY genes found in\nmetagenomes and metatranscriptomes. But these are not explained nor discussed.\nThanks for this comment, that also allows us to better explain our approach. In the\nrevised manuscripts, we have added sentences to explain the differences between\nthe number of CAZyme and peptidase genes found in metagenomes and\nmetatranscriptomes.\nPlease refer to page 6 in lines 172-176:\n7\n“In our study, we generated a total of 455.1 Gb of raw data from 42 metagenomes,\nwith each DNA library yielding an average of 10.8 Gb of raw data (Table S2). This\nenabled us to identify 943 fungal peptidase sequences and 1,320 CAZyme\nsequences (Table S4). A total of 1959,9 Gb of raw data were generated from 53\nmetatranscriptomes using magnetic Oligo(dT) beads to isolate poly(A)+ mRNA, with\nan average of 37.0 Gb raw data per sample (Table S3).”\nAnd lines 185-192:\n“The methodological differences—particularly the significantly greater sequencing\ndepth and the poly(A)+ enrichment in the metatranscriptomic approach—explain the\nhigher number of peptidase and CAZyme genes detected in the metatranscriptomes\ncompared to the metagenomes. Given the more comprehensive dataset provided by\nthe metatranscriptomic approach, our subsequent analyses will primarily focus on\nthe metatranscriptomic data to further explore the functional diversity and secretory\npotential of fungal peptidases and CAZymes.”\nLine 219-220. The groups dominating in peptidases genes (Chytridiomycota-\nBasidiomycota) are very different from the groups dominating the community\n(Ascomycota-Mucoromycota). While this is possible, it is a bit unexpected, and it\ncould also be due to methodological reasons (mostly related to database\ncompleteness for taxonomy). At any rate, I am surprised that the authors did not try\nto explain these very obvious differences.\nThanks for the comment. This phenomenon is indeed observed not only in peptidase\ngenes but also in CAZyme genes. In the revised manuscript, we have expanded our\ndiscussion to better address these differences. Please see lines 328-345:\n“Ascomycota dominated the total fungal genes (34.2%) and transcripts (67.1%), but\nonly contributed ~26% of fungal CAZyme genes and transcripts (Figures 1C vs. 3C\nand 4C). The total CAZyme genes and transcripts from Ascomycota and\nBasidiomycota were more abundant in non-polar environments compared to polar\nones (Figures 3C and 4C). In contrast, Chytridiomycota CAZyme genes and\ntranscripts were more prevalent in polar environments (28.7% - 44.5%) compared to\n8\nnon-polar ones (16.1% - 21.1%) (Figures 3C and 4C). A similar trend was observed\nin total fungal peptidases and secretory CAZyme encoding genes and transcripts\n(Figures 3A, 4A, 5B and S5C). This pattern reflects a taxonomic shift in ecological\nniches, where the proportion of fungal CAZymes from other groups (i.e., Ascomycota\nand Basidiomycota) increases in non-polar oceans compring to Chytridiomycota.\nIn addition to their differences in cell size 23, 24, 25, 26, Ascomycota and Basidiomycota\nare known to have spore adaptations, which help them remain in the water column\nfor longer periods 27. Chytrids have been found in cold environments such as high-\narctic tundra soils 28, in soils under persistent snow packs 29, in high-mountain lakes\n30, and in sea ice and Arctic waters 31, 32, 33. The high relative abundance of the\nCAZyme gene and transcripts from chytrids in the polar ocean agrees well with their\nphysiological advantages for inhabiting aquatic ecosystems, including their mobility\nand capacity to parasite diatoms which are known to dominate marine phytoplankton\nin polar oceans 34, 35, 36.”\nLine 313. Results show that the most prevalent fungal CAZYme were GT. However,\nthere is no mention in the next paragraph on what they are doing (they do not seem\nto be directly involved in degradation) and why they are the most important. They are\njust ignored.\nIntroduction and discussion of GTs were included in lines 377-378 and lines 415-\n422:\n“Although GTs, responsible for glycosidic bond formation, constitute a high\nproportion of total CAZyme genes (62%) in polar oceans 37, 38, CBMs are more\nprevalent (41.2%) in the LF of non-polar regions, underscoring their critical role in\nsubstrate targeting (Figure 3D).”\n“Among the identified GT families, GT8 was the most abundant in polar\nenvironments compared to non-polar ones. GT8 enzymes are involved in\nglycoprotein folding quality control and cell wall biosynthesis 39. The active\nexpression of GT8 may influence fungal adaptation to environmental stress by\nregulating the composition and structure of the cell wall. In contrast, GT62 and\nGT68, which participate in the modification of glycan structures 40, were relatively\n9\nmore abundant in non-polar oceans (Figures 6 and S10). This distribution pattern is\nconsistent with that of GH128, GH72, and GH45, which utilize beta-glucan as a\nsubstrate (Figures 6 and S10).”\nLines 406. I do not see why detecting diverse fungal genes in the omics data\nsupports the conclusion that fungi play a \"profound and significant role\" in governing\nbiogeochemical cycles. This depends on how much fungi contribute to these cycles\n(as compared with other taxa), which cannot be derived from the presented data.\nWe have changed \"profound and significant role\" to “overlooked role”.\nLines 416-418. Comparing fungal peptidase and CAZyme genes against non-fungal\npeptidases/CAZYmes would be a first step to interpret the importance and role of\nfungi in marine ecosystems.\nThank you for this comment. We have now included this comparison in lines 210-216:\n“Notably, more than 13% of fungal CAZyme transcripts in both polar and non-polar\nwaters displayed secretory capability (Figure 1G), a rate significantly higher than <10%\nobserved in prokaryotes from open oceanic and deep-sea environments 2, 12. In\ncontrast, less than 0.5% of fungal peptides in both non-polar and polar waters showed\nsecretory capability (Figure 1E), compared to the 2 - 9% secretory capability found in\nprokaryotic peptides 2. These findings suggest that fungi exhibit a substantial capacity\nfor extracellular carbohydrate degradation, characteristic of a saprotrophic lifestyle 41.”\nLine 430-431. Here it appears temperature, but when this has been analyzed??\nPlease see line 237-239, where we mentioned that “funtional composition of fungal\nCAZyme transcripts were linked to temperature (Figure S3, mantel test, p<0.05).”\nLine 442- I guess it should be \"latitudinal\"\n10\nCorrected accordingly.\nLines 504-505. It is not very clear how the metagenomes were treated. I guess no\nassembly was attempted, right? Then read mapping to the transcripts, resulted in a\nlow number of genes found. Can you explain this, please (I guess most signal was\nprokaryotic), And how this could impact the results found?\nThank you for this insightful comment. The revised method can be found in lines\n585-591 of page 19:\n“To evaluate the relative abundance of genes in the metagenomic data reads from\neach metagenome were mapped to the CAZyme and peptidase gene categories\nderived from the metatranscriptomic assembly using the BWA algorithm (0.7.17) 21.\nThis approach allowed for a direct comparison of metagenomic data to the functional\ngenes identified in the metatranscriptomic data without the complexities and potential\nbiases associated with metagenome assembly, especially given the high proportion of\nprokaryotic sequences in the metagenomes.”\nWhile mapping reads directly to metatranscriptomic gene categories can effectively\nhighlight functionally relevant sequences, this approach may result in identifying a\nrelatively low number of genes from the metagenomes due to the predominance of\nprokaryotic sequences 42, 43. Consequently, there is a risk of underestimating the\ndiversity and abundance of eukaryotic CAZyme and peptidase genes in the\nmetagenomes. Additionally, genes present in the metagenome may not be\nexpressed in the metatranscriptome, introducing another layer of potential bias 44.\nFuture studies could benefit from integrating both assembly and read mapping\nstrategies to capture a more comprehensive view of the microbial community and its\nfunctional dynamics 45, 46, 47.\nFigure 1. Some of the graphs do not have the y-axis legend. Also, what is the x-axis\nof panel C? Are individua stations? How about water depth? This seems just a\ncompilation of data, with very little effort to convey a message to the reader.\nThanks for the comment. Modified as suggested. Please see the revised Figure 1.\n11\nLine 545. I do not see the category \"Others\"\nNow all phylum were included in the figure legend. “Others” was deleted in the\nrevised manuscript.\nFig. 3. Again, this figure includes a lot of categories, plots, and colors. Very, very\ncomplicated to follow. The same for Figure 4\nWe have revised Figures 3 and 4 to address the concerns about complexity. The\noriginal Figure 3 has been reorganized and divided into the new main Figure 3 and\nSupplementary Figure S4. Similarly, the original Figure 4 has been updated and\ndivided into the new main Figure 4 and Supplementary Figure S5.\nReviewer #2 (Remarks to the Author):\nWhilst more data on marine fungi are needed and I understand the exploration of the\npolar region is interesting (though not totally novel), I miss the significance of the\ncontribution to the field to warrant publication in Nature Communications or similar\nlevel journal. Similar work has already been conducted that is already in the\nestablished literature, including on marine fungal Cazymes and in the Atlantic region,\nfor example see work from Orsi Group. Some of these studies in the established\nliterature (particularly studies not from the author team) appear missing from the\nmanuscript.\nOrsi et al (2022) Carbon assimilating fungi from surface ocean to subseafloor\nrevealed by coupled phylogenetic and stable isotope analysis. The ISME Journal 16\n1245–1261.\nChrismas et al (2020) Depth-dependent mycoplankton glycoside hydrolase gene\nactivity in the open ocean—evidence from the Tara Oceans eukaryote\nmetatranscriptomes. The ISME Journal 14 2361–2365.\nBaltar et al (2021) Potential and expression of carbohydrate utilization by marine\nfungi in the global ocean. Microbiome 9 106.\n12\nBreyer et al (2022) Global contribution of pelagic fungi to protein degradation in the\nocean. Microbiome 10 143.\nThank you for your valuable feedback. We have now included all the citations\nmentioned by the reviewer.\nOrsi et al has indeed made significant contributions to the study of both soil and\nocean fungal biodiversity, and their work is crucial to our research. We would like to\nclarify that we have already cited several of Orsi et al.'s important publications in our\nstudy. In addition to the reference reviewer mentioned (Orsi et al., 2022), we have\ncited:\n1) Orsi WD, Biddle JF, Edgcomb V. Deep Sequencing of Subseafloor Eukaryotic\nrRNA Reveals Active Fungi across Marine Subsurface Provinces. PLOS ONE 8,\ne56335 (2013)\n2) Orsi WD, Edgcomb VP, Christman GD, Biddle JF. Gene expression in the deep\nbiosphere. Nature 499, 205-208 (2013)\n3) Orsi WD, Richards TA, Francis WR. Predicted microbial secretomes and their\ntarget substrates in marine sediment. Nat Microbiol 3, 32-37 (2018)\nThanks to the reviewer for recognizing the significance of Baltar et al. (2021) and\nBreyer et al. (2022), which are references from our lab that had already been cited\nin our original manuscript.\nThe study is largely descriptive, more a catalogue of genes/mRNA, greater attempt\nshould have been made to synthesise the work.\nWe gratefully thank the reviewer for the critical comments. We have addressed\nsimilar concerns raised by reviewers 1# and 3#, as detailed in our responses to their\ncomments. To better synthesize the major findings of this study, we present two key\nexamples:\n13\nFirstly, we discovered that fungi constituted 1-2% of total eukaryotic genes and\ntranscripts in both metagenomes and metatranscriptomes. Interestingly, fungi\naccounted for >3% of the eukaryotic CAZyme transcripts but only 0.16% for\nproteases, suggesting a preferential role of pelagic fungi in carbohydrate degradation\nover proteolysis. This highlights the specialized ecological roles fungi play in organic\nmatter degradation in marine environments.\nSecondly, our research reveals significant functional and taxonomic divergence\namong pelagic fungi between polar and non-polar oceanic regions. Fungal CAZyme\ngenes were more abundant in non-polar regions, while peptidase genes dominated\nin polar waters. We also observed distinct size fractionation patterns, with\nBasidiomycota contributing more to peptidase genes in large size fractions, while\nChytridiomycota were prominent in small fractions in non-polar regions. These\nfindings underscore the ecological importance of size fractionation in fungal\ncommunities, despite only minor differences between size fractions in overall fungal\nabundance.\nOverall, our study provides new insights into the ecological and biogeochemical roles\nof pelagic fungi, complementing existing knowledge on marine microbial\ncommunities and emphasizing fungi's unique contributions to organic matter\ndegradation in different oceanic regions.\nThe use of DNA/RNA metagenomes and metatranscriptomes can only go so far.\nFunction could have been explored further with stable isotope analysis as the Orsi\nGroup have done (see Orsi et al (2022) Carbon assimilating fungi from surface\nocean to subseafloor revealed by coupled phylogenetic and stable isotope analysis.\nThe ISME Journal 16 1245–1261).\nWe completely agree that additional approaches, such as stable isotope analysis,\ncould further expand our understanding of oceanic fungi, particularly in their roles in\nbiochemical cycles. However, the scope of the study was to study the expression\nand role of pelagic fungia accross a large range of latitudes, known to exhibit marked\ncontrasting biogeochemical settings. In future studies, based on the findings of the\npresent study, it is now possible to target specific regions, where contrasts in fungal\n14\ngene expression were found, to conduct stable isotopes as well as other techniques,\nto further increase our knowledge on the role of pelagic fungi in organic matter\ndegradation. In response to this suggestion, we have added a sentence to the\ndiscussion emphasizing the importance of stable isotope analysis in revealing fungal\ninvolvement in carbon assimilation. Please see page 15 lines 475-478.\nThe presentation of the data in the current figures in not very useful.\nWe thank the reviewer for the critical comments. In response to the comments made\nby Reviewer 1#, we have intensively modified our main Figures and Supplementary\nFigures. Briefly, the original Figure 1 has been revised by deleting original Figure 1E\nand H. We moved panel 1B to the lower section. This new layout clarifies how fungi\ncontribute to eukaryotic diversity and activity. The previous Figure 3 has been\nreorganized into the current main Figure 3 and Supplementary Figure S4, while the\noriginal Figure 4 has been divided as the new main Figure 4 and Supplementary\nFigure S5. Additionally, we have revised the main Figure 6, along with\nSupplementary Figures S3-S7, now labled as Supplementary Figures S6-S10, to\nensure consistency and improve the interpretability of the data. In the various\nsubplots across Figures S6 and S9, we ensured that the same fungi species are\nrepresented using the same color across all panels. This consistency helps readers\ntrack the contribution of specific species more easily. For the heatmaps depicting\npeptidase and CAZyme functions (Figures S7 and S10), we adopted a color scheme\nwith stronger contrast. We organized the samples by ocean regions and size\nfractions, which allows the heatmaps to effectively display the variation in peptidase\nand CAZyme functions across these categories. This structured grouping enhances\nthe clarity of functional differences between polar and non-polar oceans.\nWas any attempt made to explore differences across the Atlantic. A range of biomes\nwere sampled but not obvious differences.\nWe gratefully thank the reviewer for the critical comments. We did explore the\ndifferences across the Atlantic Ocean, but compared to the obvious difference\nbetween non-polar and polar environments, we did not find a significant difference\n15\nwithin the Atlantic Ocean. We included this part of the result in lines 231-233 and in\nthe Supplementary Materials, Figure S2.\nI can see a potential issue in the data analysis, interpretation and conclusions. The\nstudy is based on two cruises conducted in two different years. I cannot find any\ninformation the dates when samples were collected. Where the different cruises one\nAtlantic and one Arctic? How do you separate the effect of time?\nNow, in lines 514–515, detailed sampling dates have been incorporated as\nrecommended.\n“The samples were collected during the oceanographic research cruises ANTOM-I\n( 15 December - 15. Jannuary, 2020/2021) and ANTOM-II (January 23 - February 6,\n2022).”\nIn oceanographic research, it is often challenging to collect samples at the same\ntime across different locations and expeditions due to logistical constraints, even\nmore so when Antarctic research is performed. For instance, the Tara Oceans\nexpedition, which is the benchmark for multiple marine microbial studies used by\nmultitude of research groups worlwide, collected plankton samples covering multiple\nocceans over several years from 2009 to 2013 43, 48. Similarly, in our study, while the\ntwo cruises were conducted in different years, we carefully scheduled both in the\naustral summer season to minimize the influence of seasonal variations, particularly\ntemperature, on the microbial communities and their enzymatic activities. By\nsampling within the same season, we aim to reduce the confounding effects of\ntemporal variability, allowing a clearer focus on biogeographical patterns and\nfunctional diversity across different oceanic regions. While temporal variability\nremains a factor to consider, this seasonal coordination helps ensure that our\nfindings are robust in interpreting the biogeographical and ecological roles of marine\nfungi across large spatial scales.\nAll sequence data should be made available in a public database.\n16\nDone as suggested. The metagenomic raw reads have been deposited with the\nNCBI under Bioproject number PRJNA1116066. The metatranscriptomic raw reads\nhave been deposited with the NCBI under Bioproject number PRJNA1115042.\nPlease see lines 604-607.\nReviewer #3 (Remarks to the Author):\nThe authors used multi-omics techniques (metagenomics and metatranscriptomics)\nfor high-resolution spatial analysis of functional fungal diversity and metabolic\npotential in the open Atlantic and Southern Ocean from subtropical to polar regions.\nUsing these techniques, the researchers successfully uncover the significant role of\nfungi in organic matter degradation, with a focus on their preferential role in\ncarbohydrate degradation. The differentiation between free-living and particle-\nattached communities, along with the high-resolution spatial analysis across diverse\nmarine regions, adds considerable depth to the study's findings.\nWe gratefully thank the reviewer for the positive remarks.\nThe introduction is comprehensive, with pertinent references cited, and clearly\narticulates the novelty and necessity of the study. The materials and methods\nsection are detailed and well-written. The results and discussion provide a\ncomprehensive and insightful analysis, the findings are presented in detail, with\nnuanced data interpretation, highlighting the study's rigor and depth. Although the\nauthors provided enough data to conclude this study, some questions and errors\nmust be addressed. Additionally, the discussion section could be expanded to\nimprove the clarity and depth of the interpretation of the findings.\nWe appreciate the reviewer’s detailed and constructive comments. We have\nthoroughly revised the manuscript in response to these suggestions. Below, we\noutline our responses and the corresponding changes made in the revised\nmanuscript.\n17\n1. How do the functional roles of the identified fungal taxa (Ascomycota,\nBasidiomycota, Mucoromycota, etc.) differ between free-living and particle-attached\ncommunities? Please thoroughly elaborate and include in result and discussion\nsection.\nThank you for the reviewer’s comments. We have identified differences in fungal\ntaxa associated with peptidases and CAZymes between polar and non-polar ocean\nregions at both the gene and transcript levels. Additionally, we change the definition\nof PA and FL to SF and LF to avoid the confusion with prokaryotic lifestyles as\ncomented by reviewer #1, in this way, we found distinctions between small and\nlarge fraction communities at the gene level, though these differences were not\nobserved in the metatranscriptomes. These findings, along with a summary of\nspecies differences in small and large size fraction communities and in polar versus\nnon-polar environments, are presented in Figure 7. We also compared these results\nwith functional species identified in marine environments from the Tara Oceans\nproject 5, 6. The manuscript has been revised accordingly in the Results and\nDiscussion sections.\nPlease see page 8 in lines 234-236:\n“Moreover, beyond regional disparities, genes encoding fungal peptidases and\nCAZymes also exhibited distinct differences between the SF and LF size fractions in\nthe non-polar ocean (Figures 2, A and B, pairwise adonis, p < 0.01).”\nLines 268-279:\n“The taxonomic shift was evident across different size fractions in non-polar\nenvironments, where the contributions of Basidiomycota and Chytridiomycota to total\npeptidase genes varied. Basidiomycota dominated the LF (>50%), while\nChytridiomycota contributed more to the SF (Figure 3A). The predominance of\nChytridiomycota in the SF aligns with their typically unicellular or simple multicellular\nmorphology 23, 24. Their smaller size and the production of motile zoospores likely\nfacilitate their presence in the SF, allowing them to effectively locate and potentially\nexploit smaller particulate matter and dissolved organic substrates once they find a\n18\nsuitable host 49. In contrast, Basidiomycota were more abundant in the LF (>3 µm),\nconsistent with their more complex, filamentous structure 25, 26. These results\nhighlight the critical role of size fractionation in understanding the ecological niches\nand functional roles of different fungal groups in marine environments.”\n2. How does the association with particles influence the functional roles and gene\nexpression profiles of fungi in marine environments?\nIn lines 136-141 of page 5, we have included information in the text to emphasize the\nimpact of size fraction on the function of marine fungi:\n“Our results show a higher relative abundance of fungal genes in the large size\nfraction (1.5 to 2.0%) compared to the small size fraction (1.0 to 1.5%) across both\npolar and non-polar regions, indicating that fungi prefer inhabitating on larger\naggregate environments such as marine snow and algal detritus 12, 50, 51, 52. These\n(micro)environments of particles provide greater nutrient availability than the ambient\nwater, supporting their roles in nutrient cycling and degradation processes.”\n3. What is the main reason for the higher proportion of peptidase genes in polar\nwater compared to non-polar water?\nDisscussion included in line 204-210 of page 7:\n“These spatial variations highlight that higher proportions of peptidases in polar waters\nlikely reflect fungi's adaptation to efficiently degrade proteins in cold, nutrient-limited\nenvironments 53, 54. Conversely, the increased capability of fungal CAZymes in non-\npolar regions reflects the higher availability of complex carbohydrates and warmer\ntemperatures, which favor the breakdown of polysaccharides and other carbohydrate-\nrich substrates, a primary function of CAZymes 55, 56.”\n4. What are the specific ecological roles of different fungal species in polar versus\nnon-polar regions?\n19\nIn the Results and Disscussion, we address the ecological roles of different fungal\nspecies in polar versus non-polar regions in lines 328-331:\n“Ascomycota dominated the total fungal genes (34.2%) and transcripts (67.1%), ,\nbut\nonly contributed ~26% of fungal CAZyme genes and transcripts (Figures 1C vs. 3C\nand 4C). The total CAZyme genes and transcripts from Ascomycota and\nBasidiomycota were more abundant in non-polar environments compared to polar\nones (Figures 3C and 4C).”\nLines 333 - 339:\n“A similar trend was observed in total fungal peptidases and secretory CAZyme\nencoding genes and transcripts (Figures 3A, 4A, 5B and S5C). This pattern reflects a\ntaxonomic shift in ecological niches, where the proportion of fungal CAZymes from\nother groups (i.e., Ascomycota and Basidiomycota) increases in non-polar oceans\ncompring to Chytridiomycota. In addition to their differences in cell size 23, 24, 25, 26,\nAscomycota and Basidiomycota are known to have spore adaptations, which help\nthem remain in the water column for longer periods 27.”\nLines 341 - 345:\n“The high relative abundance of the CAZyme gene and transcripts from chytrids in\nthe polar ocean agrees well with their physiological advantages for inhabiting aquatic\necosystems, including their mobility and capacity to parasite diatoms which are\nknown to dominate marine phytoplankton in polar oceans 34, 35, 36.”\nAnd lines 361 - 364:\n“Our findings further confirm that fungi play active roles in specific functions that are\ninfluenced by environmental conditions in the ocean, thereby reinforcing the\nconcepts of niche specialization and resource utilization 57, 58, 59.”\nWe have also thoroughly discussed the specific ecological roles of various fungal\nspecies in polar and non-polar regions in the revised manuscript, incorporating\nfeedback from the other comments.\n20\n5. What are the ecological implications of the dominant presence and higher\nexpression of Serine peptidases (SP) by Chytridiomycota in polar oceans compared\nto Basidiomycota and Ascomycota in non-polar regions?\nDisscussion included in lines 296 – 307 of page 10:\n“In this study, the fungal composition specifically contributing to serine peptidases\naligns well with the high contributions of Basidiomycota and Ascomycota in non-polar\noceans, contrasted with the dominant role of Chytridiomycota in polar oceans. This\ntrend is consistently observed across both gene and transcript levels of total fungal\npeptidases (Figures 3A, 4A, 5B). The distribution patterns reflected ecological\ncompetition and resource partitioning among fungi. The high prevalence of\nChytridiomycota-related serine peptidase transcripts in polar regions indicates their\ncrucial role in breaking down polypeptides and proteins into smaller peptides or\namino acids at low temperatures. As climate change progresses, polar regions are\nwarming and experiencing shifts in nutrient availability and primary production 60, 61.\nThese changes could impact fungal community distribution and metabolism,\npotentially altering the balance of organic matter storage and cycling in these\nsensitive environments.”\n6. How does the functional diversity of fungal CAZymes, particularly the differences\nbetween glycoside hydrolases (GHs), carbohydrate-binding modules (CBMs), and\nglycosyltransferases (GTs), influence carbon cycling in polar versus non-polar\nmarine environments?\nDisscussion included in lines 384-386 of page 13 and lines 438-442 of page 14 :\n“The increased transcripts abundance of CBMs and GHs in non-polar environments,\ncompared to polar regions, confirms the greater availability of complex\ncarbohydrates in these areas 55, 56.”\n“Thus, the presence and transcript expression of fungal clades in specific functional\nroles is pivotal in shaping the diversity and abundance of CAZymes. Our findings\n21\nfurther reveal that distinct fungal clades dominate the production of different CAZyme\nfamilies (CBMs, GTs, and GHs), suggesting potential divergent strategies for\ncarbohydrate degradation and utilization by pelagic fungi in the ocean.”\n7. Why there is a vast difference in the number of metagenomics and\nmetatranscriptomics samples, it won't be same? Please justify.\nThank you for this comment. We have added a paragraph in the Materials and\nmethods section to clarify the difference in sample numbers. Please refer to lines\n557-562:\n“The primary reason for the difference in sample numbers (42 metagenomic vs. 53\nmetatranscriptomic) is due to logistical challenges during sample collection and our\nfocus on obtaining high-quality RNA for metatranscriptomic analyses. The limited\nseawater volume collected restricted the available biomass for both DNA and RNA\nextractions. Prioritizing the required RNA input (>700 ng) for metatranscriptomics, we\nhad fewer samples available for metagenomic sequencing (42 DNA samples).”\n8. When contradictory results are observed in a manuscript, authors should provide\nthe most probable reason and possible mechanism to aid understanding of the\nfindings.\nWe thoroughly revised our manuscripts as suggested.\n9. Please reframe the sentence from lines 31 to 36 of the abstract because it is too\nlong.\nThe sentence was rephrased as follows:\n“In this study, we employed metagenomic and metatranscriptomic approaches, to\nexplore the fungi mediated organic matter degradation in the sunlit ocean. Samples\ncollected from subtropical Atlantic Ocean (non-polar) to Southern Ocean (polar\nregions) represent spatial differences and size fracnation between small (0.2 - 3 µm,\n22\nSF) and large (> 3 µm, LF) fractions reflecting niche partitioning in fungal\ncommunities.”\n10. In line number 195 “(Figure S1C)).” Remove one “)”.\nCorrected accordingly.\n11. Please include a few lines in the appropriate section explaining why the\nmetagenomic and metatranscriptomics analysis results differ.\nWe have added sentences addressing this point. Please refer to lines 158-162:\n“The changes between metagenomic and metatranscriptomic results suggest that\nthe metabolic activity of marine fungi may vary among taxonomic groups, where\nfungal species with higher contributions to the transcripts pool may play a more\nprofound role in marine biogeochemical cycles because the expression level of\ntranscripts reflects metabolic response to the changing environment.”\nLines 220-223:\n“These findings indicate clear disparities in the genomic potential (genes) and\nexpression profiles (transcripts) of fungi involved in carbohydrate and protein\ndegradation, as has been recently shown for micobial communities from marine and\nsoil ecosystems as well as in hunman gut 44, confirming the importance of studying\nfungal transcripts in addition to genes.”\nIn addition, the original introduction includes a sentence in lines 99-102 that explains\nthe rationale behind using both metagenomic and metatranscriptomic approaches:\n“We investigated both the metabolic potential (metagenomics) and the gene\nexpression (metatranscriptomics) of pelagic fungi since DNA-based results might\ndiffer dramatically from the actual transcription and function of microorganisms in the\nenvironment 44”. This statement establishes the basis for comparing genomic\npotential with transcriptional activity.\n23\n12. Please reframe the sentence in lines 65-67, as \"such as\" was used twice and\nshould be replaced with another word.\nWe have replaced the first instance of 'such as' with 'including' to avoid repetition in\nthe sentence.\n13. A thorough proofreading would be necessary to avoid grammatical and\ntypographical errors throughout the manuscript.\nReviesed thoroughly as suggested.\n14. No information is provided about how to download the sequence data in a public\ndatabase such as NCBI or EMBL-EBI.\nDone as suggested. The metagenomic raw reads have been deposited with the\nNCBI under Bioproject number PRJNA1116066. The metatranscriptomic raw reads\nhave been deposited with the NCBI under Bioproject number PRJNA1115042.\nPlease see lines 604-607.\nReferences\n1. Zhao Z, Amano C, Reinthaler T, Orellana MV, Herndl GJ. 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While we note\nthat Reviewer #3 expressed overall enthusiasm for the study, we also recognize and appreciate\nthe critical feedback from Reviewers #1 and #2, particularly regarding the bioinformatics\nanalysis and the need for further validation using additional data. In response, we have\nsubstantially expanded our analyses and revised the manuscript accordingly to strengthen the\nrobustness and clarity of our conclusions.\nBelow, we provide a detailed point-by-point response to each of the reviewers' comments. In\nour responses, references to the revised manuscript are provided with corresponding line\nnumbers, and all changes are highlighted in yellow for ease of review.\nReviewer #1 (Remarks to the Author):\nThis is the second time I analyze this manuscript. In my first review I raise a set of\nmethodological questions (bioinformatic tools used) and formal questions (how data is\npresented). Now I have carefully read the answers to my previous questions and although I\nrecognize that the manuscript seems to be improved, I am not completely satisfied with all\nthe answers.\nWe appreciate the positive comment of the reviewer about the improvement of the second\nversion. In response to the reviewer’s comments, we have now carefully addressed each point\nraised in the initial and second reviews, particularly regarding methodological and formal\naspects of the study. In brief, now we have applied the exact methodology suggested by the\nreviewer, and compared it to our previous methodology, reaching very similar results,\nsupporting the strenght of our study. So we appreaciate also the suggestion of the reviewer\nconcerning this methodological aspect. We have provided detailed explanations and made\nsubstantial revisions to the manuscript. Below, we outline our responses to the specific\nconcerns raised, and we believe that these revisions meet the reviewer’s expectations.\nOne substantial criticism still remains, why using a program designed for gene prediction in\nprokaryotes to perform gene prediction in eukaryotes. Eukaryotic genes have introns and\nalternative splicing, which make their prediction substantially different than that of\nprokaryotic genes. Programs for predicting eukaryotic genes from metagenomic or\nmetatranscriptomic data already exist, such as MetaEuk and others, and these should be\nused together with a complete reference database of eukaryotes. Perhaps the predictions\nwill not be extremelly different, but the authors should use the best tools available.\nIn our initial response, we explained our rationale for using Prodigal instead of MetaEuk for\neukaryotic gene prediction, based on two key considerations. First, several gene prediction\ntools, such as GeneMarkS-T and TransDecoder, have demonstrated robust performance in\npredicting genes from eukaryotic transcriptomic data, particularly for intronless or minimally\nspliced sequences. While these tools were originally designed for prokaryotic gene prediction,\ntheir algorithms are adaptable to eukaryotic data due to shared features in translation initiation,\nsuch as the use of start codons (AUG) and open reading frame (ORF) identification. Second,\nin our case, Prodigal was employed to predict protein-coding sequences from eukaryotic\nmetatranscriptomic data, which are typically continuous and intronless, using contigs longer\nthan 200 base pairs 1, 2, 3. To link metagenomic data to functional annotations, we mapped\ngene\nabundances from the metagenome to gene categories derived from the metatranscriptomic\nassembly using the BWA algorithm 4. This approach allowed us to extract abundances of\nCAZymes and peptidases while avoiding the complexities and potential biases associated with\nmetagenome assembly, particularly given the dominance of prokaryotic sequences in\nmetagenomic datasets 2, 3, 5, 6.\nNevertheless, in the current revision, we have addressed the reviewer’s concern by re-analyzing\nthe data using MetaEuk, a tool specifically designed for eukaryotic gene prediction. MetaEuk\nwas run against a comprehensive protein reference database comprising approximately 88\nmillion curated sequences from three primary sources: the MERC dataset (derived from Tara\nOceans eukaryotic metatranscriptomes), the Marine Microbial Eukaryote Transcriptome\nSequencing Project (MMETSP), and the Uniclust50 database\n( https://wwwuser.gwdg.de/~compbiol/metaeuk/2019_11/)7. All subsequent steps, including\npeptidase and CAZyme functional annotation and taxonomic assignment, were re-performed\nbased on MetaEuk-predicted genes.\nConsistent with both our original findings and the reviewer's expectations, the re-analysis\nconfirmed that our core conclusions remain robust. Specifically, the functional and taxonomic\ndistributions of total genes/transcripts—as well as peptidase- and CAZyme-associated fungi—\nshowed minimal changes. The taxonomic patterns of fungal peptidases and CAZymes\nconsistently differed from those of overall fungal genes/transcripts. Ascomycota,\nBasidiomycota, and Chytridiomycota were the primary contributors to peptidase and CAZyme\ngenes, with Mucoromycota additionally prominent in CAZyme production (Additional Figure\n2).\nTo facilitate a direct comparison, we have included two representative sets of comparative\nanalyses in this response letter:\nAdditional Figure 1. Taxonomic assignment comparisons of total fungal community\ncomposition in the metagenome and metatranscriptome based on the two prediction methods\n(original Fig.1 vs. new Fig. 1)\nAdditional Figure 2. Comparative analysis of functional annotations: Prodigal vs. MetaEuk\nfor peptidase and CAZyme transcript predictions (original Fig. 4 vs. new part of Fig. 3)\nAnother concern is about size fractionation. Initially samples were divided as free-living and\nparticle attached, ignoring the fact that fungi can cover a wide spectra of cell sizes. So, in the\nfraction >3 µm there could be small or large fungi attached to particles or simply fungi larger\nthan 3 µm (for instance making hypha). This has been assumed in some parts (i.e. lines 268-\n279), but in other parts the assumption that fungi on the >3 µm are particle attached still\nremains (i.e. lines 117-120, 136-139). The reality can include the two aspects, but this needs\nto be explicitly explained.\nWe appreciate the reviewer’s insightful comment regarding the complexity of size fractionation\nand its implications for fungal ecology. We agree that the distinction between large size (>3\nµm) and"}, "author_response": ""}]

Decision letter