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Microbial metabolism in laboratory reared marine snow as revealed by a multi-omics approach

Microbiome volume 13, Article number: 114 (2025) Cite this article

Abstract

Background

Marine snow represents an organic matter-rich habitat and provides substrates for diverse microbial populations in the marine ecosystem. However, the functional diversity and metabolic interactions within the microbial community inhabiting marine snow remain largely underexplored, particularly for specific metabolic pathways involved in marine snow degradation. Here, we used a multi-omics approach to explore the microbial response to laboratory-reared phytoplankton-derived marine snow.

Results

Our results demonstrated a dramatic shift in both taxonomic and functional profiles of the microbial community after the formation of phytoplankton-derived marine snow using a rolling tank system. The changes in microbial metabolic processes were more pronounced in the metaproteome than in the metagenome in response to marine snow. Fast-growing taxa within the Gammaproteobacteria were the most dominant group at both the metagenomic and metaproteomic level. These Gammaproteobacteria possessed a variety of carbohydrate-active enzymes (CAZymes) and transporters facilitating substrate cleavage and uptake, respectively. Analysis of metagenome-assembled genomes (MAGs) revealed that the response to marine snow amendment was primarily mediated by Alteromonas, Vibrio, and Thalassotalea. Among these, Alteromonas exclusively expressing auxiliary activities 2 (AA2) of the CAZyme subfamily were abundant in both the free-living (FL) and marine snow-attached (MA) microbial communities. Thus, Alteromonas likely played a pivotal role in the degradation of marine snow. The enzymes of AA2 produced by these Alteromonas MAGs are capable of detoxifying peroxide intermediates generated during the breakdown of marine snow into smaller poly- and oligomers, providing available substrates for other microorganisms within the system. In addition, Vibrio and Thalassotalea MAGs exhibited distinct responses to these hydrolysis products of marine snow in different size fractions, suggesting a distinct niche separation. Although chemotaxis proteins were found to be enriched in the proteome of all three MAGs, differences in transporter proteins were identified as the primary factor contributing to the niche separation between these two groups. Vibrio in the FL fraction predominantly utilized ATP-binding cassette transporters (ABCTs), while Thalassotalea MAGs in the MA fraction primarily employed TonB-dependent outer membrane transporters (TBDTs).

Conclusions

Our findings shed light on the essential metabolic interactions within marine snow-degrading microbial consortia, which employ complementary physiological mechanisms and survival strategies to effectively scavenge marine snow. This work advances our understanding of the fate of marine snow and the role of microbes in carbon sequestration in the ocean.

Video Abstract

Background

Marine snow, typically larger than 500 μm in size, is ubiquitous in the marine ecosystem, with its abundance ranging from 1 to 100 aggregates per liter [1, 2]. It constitutes more than 90% of the vertical flux of particulate organic matter to the deep ocean [3], being one key vector of the biological carbon pump. Marine snow is composed of complex components [1, 4]. Phytodetritus, referred to phytoplankton-derived organic matter, plays a predominant role in the formation of marine snow, accounting for the majority of the annual export of organic material from the euphotic to the oceanic deep waters [2]. It is well known that phytodetritus typically supports a large number of active microbes, leading to their crucial involvement in key biogeochemical processes such as secondary production, carbon utilization and degradation, and nutrient recycling [5, 6]. Marine snow also incorporates dead zooplankton and fecal pellets [2]. For instance, the compounds produced by gelatinous zooplankton have been found to episodically contribute to the export of organic matter to the deep ocean, serving as a substantial organic matter source for deep-sea microbes [7,8,9,10].

Microbial heterotrophic metabolism leads to the solubilization of marine snow. The microbes colonizing marine snow produce abundant extracellular enzymes, primarily highly active hydrolytic enzymes, to break down complex macromolecular organic components from the marine snow [6, 11,12,13,14,15,16], ultimately resulting in (in)organic matter-enriched plumes [11, 17]. Microbes lacking the capability to produce extracellular enzymes benefit from the release of these small molecular substrates [18]. Therefore, the marine snow-attached (MA) and free-living (FL, near the plume) microbial communities undergo both a structural and functional succession throughout the process of marine snow degradation [19,20,21,22]. Increasing evidence indicates that some specific heterotrophic microbial populations often exhibit a transient dominance in the degradation of phyto-organic matter, such as members of Gammaproteobacteria, Bacteroidetes, and the Roseobacter clade [23,24,25,26]. These microbial activities initiate the degradation of marine snow and significantly impact the fate of marine snow, ultimately influencing the efficiency of the biological carbon pump [11, 27,28,29,30].

The response of microbial communities to marine snow has been investigated through field and micro- and mesocosm experiments, revealing distinct successional patterns in dominant species and metabolism responding to phytoplankton blooms [31,32,33,34,35]. For example, it was observed that members of the Bacteroidetes (Flavobacteriia), Gammaproteobacteria, and Alphaproteobacteria (Roseobacter clade) rapidly responded to a phytoplankton bloom in the southern North Sea (German Bight) by upregulating carbohydrate-active enzymes (CAZymes) and various transporter proteins, allowing for the efficient breakdown of algal organic matter [33, 34, 36]. Within mesoscale eddy systems, driven by intensified phytoplankton primary production and enhanced organic carbon export, different members of Gammaproteobacteria, Bacteroidetes, Alphaproteobacteria, and Planctomycetes frequently emerged as dominant active taxa [37, 38]. In addition, a microcosm experiment supplemented with phytoplankton-derived dissolved organic matter (DOM) demonstrated the rapid proliferation of Vibrio (Gammaproteobacteria), followed by members of Flavobacteriia (Bacteroidetes), accompanied by a shift in extracellular enzyme activity [39]. These studies have advanced our understanding of microbial community structure and metabolism in response to phytoplankton production as well as marine snow formation across various marine provinces.

However, characterizing the changes in the taxonomic and functional profiles of MA and FL microbial communities during the degradation of marine snow is challenging due to the difficulty of capturing real-time response processes in the field. Therefore, there is still no consensus on how the dominant populations respond to marine snow and on the microbial metabolism on marine snow. In this study, we simulated marine snow formation with phytoplankton-derived aggregates using rolling tanks in an experimental setup [40]. We integrated the metagenomic and metaproteomic datasets to identify the key microbial players in marine snow degradation and to gain a comprehensive understanding of the underlying physio-ecological mechanisms and strategies of MA and FL microbes in response to marine snow.

Methods

Establishing a microbial culture system with phytoplankton-derived marine snow in rolling tanks

A non-axenic microalgae Picochlorum sp. was cultured in f/2 medium [41, 42] at ~ 25 °C under artificial solar radiation as described in Unanue et al. [43]. Cultures were sampled daily to measure the optical density (OD) at 550 nm using a spectrophotometer (Hitachi U-2000) until they reached the early stationary phase (Supplementary Fig. 1). Subsequently, the cultures were autoclaved and evenly transferred to three 5-L serum bottles. These bottles contained prefiltered (3-μm pore-size polycarbonate membrane, Millipore) natural seawater collected from the surface waters of the Adriatic Sea (43°N, 15°E). Two serum bottles were only filled with prefiltered natural seawater to serve as a control. All the bottles were placed on a rolling table in the dark at ~ 22.5 °C (Supplementary Fig. 2a, b, c) [40]. Phytoplankton-derived marine snow began to form in the rolling tanks within 2 h after autoclaved algae were added (Supplementary Fig. 2d, e, f), and the concentration of total organic carbon was estimated to be ~ 6.64 mM based on the optical density of algae cell (Supplementary Table 1) [44, 45].

Monitoring prokaryotic abundance in marine snow and ambient water

Samples for prokaryotic abundance in the three culture bottles were collected and counted every 3 to 4 h, while samples in the control bottles were collected and counted every ~ 10 h. For the free-living (FL) cells in the culture bottles, 1–2 mL particle-free ambient water was preserved with formaldehyde (final concentration of 2%) and stored at 4 °C. Similarly, for the total cells in the control bottles, 1–2 mL of water was treated using the same preservation method. For the marine snow-attached (MA) cells in the culture bottles, visible aggregates, extensively colonized by microbial cells, were randomly collected. The aggregates were then pre-treated to obtain cell suspensions, following the procedures described by Agis et al. [46] and Besemer et al. [47, 48] with some modifications. Briefly, approximately 20 μL of visible particles (containing both the particulate fraction and its porewater) was dispersed in 0.5–1 mL tetrasodium pyrophosphate solutions (final concentration of 25 μM) for 1 h. Subsequently, the samples were sonicated (40 W) at room temperature for 3 min using an ultrasonic water bath. Cell suspensions free of particles were then transferred to a new tube and preserved using the same method as described above.

For the enumeration of the FL cell abundance, fixed water samples were filtered through polycarbonate membranes with pore sizes of 0.8 μm and then 0.22 μm (25-mm diameter, Millipore) at a pressure of < 0.03 MPa. The harvested cells within the size range of 0.22–0.8 μm were then counted. For the enumeration of the MA microbes and of microbes in the control tanks, fixed water samples were directly filtered onto 0.22-μm pore-size polycarbonate membranes (25-mm diameter, Millipore) at a pressure of < 0.03 MPa. Microbial cells were stained with 4′,6‐diamidino‐2‐phenylindole (DAPI) [49] at a final concentration of 2 μg mL−1, and the cells were counted using a Zeiss Axio Imager M2 epifluorescence microscope.

Nucleic acid and protein extraction

Once prokaryotic cells reached the early stationary phase after about 30 h, the marine snow suspension in the rolling tanks was filtered through 3-µm and then 0.22-µm pore-size polycarbonate membranes (47-mm diameter, Millipore) at a pressure of < 0.03 MPa to obtain the MA community (size fraction: > 3 µm) and FL community (size fraction: 0.22–3 µm) for further analysis. Water samples in the control bottles were filtered directly through 0.22-µm pore-size polycarbonate membranes (47-mm diameter, Millipore) at a pressure of < 0.03 MPa to obtain the control community. A total of eight samples for metagenomic and metaproteomic analyses were collected from the control tanks (two biological replicates), the FL (three biological replicates), and the MA (three biological replicates) microbial community.

Nucleic acid extraction was carried out using the modified phenol-chloroform-isoamyl alcohol method [50]. Briefly, membranes were cut into small pieces, transferred into tubes (2-mL volume) containing beads and lysis buffer (100-mM EDTA, 50-mM Tris, and 0.75-M sucrose), and then homogenized using the MP FastPrep-24 instrument (MP Biomedicals). The homogeneous liquid samples were digested with lysozyme, proteinase K, and sodium dodecyl sulfate. Nucleic acids were extracted from the lysates with NaCl (5 M) and phenol-chloroform-isoamyl alcohol (25:24:1). Residual phenol was removed using chloroform-isoamyl alcohol (24:1), and the nucleic acids were precipitated, collected, and cleaned using ice-cold isopropanol and 70% ethanol. Concentration and purity of nucleic acid extracts were quantified and analyzed using Quant-iT™ PicoGreen™ dsDNA Assay Kit (Invitrogen) and a NanoDrop ND-1000 spectrophotometer, respectively.

Protein extraction was carried out according to Dong et al. [51] and Zhao et al. [8] with some modifications. Briefly, membranes were cut into small pieces and suspended in ~ 5-mL lysis buffer containing urea (7 M), thiourea (2 M), dithiothreitol (DTT, 1%), 3-[(3-cholamidopropyl)-dimethylammonium]-1-propanesulfonate (CHAPS, 2%), and protease inhibitor cocktail. The suspension was shaken and homogenized using a Vortex-Genie 2 mixer with a vortex adapter (MO BIO) at moderate speed for 12 h at room temperature and then centrifuged at 7500 × g for 15 min at room temperature to separate the supernatant from the membrane pieces. The supernatant was concentrated using a 3000-Da Amicon Ultra-15 Centrifugal Filter Unit (Millipore), and the protein concentrations were determined using a Pierce 660-nm Protein Assay (Thermo Fisher Scientific). The protein solution was subjected to two methods of digestion, in-gel and in-solution trypsin digestions (1:100, w/w), and then incubated at 37 °C overnight. After digestion, the peptides were processed into freeze-dried powders. The freeze-dried powders were then resuspended in 1% (final concentration) trifluoroacetic acid (TFA) and desalted using C18 Tips (Thermo Fisher Scientific).

Metagenomic sequencing, assembly, and annotation

The nucleic acid extracts were sent to Microsynth AG, Switzerland, for sequencing. Reads from each metagenomic dataset were individually assembled using MEGAHIT (v1.1.2) [52] with default settings. Putative genes were then predicted on contigs longer than 200 bp using Prodigal (2.6.3) [53] in metagenome mode (− p meta). The abundance (RPKM) of each predicted gene was evaluated by mapping the reads back using the BWA algorithm (0.7.16a) [54]. For all the predicted genes, function annotation was performed using the eggNOG database (v5.0) [55] via eggNOG-mapper [56], clusters of orthologous genes (COG) database [57], and Kyoto Encyclopedia of Genes and Genomes (KEGG) database [58]. The taxonomic affiliation of sequences was determined using the lowest common ancestor algorithm adapted from DIAMOND (0.8.36) [59], by performing a search against the nonredundant (NR) database. The taxonomic determination was based on the top 10% hits with an e-value < 1 × 10−5 (–top 10).

For CAZymes, the genes were annotated using hmmsearch against the dbCAN database (e-value < 1 × 10−10, coverage > 0.3) [60]. KO groups such as K00026, K00055, K00108, K11517, K18370, and K19511 were manually checked and classified as AAs of CAZymes according to function description and EC numbers. SignalP (4.0) was used to detect the presence of signal peptides for bacterial sequences [61]. For TBDTs, substrate specificity was predicted by searching (blastp) TBDTs against a curated TBDT sequence database of pure cultures with a threshold of identify ≥ 30% [62].

Metagenome-assembled genomes (MAGs) construction

Reads were co-assembled using MEGAHIT (v1.1.2) [52] to construct metagenome-assembled genomes (MAGs). The contigs were clustered using two separate automatic binning algorithms: MaxBin⁠ [63] and MetaBAT [64]. The generated bins were automatically dereplicated and aggregated using Metawrap [65]. Bins meeting the criteria of ≥ 50% completeness and < 10% contamination were retained for further analysis. In total, 17 MAGs exhibited a completeness of greater than 90% with less than 5% contamination (MIMAGs category of “high quality” or “near complete”), and 60 MAGs showed a completeness of 50–90% with contamination < 10% (MIMAGs category of “medium quality”). Short reads from each metagenome were mapped to the bins using the BWA algorism (0.7.16a) [54]. Gene prediction for each bin was performed using Prodigal (2.6.3) [53]. Gene annotation was performed using KofamKOALA [58].

Phylogenomic analysis

The multiple sequence alignment (MSA) from the 77 prokaryotic MAGs (high and medium quality) was produced by the Genome Taxonomy Database toolkit (GTDB-TK) [66]. The phylogenomic tree of MSA was then constructed using RAxML (version 8.2.12) under all-in-one analysis mode (LG + G8 + F) [67]. The visualization was carried out using the Interactive Tree of Life (http://itol.embl.de/) [68].

Metaproteomic analysis, identification, annotation, and quantification

The peptides were analyzed using a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Scientific). The MS/MS spectra from each proteomic sample were searched against the protein database from the total community and each bin using SEQUEST-HT engines [69]. The resulting matches were further validated using Percolator in Proteome Discoverer 2.1 (Thermo Fisher Scientific). To minimize the probability of false peptide identification, the target–decoy approach [70] was employed. Only results with a peptide-level false discovery rate (FDR) < 1% were retained. For protein identification, a minimum of two peptides and at least one unique peptide were required. Protein quantification was conducted using a label-free quantitative method based on chromatographic peak area [71]. The metaproteomic results of each sample are shown in Supplementary Table 2.

Statistical analysis and visualization

Paired and unpaired t-test and nonparametric Wilcoxon tests were used to compare two variables because normal distribution of the individual data sets was not always met. The differentially expressed proteins (DEPs) among size fractions for each MAG were identified using edgeR (|log2FC|≥ 1, adjusted P-value < 0.05). Nonmetric multidimensional scaling (NMDS) analysis was used to assess the similarity between samples based on Bray–Curtis similarities using PRIMER v5 software [72]. Similarity tests for differences between samples were performed using a one-way analysis of similarity (ANOSIM) [72]. Other statistical analyses, including α-diversity (Shannon index), Bray–Curtis dissimilarity, and PERMANOVA analysis, were performed using the vegan package in R. Data visualization was performed using SigmaPlot v12.5, as well as the ggplot2 and ComplexHeatmap packages in R.

Results

Structure and function of MA and FL prokaryotic communities in the marine snow culture system

The prokaryotic abundances in the marine snow culture system showed that the total community entered early stationary phase after approximately 30 h, reaching abundances of ~ 4.7 × 107 and ~ 3.3 × 109 cells mL−1 for the FL and MA communities, respectively. The number of prokaryotes in the control treatment (without marine snow addition) remained nearly constant (4.9–8.6 × 106 cells mL−1) (Supplementary Fig. 3).

The community structure, taxonomic diversity, and functional variability were examined through combined metagenomic and metaproteomic analyses. The α-diversity of both taxonomic (unpaired t-test, P< 0.01) and functional (unpaired t-test, P < 0.05–0.01) composition in the metaproteomes was significantly higher compared to the results obtained from the metagenomes, except for the taxonomic composition of the MA community (Fig. 1a, b, Supplementary Table 3). The FL community exhibited lower α-diversity in taxonomic composition compared to the MA community in the metaproteomes (unpaired t-test, P < 0.05; Fig. 1a, Supplementary Table 3) but displayed higher α-diversity than the MA community at functional levels in both the metagenomes and metaproteomes (unpaired t-test, P<0.01; Fig. 1b, Supplementary Table 3). The Bray–Curtis dissimilarity indicated a significantly higher degree of taxonomic variation between the FL and MA fraction in the metagenomes than the metaproteomes (unpaired t-test, P < 0.01; Fig. 1c), whereas the metaproteomes exhibited greater functional versatility (unpaired t-test, P < 0.01; Fig. 1d). This observation suggests a more variable expression of proteins/enzymes within the microbial community in response to the addition of marine snow. The functional annotations at the COG level further support this observation. In the metagenomes, despite shifts in both taxonomic and functional profiles observed in both the FL and MA fractions following marine snow amendment compared to the control, the changes in relative abundances of COG categories were low (Fig. 1e, Supplementary Fig. 4a, c, Supplementary Fig. 5a, Supplementary Data 1).

Fig. 1
figure 1

Taxonomic and functional profile of free-living (FL) and marine snow-attached (MA) communities in the marine snow culture system. a Alpha-diversity (Shannon index) of taxonomic and b functional composition in the FL and MA communities at both the gene and protein levels. F, significant difference in the FL fraction between the gene and protein levels (P < 0.05‒0.01); M, significant difference in the MA fraction between the gene and protein levels (P < 0.01); D, significant difference in the metagenome between the FL and MA communities (P < 0.01); P, significant difference in the metaproteome between the FL and MA communities (P < 0.05‒0.01). c Bray–Curtis dissimilarity in taxonomy and d function between the FL and MA communities at both the gene and protein levels. The boxes show the mean, median, and interquartile range (IQR); the whiskers show 1.5 times the IQR of the lower and upper quartiles or the data range. **P < 0.01. e Scatter pie charts showing the compositions and relative abundances of the gene- and f protein-based taxonomic contribution to the major functions, including C, E, J, O, and P categories, based on the Clusters of Orthologous Groups (COGs) database at the phylum level (order level for Alpha- and Gammaproteobacteria and class level for other proteobacteria). C, control community

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In contrast, in the metaproteomes, significant differences were observed for the following COG functional categories: energy production and conversion (C), translation, ribosomal structure and biogenesis (J), posttranslational modification, protein turnover, chaperones (O), and inorganic ion transport and metabolism (P) following marine snow amendment (PERMANOVA, P < 0.01; Fig. 1f, Supplementary Fig. 4b, d, Supplementary Fig. 5b, Supplementary Data 1). The relative abundance of proteins involved in amino acid transport and metabolism (E) showed distinct differences between the FL and MA communities (Fig. 1f, Supplementary Fig. 5b). Both the metagenomes and metaproteomes indicated a shift in community dominance from Alphaproteobacteria to Gammaproteobacteria following the addition of marine snow. Specifically, more than 84% of the prokaryotes in both the FL and MA communities belonged to the orders Alteromonadales, Vibrionales, and Oceanospirillales. In addition, Rhodobacterales became the dominant alphaproteobacterial taxon after marine snow addition, while Pelagibacterales, which originally dominated the community, became rare (Fig. 1e, f, Supplementary Fig. 5).

Furthermore, clustering the proteins into KEGG Orthology (KO) groups revealed that the addition of marine snow resulted in highly expressed proteins/enzymes observed in specific functional categories, including auxiliary activities (AAs) of CAZyme subfamilies, transporters, two-component pathways, biofilm formation, oxidative phosphorylation, and ribosomes. Notably, AAs and transporters were found to be overexpressed in the MA compared to the FL community (paired t-test, P < 0.05; Fig. 2, Supplementary Data 2). These results align with the higher Bray–Curtis dissimilarity (β-diversity) between FL and MA communities observed in the overall functional profiles of the metaproteomes compared to the metagenomes (Fig. 1d), providing further support for the hypothesis that a specific set of key proteins/enzymes drives the microbial community response to environmental perturbations, such as marine snow amendment. Pronounced changes in these key proteins/enzymes revealed by the metaproteomics analysis may be overlooked if solely analyzing metagenomes.

Fig. 2
figure 2

Relationship between the relative abundance of genes and protein expression of KEGG Ontology (KO) groups. a Relationships in the free-living (FL) community. b Relationships in the marine snow-attached (MA) community. Dashed lines are the 1:1 line. The shadow lines represent error bars derived from three biological replicates in both FL and MA treatments. Some error bars are not visible because they are too small and fall within the boundaries of the dots

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Characteristics of marine snow degradation as revealed by essential proteins

The relative abundances of AAs, ATP-binding cassette transporters (ABCTs), and TonB-dependent outer membrane transporters (TBDTs) were higher at the protein compared to the gene level, indicating a preference for the expression of CAZymes and transporter proteins as a response to marine snow amendment (Fig. 2, Supplementary Data 2). These enzymes/proteins primarily facilitate the microbial community in the degradation of algal debris and assimilation of substrates [6, 8, 9, 18, 73]. Therefore, we analyzed the taxonomic and functional composition of CAZymes and transporters. The taxonomic α-diversity of genes encoding CAZymes and transporters was higher in the MA community in both metagenomes and metaproteomes (unpaired t-test, P < 0.01; Fig. 3a, Supplementary Table 4). However, the functional α-diversity showed an opposite pattern, with the FL community exhibiting a higher α-diversity (unpaired t-test, P < 0.01; Fig. 3b, Supplementary Table 4). In addition, the taxonomic and functional α-diversity of CAZymes and transporter proteins was consistently higher in the metagenomes compared to the metaproteomes (unpaired t-test, P < 0.01; Fig. 3a, b, Supplementary Table 4) in stark contrast to the overall taxonomic and functional α-diversity (Fig. 1a, b, Supplementary Table 3). The decreased diversity in CAZyme and transporter proteins in the metaproteome indicates that although most of the microbes carries corresponding genes, the expression was dominated by certain taxonomic groups that likely played a leading role in marine snow degradation. It is also notable that the resolution of mass spectrometry-based metaproteomic analysis is lower than metagenomic sequencing, leading to an underestimation of α-diversity at the protein level. The Bray–Curtis dissimilarity in both taxonomic and functional composition of genes encoding CAZymes and transporters between the FL and MA communities was higher in the metagenomes compared to the metaproteomes (unpaired t-test, P < 0.01; Fig. 3c, d).

Fig. 3
figure 3

Taxonomic and functional profile of carbohydrate-active enzymes (CAZymes) and transporter proteins in free-living (FL) and marine snow-attached (MA) communities in the marine snow culture system. a Alpha diversity (Shannon index) of taxa and b functions of CAZymes and transporter proteins in free-living (FL) and marine snow-attached (MA) communities at both the gene and protein levels. F, significant difference in the FL fraction between the gene and protein levels (P < 0.01); M, significant difference in the MA fraction between the gene and protein levels (P < 0.01); D, significant difference in the metagenome between the FL and MA communities (P < 0.01); P, significant difference in the metaproteome between the FL and MA communities (P < 0.01). c Bray–Curtis dissimilarity in taxa and d functions of CAZymes and transporter proteins between the FL and MA communities at both the gene and protein levels. The boxes show the mean, median, and interquartile range (IQR); the whiskers show 1.5 times the IQR of the lower and upper quartiles or the data range. **P < 0.01. e Bar charts showing the taxonomic composition and relative abundance of CAZymes and f transporters at the phylum level (order level for Alpha- and Gammaproteobacteria and class level for other proteobacteria). g Stack bar charts showing the functional composition and relative abundance of CAZymes and h transporters. AA, auxiliary activity; CBM, carbohydrate-binding module; CE, carbohydrate esterase; GH, glycoside hydrolase; GT, glycosyl transferase. ABCT, ATP-binding cassette transporter; TBDT, TonB-dependent transporters; PTS, phosphotransferase system; TRAP-T, tripartite ATP-independent periplasmic transporter. C, control community

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Compared to the control, pronounced shifts were observed in the taxonomic composition of prokaryotes expressing CAZymes and transporters after the marine snow addition as revealed by the metaproteomes (Fig. 3e, f, Supplementary Fig. 6a, b). Alteromonadales and Chromatiales (Gammaproteobacteria) became the dominant groups, accounting for 95% and 100% of the total CAZymes in the FL and MA communities, respectively (Fig. 3e). CAZymes with AAs accounted for 48% and 66% of total CAZymes in the marine snow culture system in the FL and MA communities, respectively (Fig. 3g). We further categorized CAZymes into two types, namely periplasmic (cell-associated) and cytoplasmic (intracellular) enzymes, based on the presence of signal peptides [8, 74]. The results indicated that the AA2 enzymes (class II peroxidase) were predominantly located in the periplasmic space, constituting 79% and 75% of the periplasmic CAZymes in the FL and MA communities, respectively (Supplementary Table 5a). Glycosyl transferase (GT) was more abundant in the cytoplasmic than the periplasmic space (Supplementary Table 5). Moreover, a wide variety of CAZyme subfamilies was found in the cytoplasmic space of the FL community, in comparison to those in the MA community (Supplementary Table 5b). The relative abundance of cytoplasmic CAZymes in the FL community was also significantly higher than that in the MA community (Wilcoxon rank-sum test, P < 0.05; Supplementary Table 5b).

TBDTs were the major transporters utilized by microbes for substrate assimilation, constituting approximately 49% of the total transporter proteins in both the FL and MA communities after the addition of marine snow (Fig. 3h). The dominant taxonomic groups expressing TBDTs in both the FL and MA communities were members of Alteromonadales, with minor contributions from Rhodobacterales (1.8% in the FL and 3.3% in the MA community) and Bacteroidetes (5.4% in the FL and 3.1% in the MA community, Supplementary Fig. 6b). Substrate specificity analysis showed that TBDTs were predominantly involved in the uptake of DOM, contributing to 60% in the FL community and 57% in the MA community while also contributing to the uptake of iron-rich substrates (Supplementary Fig. 6d). ABCTs, as the prevalent and abundant transporters, exhibited relatively high expression levels in the control (90%) but decreased after marine snow addition (41% in FL and 50% in MA; Fig. 3h). These decreases coincided with a taxonomic shift from Pelagibacterales to Vibrionales and Rhodobacterales (Fig. 3f, Supplementary Fig. 6a). Substrates of ABCTs primarily included monomers such as amino acids, carbohydrates, and inorganic ions (Fig. 3h, Supplementary Fig. 6c). In addition, the phosphotransferase system (PTS) transporters and tripartite ATP-independent periplasmic transporters (TRAP-Ts) were also identified in the FL communities, comprising 7% and 3.2% of the total transporter proteins, respectively (Fig. 3h). Notably, all PTS transporters were found to originate from the Vibrionales.

Furthermore, two-component (signal transduction) pathways (TCPs) and bacterial secretion systems (BSSs) were also examined, as they have been shown to play important roles in marine snow degradation [31]. TCPs are implicated in mediating the response of bacteria to external stimuli, and BSSs are implicated in competing with nearby microorganisms [75, 76]. In the marine snow culture system, the taxonomic and functional compositions of TCPs and BSSs changed significantly compared to the control following the addition of marine snow (Supplementary Fig. 7). The major TCPs in both the FL and MA communities included proteins involved in the osmolarity response regulator protein R (OmpR) family, nitrogen regulation protein NR(I) (NtrC) family, chemotaxis (CheA) family, and polar flagellar synthesis and redox response (Supplementary Fig. 7 g, h, i, j, k), while the major BSSs consisted of proteins involved in the Type I secretion system (T1SS) and general secretion (Sec) pathways (Supplementary Fig. 7 l). The main group expressing these proteins was Alteromonadales in both the FL and MA fractions, and, in addition, Vibrionales in the FL community also significantly expressed these proteins (Supplementary Fig. 7a, b, c, d, e, f).

Phylogenetic affiliation and protein expression of dominant prokaryotic MAGs degrading marine snow

To identify the key bacteria degrading marine snow, we performed metagenomic co-assembly analysis and recovered 77 MAGs. These MAGs were grouped into five clades based on phylogenetic analysis, including Alphaproteobacteria, Gammaproteobacteria, Verrucomicrobiota, and Bacteroidetes (Fig. 4). By mapping the metagenomic reads back to the MAGs, we observed a niche separation among the MAGs. Three MAGs (bin.74, bin.22, and bin.20) belonging to Gammaproteobacteria showed the highest abundance in the marine snow culture system, surpassing that found in the control by two to three orders of magnitude (Fig. 4, Supplementary Fig. 8, Supplementary Data 3, Supplementary Data 4). Bin.74, bin.22, and bin.20 were further identified as members of the genera Alteromonas, Thalassotalea, and Vibrio, respectively. Among them, bin.74 was the most abundant, accounting for 52% and 42% of the reads mapped to all MAGs in the marine snow culture system for the FL and MA communities, respectively (Supplementary Data 4). Bin.20 exhibited a preference for the FL community, representing 20% of the reads mapped to all MAGs, whereas bin.22 showed a higher abundance in the MA community with 19% (Supplementary Data 4). The three MAGs exhibited a disproportionate increase compared to the control and became the predominant species by presumably assimilating substrate from phytoplankton-derived marine snow. In addition, another MAG, bin.70, identified as a member of the genus Tenacibaculum affiliated with Bacteroidetes, also exhibited an increase after marine snow addition accounting for 5% and 2% of the reads mapped to all MAGs in the FL and MA communities, respectively (Supplementary Data 4). The relative abundance of other prokaryotic clades, such as most members of Alphaproteobacteria and Verrucomicrobiota, experienced a significant decrease after marine snow addition (Fig. 4, Supplementary Fig. 9).

Fig. 4
figure 4

Phylogenomic placement of the 77 prokaryotic metagenome-assembled genomes (MAGs) recovered in the marine snow culture system. Five main clades affiliated with Alphaproteobacteria (blue), Gammaproteobacteria (orange), other Proteobacteria (green), Verrucomicrobiota (pink), and Bacteroidetes (yellow) are indicated by differently colored fonts. Dominant MAGs are shown in black bold italics. The log-transformed reads per kilobase per genome (RPKG) values of prokaryotic MAGs in each sample are shown in the heatmap. The basic information, including completeness (cyan), contamination (blue), G + C (green), size (orange), and N50 (yellow), is shown in the bar chart. Bootstrap values ≧ 50% are shown in the phylogenetic tree by gray circles; the size of the circle corresponds to the magnitude of the bootstrap value. The scale bar represents the average number of amino acid substitutions per site. C, control; FL, free-living community in the marine snow micrcosm; MA, marine snow-attached community in the marine snow microcosm

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By integrating metaproteomic expressions with the functional profiles of key MAGs (bin.74, bin.22, and bin.20), we identified a distinct set of key enzymes/proteins expressed by each bacterium in response to marine snow addition. Thirty-eight to 49 proteins were found to be differentially expressed across the three bins (Fig. 5a, b, c). In bin.74 (Alteromonas), functional proteins associated with outer membrane receptor proteins, ribosomal proteins, and flagellin were upregulated (adjusted P-value < 0.05) in the FL and MA communities during growth in the marine snow culture system as compared to the control. This indicates a strong metabolic response of Alteromonas members to marine snow. Flagellin can help microbial colonization on particles, and the outer membrane proteins facilitate substrate uptake, which boost cellular metabolism such as protein synthesis, as reflected by the upregulation in ribosomal proteins [31, 73, 77]. These proteins were predominant in both FL and MA fractions. We also found that the catalase-peroxidase (KatG) was the only CAZyme detected at the protein level in bin.74 in all treatments, while it was absent in other bins (Fig. 5a). Methyl-accepting chemotaxis protein (MCP) displayed a notable upregulation in the FL compared to the MA fraction (Log2FC = 1.29, adjusted P-value < 0.01) and control treatment (Log2FC = 0.74, adjusted P-value < 0.05; Fig. 5a). KatG belongs to the AA2 of the CAZyme subfamily and plays an important role in detoxification of peroxide intermediates generated during the degradation of marine snow [78], whereas MCPs serve as primary chemoreceptors in prokaryotes, facilitating to sense environmental conditions, and move away from toxic chemicals [79]. Therefore, the characteristics exhibited by these two proteins of bin.74 in response to marine snow addition may contribute to its predominance in the marine snow culture system.

Fig. 5
figure 5

Heatmaps protein expression pattern of the dominant taxa (a bin.74, b bin.22, c bin.20, and d bin.70) involved in marine snow degradation across each sample. The expression data represent the relative abundance over the proteome associated with each of the metagenome-assembled genomes (MAGs). The expressed proteins in each MAG with a relative abundance > 1% in at least one sample are shown. The colors carmine (bin.74), purple (bin.22), orange (bin.20), and yellow (bin.70) represent upregulated proteins (adjusted P-value < 0.05); the colors blue and white represent downregulated proteins (adjusted P-value < 0.05) and nonsignificantly regulated proteins, respectively. DEP, differentially expressed protein among size fractions for each MAG

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Bin.22 (Thalassotalea) exhibited a preference for the MA fraction, as indicated by both metagenomic and metaproteomic analyses (Fig. 4, Supplementary Table 6, Supplementary Data 4). Specifically, iron complex outer membrane receptor proteins (TC.FEV.OM), as one type of TBDT proteins facilitating the uptake of diverse compounds, were observed to be notably upregulated in the MA compared to FL fraction (Log2FC = 2.99 ~ 3.81, adjusted P-value < 0.01) and the control treatment (Log2FC = 3.09 ~ 5.97, adjusted P-value < 0.05; Fig. 5b). Additionally, proteins involved in chemotaxis, ribosomal proteins, DNA synthesis and replication, and amino acid synthesis were found to be upregulated (adjusted P-value < 0.05) in both FL and MA communities during growth in the marine snow culture system as compared to the control. These results from bin.22 also indicate a pronounced proliferation benefiting from marine snow, akin to bin.74.

Bin.20 (Vibrio) was predominantly found in the FL fraction (Fig. 4, Supplementary Table 6, Supplementary Data 4) and displayed a distinct functional expression pattern compared to bin.74 and bin.22. After the marine snow addition, proteins involved in chemotaxis, ABC transporters, amino acid synthesis, carbohydrate metabolism, and ribosomal proteins were upregulated in both the FL and MA fractions (adjusted P-value < 0.05; Fig. 5c). The upregulation of these proteins suggests that Vibrio may have enhanced the metabolism of low-molecular-weight (LMW) hydrolysates from marine snow. In addition, bin.70 from Bacteroidetes was identified as another crucial bacterium involved in the degradation of marine snow. It predominantly expressed outer membrane receptor proteins (Fig. 5d). Overall, the expression levels of proteins related to chemotaxis, surface attachment, and substrate assimilation shape the metabolic responses of individual taxa to marine snow, thereby providing a molecular basis for microbial niche separation within marine snow.

Discussion

Microbial cleavage of marine snow plays an essential role in the marine carbon cycle. The taxonomic and functional response of the microbial community to marine snow amendment is key to understanding this process. Here, we employed a combined metagenomic and metaproteomic analysis to elucidate the taxonomic and functional aspects of microbial metabolism after marine snow amendment.

Prominent populations promoting the degradation of marine snow

Members of Alteromonadales and Vibrionales became the predominant groups in both FL and MA communities after marine snow amendment (Fig. 1e, f, Supplementary Fig. 5). This finding is consistent with previous studies that have shown a significant increase in the growth of Alteromonadales populations following the addition of high-molecular-weight DOM [80], incubations amended with polysaccharides (such as laminarin) [81], and nutrient addition [82]. Also, it has been reported that members of the Vibrionales are enriched in marine snow, responding to phytoplankton-derived substrates such as low-molecular-weight DOM (LMW-DOM) and iron [15, 31]. It has also been demonstrated that when the concentrations of total organic carbon and nitrogen are very high in the environment, the relative abundance of Vibrio increased disproportionately, accounting for more than half of the prokaryotic community [83]. Despite an overall slight decline in the Bacteroidetes population, the relative abundance of a specific member (Thalassotalea bin.70) within the Bacteroidetes increased in response to the marine snow addition, particularly in the FL fraction (Fig. 1e, f, Fig. 4, Supplementary Fig. 5, Supplementary Table 6, Supplementary Data 3, Supplementary Data 4). Krüger et al. [35] documented a comparable finding, highlighting the proliferation of only a few free-living Bacteroidetes during the microalgal blooms.

Essential degradation mechanisms in the marine snow culture system

The functional profile of COG categories and KO groups showed significant differences between the metagenomes and metaproteomes (Figs. 1, 2, Supplementary Data 1, Supplementary Data 2). Approximately 38 KO groups involved in the degradation of marine snow were significantly expressed, with their relative abundance in the metaproteomes being one to three orders of magnitude higher than that in the metagenomes (Fig. 2). These observations indicate that the metabolic responses to marine snow addition are more pronounced than the genetic responses. CAZymes are the key enzymes involved in the degradation of algal debris, while TBDTs are responsible for the assimilation of hydrolysates [36, 84]. In our experiment, the relative abundance of CAZymes and TBDTs at both the metagenomic and metaproteomic levels increased after marine snow amendment (Fig. 3g, h, Supplementary Fig. 10, Supplementary Data 5). The diversity (Fig. 3a, b) and variation (Fig. 3c, d) of CAZymes and transporters were lower at the metaproteomic than at the metagenomic level, in contrast to the trends found in the overall taxonomy and function (Fig. 1a, b). These observations can be attributed to Alteromonadales and Chromatiales becoming the major producers of CAZymes (Fig. 3e), and Alteromonadales are the major producers of TBDTs following marine snow amendment (Supplementary Fig. 6b). These results indicate the synergistic expression of key CAZymes and transporters by a few highly abundant species (i.e., Alteromonadales and Chromatiales) was crucial for the degradation of complex carbohydrate polymers, such as marine snow [6,7,8, 11, 18, 73].

Specifically, AA2 enzymes were predominantly identified in both FL and MA communities (Fig. 3g, Supplementary Fig. 10, Supplementary Table 5, Supplementary Data 5). These enzymes play a crucial role in detoxifying peroxide intermediates generated during the degradation of marine snow due to their high redox potential [78]. Thus, the high relative abundance of periplasmic AA2 indicates their significant involvement in the degradation of phytoplankton-derived marine snow. Additionally, other CAZymes, including AA3_2, AA7, and AA12, identified through different annotation databases were also detected in FL and/or MA communities (Fig. 2, Fig. 3g, Supplementary Table 5, Supplementary Data 2, Supplementary Data 5). These enzymes play a key role in cellulose degradation [85]. Their occurrence further supports the hypothesis that AA enzymes of the CAZyme subfamily are important in the degradation of marine snow. TBDTs were identified as the predominant transporters in both FL and MA communities (Fig. 3h). These findings align with previous studies [33, 34, 36] which also observed a high level of outer membrane TBDT expression during phytoplankton blooms. The most abundant TBDTs identified in our culture system were involved in the uptake of DOM (Fig. 3h, Supplementary Fig. 6d) [9, 73, 86]. A taxonomic analysis at the order level revealed that Alteromonadales were the major contributors to the expression of CAZymes and TBDTs in both the FL and MA communities following marine snow addition (Fig. 3e, Supplementary Fig. 6b). Previous studies have reported similar observations, where Alteromonadales actively secreted extracellular hydrolytic enzymes and responded to variations in organic matter [4, 8, 80, 82]. Also, TBDTs were identified as some of the most abundant transcripts in a DOM-enriched marine microcosm [80]. Previous studies have also indicated that Gammaproteobacteria and Bacteroidetes express TBDTs at high levels, facilitating their acquisition of polymeric matter and other nutrients derived from phytoplankton [9, 62, 87, 88].

In contrast, the relative abundance of ABCTs significantly decreased after the addition of marine snow (Fig. 3h). However, in the FL community, a more diverse range of ABCT subfamilies associated with the transport of amino acids and carbohydrates was found, primarily originating from members of Rhodobacterales and Vibrionales (Supplementary Fig. 11). This suggests that in the ambient water, a wider range of substrates is available for utilization [89,90,91], likely due to the release of diverse-free enzymes into the ambient seawater during the hydrolysis of marine snow [15, 16, 90]. Previous observations have demonstrated the dominance of the Rhodobacterales taxa, particularly in copiotrophic environments, attributed to their metabolic versatility and adaptability to marine snow facilitated by ABCTs [88, 92, 93].

In addition, the PTS and TRAP-Ts systems were expressed at low abundances in the metaproteomes following the addition of marine snow (Fig. 3h). The PTS is also a crucial transport system for bacterial uptake of various carbohydrates and plays a major role in carbon catabolite repression, an intrinsic substrate preference mechanism in many microorganisms when preferred sugars are scarce [94, 95]. In our marine snow culture system, the low relative abundance of PTS was primarily detected in the FL community due to the likely presence of preferred sugars. TRAP-Ts typically transport C4-dicarboxylates and confer advantages under oligotrophic, deep-sea conditions [9, 62]; hence, they were generally scarce in our culture system.

Identification of key players in the degradation of marine snow

Analysis of the MAGs allows identification of key participants in the degradation of marine snow, while the proteome profile of each MAG unveils distinct cellular responses to the availability of substrates. After the addition of phytoplankton-derived organic matter and the formation of marine snow, members of Gammaproteobacteria, including Alteromonas (bin.74), Thalassotalea (bin.22), and Vibrio (bin.20), exhibited a pronounced increase in abundance within the FL or/and MA fractions compared to the other bins, followed by Tenacibaculum (bin.70) from Bacteroidetes in the FL fraction (Fig. 4, Supplementary Data 3, Supplementary Data 4, Supplementary Fig. 9). Overall, this aligns with our analysis that members of Gammaproteobacteria emerged as the the most dominant group at both the metagenomic and metaproteomic level. Compared with the MAGs in the Ocean Microbiomics Database (https://microbiomics.io/ocean/), the MAGs recovered from our research showed relatively low average nucleotide identity (ANI) (ANI < 97%, Supplementary Data 6), indicating their relatively low prevalence in the oligotrophic ocean. This result suggests that the addition of marine snow stimulated the enrichment of a group of bacterial opportunists (especially for bin.74, ANI = 80%), which preferentially take up hydrolysates from marine snow degradation. Furthermore, this is consistent with previous findings indicating that members of Gammaproteobacteria, Bacteroidetes, and Rhodobacteraceae often dominate and synergistically hydrolyze and utilize organic matter from marine snow [96], within the phycosphere [31, 97, 98], during blooms [32, 33], or in diffusion-limited systems [8, 99, 100]. However, other study reported different findings, highlighting Bacteroidetes communities primarily consist of a few proliferating free-living clades during microalgal blooms [35, 101,102,103]. While these four MAGs exhibited a relatively high abundance, three MAGs, affiliated with Gammaproteobacteria, demonstrated low completeness (Fig. 4, Supplementary Data 3, Supplementary Data 4, Supplementary Data 7). The low completeness might be caused by the high microdiversity within this clade, which coincided with the low prevalence of the three MAGs in the open ocean and their potential as bacterial opportunists for nutrient-rich conditions (Supplementary Data 6). Neverthless, a clear niche separation was observed among these key taxa. The expression profile of the proteome may provide an explanation for such niche separation (Fig. 5). Alteromonas bin.74 was the most dominant group in responding to marine snow within our culture system, demonstrating the capability to express secretory AA2 proteins (KatG) related to the detoxification of peroxide intermediates generated during the cleavage of marine snow (Fig. 5a) [78]. Moreover, there was a significant enrichment of various expressed proteins from bin.74, such as iron complex outer membrane receptor protein, OmpA-OmpF porin, and flagellin (Fig. 5a). These proteins primarily facilitate the utilization of preferred substrates in response to changes in substrate composition [4, 31, 80].

The expression profile of Thalassotalea bin.22 was largely similar to that of bin.74, except for the AAs proteins (KatG). The absence of CAZymes in bin.22 suggests that Thalassotalea members may rely on the breakdown products of marine snow. In addition, the upregulated proteins involved in TonB-dependent receptors, chemotaxis (CheA family), and flagellar proteins in bin.22 after marine snow addition, particularly in the MA (Fig. 5b), may explain why Thalassotalea bin.22 is relatively more abundant in the MA than in the FL fraction. In contrast, Vibrio bin.20 was preferentially present in the FL fraction because proteins involved in flagella and/or pilus were absent, despite the upregulation of chemotaxis proteins (Fig. 5c). This finding is consistent with previous studies that suggest Vibrio thrive primarily on dissolved compounds released by algae, particularly LMW-DOM, preferring a free-living lifestyle, at least temporarily [15, 104, 105]. We speculate that the free-living Vibrio bin.20 may be attracted to LMW-DOM-rich hot spots produced by other microorganisms, allowing them to utilize available substrates without incurring unnecessary metabolic costs [106,107,108]. The relative abundance of Bacteroidetes did not significantly change after the addition of marine snow in both the metagenomes and metaproteomes, except for Tenacibaculum bin.70 within Bacteroidetes showing a relatively high abundance in the FL fraction (Fig. 1e, f, Fig. 4, Supplementary Fig. 5, Supplementary Table 6, Supplementary Data 3, Supplementary Data 4). Therefore, Tenacibaculum may constitute a group following Alteromonas (bin.74), Thalassotalea (bin.22), and Vibrio (bin.20) in responding to marine snow in our culture system. Furthermore, starch-binding outer membrane proteins (starch utilization system C and D, SusC/SusD) were upregulated in bin.70 following marine snow addition (Fig. 5d). This aligns with previous research indicating that the Bacteroidetes clade possesses a restricted set of conserved polysaccharide utilization loci (PULs, including genes encoding SusC, SusD, and CAZymes), which facilitate the degradation and utilization of algal polysaccharides [35, 109, 110]. It is well known that the degradation of algal polysaccharides produces hundreds of different compounds, including labile, semilabile, and recalcitrant dissolved organic carbon (RDOC) [33, 111, 112]. Therefore, the efficient degradation of marine snow by the specialized prokaryotic consortium not only reflects metabolic activity and characteristics of individual bacterial species but also has the possibility of producing RDOC.

Conclusions

We conducted an experimental investigation of bacterial degradation of marine snow, which was efficiently colonized. We employed metagenomic, metaproteomic, and binning analyses to explore the interactions between prokaryotes and marine snow, uncovering microbial metabolism of the free-living (FL) and marine snow-attached (MA) bacterial consortium. A schematic diagram illustrates the dominant metabolic processes of the key prokaryotic players in marine snow (Fig. 6). In brief, we utilized CAZymes and transporters to gauge the physiological mechanisms involved in degrading and utilizing marine snow, while also examining proteins associated with chemotaxis and BSSs as indicators of survival strategies on marine snow organic matter. Our findings revealed that Alteromonas (bin.74) was the dominant group in both the FL and MA communities. Alteromonas employ AA2 enzymes of the CAZyme subfamily to detoxify peroxide intermediates generated during the cleavage of marine snow, thereby facilitating their immediate and intact transport into the cells through TBDTs. The released components from marine snow solubilization were preferentially taken up through ABC transporters by Vibrio (bin.20), which primarily existed in a free-living lifestyle, and through TBDTs by Thalassotalea (bin.22), which primarily adopts a marine snow-attached lifestyle. Therefore, it can be inferred that members of the Alteromonas genus are the most dominant microorganisms, likely better adapted to initial degradation of marine snow in both the FL and MA fraction. In contrast, the FL Vibrio and MA Thalassotalea are more likely utilizing hydrolyzed products, with the former predominantly associated with the FL fraction and the latter primarily associated with the MA fraction [73]. These findings suggest that a few prokaryotic groups, including members of Alteromonadales and Vibrionales, prevailed and became the pivotal drivers in the degradation of marine snow through complementary physiological mechanisms and survival strategies (niche differentiation). These results presented in this study contribute to our understanding of how the synergistic effect of a key prokaryotic consortium influences the fate of marine snow.

Fig. 6
figure 6

Schematic diagram illustrating the dominant metabolic processes of the key prokaryotic players in the marine snow culture system. Highly expressed proteins of the top three most abundant MAGs, namely Alteromonas (bin.74), Thalassotalea (bin.22), and Vibrio (bin.20), in marine snow were shown. The relative abundance of expressed proteins in each MAG was larger than or equal to 1% in at least one of the samples. POM, particulate organic matter (i.e., marine snow); DOM, dissolved organic matter; FL, free-living community; MA, marine snow-attached community; MCP, methyl-accepting chemotaxis protein; ABCT, ATP-binding cassette transporters; TBDT, TonB-dependent outer membrane transporter; Gln, glutamine; Cys, cysteine

Full size image

Data availability

All data used in this study are provided in main text and Supplementary materials. Metagenomic reads are deposited in Natural Center for Biotechnology Information (NCBI), and is available under PRJNA1144259. The sequences of metagenome-assembled genomes (MAGs) are accessible through the link on Figshare (https://figshare.com/s/7aa3c60904aa59d865ae). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [113] partner repository with the dataset identifier PXD054591.

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Acknowledgements

We sincerely thank Prof. Tom Battin and Robert Niederdorfer (Limnology Unit, Dept. Functional and Evolutionary Ecology, University of Vienna) for developing the rolling device. We also thank Chie Amano for her assistance with microscopic counting.

Funding

This work was funded by the National Natural Science Foundation of China (NSFC) projects 42125603, 42430404, and 42421004 to YZ, the NSFC project 42206098 and the National Key Research and Development Program of China (2020YFA0608302) to LH, the Austrian Science Fund (FWF) project I4978-B to GJH, and the MEL Visiting Fellowship MELRS2327 to ZZ.

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  1. Lei Hou and Zihao Zhao contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, and College of Ocean and Earth Sciences, Xiamen University, Xiamen, China

    Lei Hou, Zihao Zhao, Nianzhi Jiao & Yao Zhang

  2. Department of Functional and Evolutionary Ecology, University of Vienna, Vienna, Austria

    Zihao Zhao, Barbara Steger-Mähnert & Gerhard J. Herndl

  3. Department of Marine Microbiology and Biogeochemistry, Royal Netherlands Institute for Sea Research (NIOZ), Den Burg, The Netherlands

    Gerhard J. Herndl

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  1. Lei Hou
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Contributions

G.J.H., Y.Z., and N.J. conceived and designed this study. L.H., Z.Z., and B.S.M. did the experiments, and L.H. and Z.Z. performed the analysis. L.H., Z.Z., Y.Z., and G.J.H. interpreted the data. L.H., Z.Z., and Y.Z. wrote the first draft. All authors discussed the results and contributed to the final version of the paper.

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Correspondence to Nianzhi Jiao, Gerhard J. Herndl or Yao Zhang.

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Supplementary Information

40168_2025_2097_MOESM1_ESM.docx

Supplementary Material 1. Fig. 1 Growth curve of Picochlorum sp. Optical density (OD, 550 nm) of Picochlorum sp. during incubation. Supplementary Fig. 2 Rolling tank setup and phytoplankton-derived marine snow formation during incubation. The setup consisted of (a) laboratory power supply, (b) a rolling table, and (c) culture bottles placed on the rolling table. (d–f) Marine snow formation during the incubation. Supplementary Fig. 3 Abundance of prokaryotes during incubation. Open circles represent the free-living (size fraction: 0.22–0.8 µm) prokaryotic abundance in the culture group. Closed circles represent the prokaryotic abundance in a given volume of particles (visible to the naked eyes) in the culture group. Open squares represent the prokaryotic cell abundance in the control treatment without the addition of algal organic matter. Supplementary Fig. 4 Variation in the prokaryotic community structure and functional composition in the marine snow microcosm. (a) Community clustering reflected at the gene- and (b) protein level. (c) Functional clusting reflected at the gene- and (d) protein level. Each triangle (Control, C), open square (Free-living, FL), and solid square (Marine snow-attached, MA) represents an individual sample. Roman numerals represent cluster numbers. Percentages indicate Bray–Curtis similarities of community structure or functional classifications. **, P < 0.01. Supplementary Fig. 5 Taxonomic and functional profile of free-living (FL) and marine snow-attached (MA) communities in the marine snow culture system. (a) Scatter pie charts showing the compositions and relative abundances of the gene- and (b) protein-based taxonomic contribution to functional classification based on the Clusters of Orthologous Groups (COGs) database at the phylum level (order level for Alpha- and Gammaproteobacteria, and class level for other proteobacteria). C, control prokaryotes. FL, free-living prokaryotes. MA, marine snow-attached prokaryotes. Supplementary Fig. 6 Taxonomic and functional composition of major prokaryotic transporter proteins in each sample. (a) Taxonomic composition of ATP-binding cassette transporters (ABCTs), and (b) TonB-dependent transporters (TBDTs) at the phylum level (order level for Alpha- and Gammaproteobacteria, and class level for other proteobacteria). (c) Functional composition and substrate preference of ABCTs, and (d) TBDTs. C, control; FL, free-living community in the marine snow culture system; MA, marine snow-attached community in the marine snow culture system. Arabic numerals represent replicate numbers. Supplementary Fig. 7 Taxonomic and functional composition of prokaryotic proteins involved in two-component (signal transduction) pathways (TCPs, including histidine kinase, response regulator, and enzymes adapted to changing conditions) and bacterial secretion systems (BSSs) in each sample. (a–e) Taxonomic composition of enzymes associated with various families of TCPs and (f) BSSs at the phylum level (order level for alpha- and gammaproteobacteria, and class level for other proteobacteria). (g–k) Functional composition of enzymes associated with various families of TCPs and (l) BSSs. OmpR, osmolarity response regulator protein R; CitB, response regulator DpiB of citrate/malate metabolism; LytTR, litter; NarL, Nitrate/nitrite response regulator protein L; NtrC, nitrogen regulation protein NR(I); LuxR, Luminescence R; Sec-SRP, general secretory-signal recognition particle; C, control; FL, free-living community in the marine snow micrcosm; MA, marine snow-attached community in the marine snow microcosm. Arabic numerals represent replicate numbers. Supplementary Fig. 8 Mean relative abundance of Gammaproteobacteria in the metagenome and metaproteome at the family level. Members of Alteromonadaceae (order Alteromonadales), Colwelliaceae (order Alteromonadales), and Vibrionaceae (order Vibrionales) are highlighted in orange, purple, and green colors, respectively. All other members are shown in grey scale. Supplementary Fig. 9 Mean relative abundance, calculated as the RPKG of each bin divided by the total RPKG of all bins, of 77 prokaryotic metagenome-assembled genomes (MAGs) classified at the genus level. Relative abundance of prokaryotic MAGs contributing more than 2% in at least one sample are highlighted in different colors, while others are shown in grey. Supplementary Fig. 10 The percentage of prokaryotic carbohydrate-active enzymes (CAZymes) in the metagenome (a) and metaproteome (b) of each sample. AA, Auxiliary activity; CBM, Carbohydrate-binding module; CE, Carbohydrate esterase; GH, Glycoside hydrolase; GT, Glycosyl transferase. C, control; FL, free-living community in the marine snow culture system; MA, marine snow-attached community in the marine snow culture system. Arabic numerals represent replicate numbers. N, not detected. Supplementary Fig. 11 Taxonomic and functional composition of prokaryotic ATP-binding cassette transporter (ABCT) proteins in each sample. (a–c) Taxonomic composition of ABCT subfamilies at the phylum level (order level for Alpha- and Gammaproteobacteria, and class level for other proteobacteria). (d–f) Functional composition and substrate preference of ABCT subfamilies. C, control; FL, free-living community in the marine snow microcosm; MA, marine snow-attached community in the marine snow microcosm. Arabic numerals represent replicate numbers.

40168_2025_2097_MOESM2_ESM.zip

Supplementary Material 2. Table 1 Dry biomass concentration of Picochlorum sp. in each rolling bottle. Supplementary Table 2 Statistics of proteins identified in the metaproteome. Supplementary Table 3 Alpha-diversity (Shannon index) of taxonomic and functional compositions in the FL and MA communities at both the gene and protein level. Supplementary Table 4 Alpha-diversity (Shannon index) of taxa and functions of CAZymes and transporter proteins in the FL and MA communities at both the gene and protein level. Supplementary Table 5 Relative abundance of CAZymes in the metaproteome. a. Periplasmic CAZymes. b. Cytoplasmic CAZymes. Supplementary Table 6 Percentages of expressed proteins in each bin across samples. 

40168_2025_2097_MOESM3_ESM.zip

Supplementary Material 3. Data 1: Gene- and protein-based relative abundance in each treatment based on the Clusters of Orthologous Groups (COG) database. Supplementary Data 2. Gene- and protein-based relative abundance in each FL and MA sample based on the KEGG Orthology (KO) database. Supplementary Data 3: Reads per kilobase per genome (RPKG) of prokaryotic metagenome-assembled genomes (MAGs) in each sample. Supplementary Data 4: Relative abundance of prokaryotic MAGs in each sample, calculated as the RPKG of each bin divided by the total RPKG of all bins. Supplementary Data 5: Gene- and protein-based relative abundance in each treatment based on the Carbohydrate-active enzymes (CAZymes) database. Supplementary Data 6: The average nucleotide identity (ANI) values of bin.20, bin.22, bin.70, and bin.77 base on the Ocean Microbiomics database. Supplementary Data 7: Completeness, contamination, GC, size, and N50 of prokaryotic metagenome-assembled genome (MAG) . Supplementary Data 8: Links for each metagenome-assembled genome (MAG) on Figshare.

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Hou, L., Zhao, Z., Steger-Mähnert, B. et al. Microbial metabolism in laboratory reared marine snow as revealed by a multi-omics approach. Microbiome 13, 114 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-025-02097-8

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