Skip to main content
Download PDF

Comparative metagenomics reveals the metabolic flexibility of coastal prokaryotic microbiomes contributing to lignin degradation

Biotechnology for Biofuels and Bioproducts volume 18, Article number: 9 (2025) Cite this article

Abstract

Coastal wetlands are rich in terrestrial organic carbon. Recent studies suggest that microbial consortia play a role in lignin degradation in coastal wetlands, where lignin turnover rates are likely underestimated. However, the metabolic potentials of these consortia remain elusive. This greatly hinders our understanding of the global carbon cycle and the “bottom-up” design of synthetic consortia to enhance lignin conversion. Here, we developed two groups of lignin degrading consortia, L6 and L18, through the 6- and 18-month in situ lignin enrichments in the coastal East China Sea, respectively. Lignin degradation by L18 was 3.6-fold higher than L6. Using read-based analysis, 16S rRNA amplicon and metagenomic sequencing suggested that these consortia possessed varied taxonomic compositions, yet similar functional traits. Further comparative metagenomic analysis, based on metagenomic assembly, revealed that L18 harbored abundant metagenome-assembled genomes (MAGs) that encoded diverse and unique lignin degradation gene clusters (LDGCs). Importantly, anaerobic MAGs were significantly enriched in L18, highlighting the role of anaerobic lignin degradation. Furthermore, the generalist taxa, which possess metabolic flexibility, increased during the extended enrichment period, indicating the advantage of generalists in adapting to heterogenous resources. This study advances our understanding of the metabolic strategies of coastal prokaryotic consortia and lays a foundation for the design of synthetic communities for sustainable lignocellulose biorefining.

Background

Lignin is a major component of plant cell walls, constituting up to 30% constituent of vascular plants [1]. As an abundant, yet complex aromatic polymer, lignin turnover is essential in the global carbon cycle, as well as in sustainable lignocellulosic biorefining. Previous studies on lignin conversion have mostly focused on terrestrial ecosystems, e.g., soil, rain forests and farmlands [2,3,4,5]. In fact, there is a substantial discharge of terrestrial organic carbon (TerrOC) into marine environments, with annual rate of 200 Mt/year [6]. Surprisingly, just 31–45% of this TerrOC discharge is detected in ocean ecosystems [6]. Coastal wetlands, the zones bridging terrestrial and marine ecosystems, play an essential role in carbon cycling [7, 8]. The turnover rate of lignin is undoubtedly underestimated in coastal wetlands.

Coastal microbial consortia were recently revealed to play a role in lignin/lignocellulose degradation [9,10,11]. 16S rRNA and Internal Transcribed Spacer (ITS) amplicon sequencing revealed the presence of the prokaryotic classes Bacteroidia, Deltaproteobacteria, Actinomycetes, Alphaproteobacteria, Gammaproteobacteria, and Bathyarchaeia and the fungal taxon Sordariomycetes in these microbial consortia [9,10,11,12,13,14]. Meanwhile, metagenomic sequencing uncovered the diverse array of lignin degrading functional genes, encoding laccase, vanillin dehydrogenase (Vdh), p-hydroxybenzoate hydroxylase (PobA) and syringate O-demethylase (DesA) [9,10,11]. Together, these studies described the taxonomic and functional profiles of various lignin degrading microbial consortia. However, a mechanistic understanding of the metabolic strategies in these consortia remains elusive.

Metatranscriptomics is a common strategy to reveal gene expression profiles and further infer metabolic mechanisms of lignin degrading consortia. For instance, the bacterial classes Alpha-, Beta-proteobacteria and Bacteroidia in the Pearl River displayed significantly induced expression of vanAB/pcaJ genes, which are involved in the oxidative demethylation of vanillic acid and the protocatechuic acid (PCA) 3,4-cleavage pathway [15]. A deep sea member of the class Bacteroidia had a higher level of gene transcripts that encode MnSOD and FeSOD when grown on wood chips, from which it was inferred that they could oxidize lignin as the carbon source [16]. Notably, the metatranscriptome is a snapshot of gene expression profiles within a community. It remains challenging to perform real-time monitoring of highly dynamic expression profiles of consortia in natural environments. Furthermore, current metagenome and metatranscriptome studies pay more attention to individual lignin degrading functional genes [15, 17]. It is worth noting that similar functional genes of microbial communities with highly variable taxonomic compositions were reported in many ecosystems [18,19,20]. It suggests that lignin degradation gene clusters (LDGCs), rather than individual genes, potentially have more influence on lignin metabolism processes. Microorganisms encode LDGCs to depolymerize lignin and degrade various lignin-derived aromatic compounds under aerobic/anaerobic conditions. They collectively metabolize a diverse array of lignin derivatives, which vary in their chemical structures, catabolic pathways and physiological functions [21, 22]. The major classes include guaiacyl (G), syringyl (S), p-hydroxyphenyl (H) and other types of lignin units [21, 22]. LDGCs enable microbes to utilize lignin and its derivatives as carbon resources, and protect cells from oxidative stress by reactive oxygen species (ROS). Consequently, microbes gain a competitive advantage in nutrients and space, especially in highly disturbed ecosystems. Analysis of LDGCs within each assembled metagenome would provide a more comprehensive genome level understanding of metabolic potentials of microbes that underwent a long-term evolution to stability in these consortia.

Generalist and specialist species commonly co-exist within microbial communities. Generalist are thought to have larger genomes with a diverse array of functional genes, and thus can perform many versatile functions. In contrast, specialists have smaller genomes and thus, perform fewer functions [23, 24]. Specialist species can achieve higher growth rates, as they have a narrow resource utilization range with lower metabolic burden. Conversely, generalist species have a wide range of resources but poor efficiency, due to their higher metabolic burden. Therefore, specialists are expected to have a growth advantage over generalists in their optimal habitats [25,26,27]. Recent studies have suggested that metabolic flexibility, e.g., the ability to switch between aerobic and anaerobic metabolism, is an important factor in controlling the ecology and biogeochemistry of the communities living in frequently disturbed ecosystems [28, 29]. Coastal intertidal zones bridge terrestrial and marine environments, and are characterized by strong dynamic interactions, e.g., tides and river runoff. In this highly disturbed ecosystem, there is the question of whether generalists would be increased to enhance the metabolic potential of consortia? Alternatively, would specialists outcompete generalists in coastal wetlands with diverse abiotic gradients? These questions remain largely unexplored for coastal lignin degrading microbial systems.

In this study, we investigated the metabolic strategies of the two groups of coastal lignin-degrading prokaryotic consortia, L6 and L18. They were developed through an in situ lignin enrichment experiment in coastal intertidal wetlands of the East China Sea, with 6- and 18-month exposure periods, respectively [11]. Through physiological analysis, 16S rRNA amplicon and metagenomic sequencing, we demonstrated that while the two groups, which demonstrated different lignin degrading capacities, exhibited varied taxonomic compositions, they still possessed similar functional traits. Further genome-centric analysis suggested that the L18 consortia, with their higher average lignin degrading capacity, recruited more abundant MAGs, especially anaerobic archaeal Bathyarchaeia and bacterial Desulfobacteria classes, highlighting the importance of anaerobic lignin catabolism in the coastal wetlands. Furthermore, our recently constructed lignin catabolism functional gene database, LCdb, which gathered experimentally validated LDGCs for each pathway, greatly enabled us to annotate LDGCs in MAGs [30]. Diverse and unique LDGCs were, thus, observed with an increase in generalist taxa in the L18 consortia. Comparative metagenomic analysis demonstrated the metabolic strategies of the in situ consortia. This not only advances our mechanistic understanding in these biological systems, but also provides insights into constructing synthetic communities for the efficient lignin conversion.

Methods

In situ enrichment and sample collection

As our previous report [11], in situ lignin enrichment was performed in the intertidal zone (122°6′14.05″ E, 29°56′48.90″ N) of the south-eastern portion of Zhairuoshan Island, ZhouShan, Zhejiang Province, China (Fig. 1A). Kraft lignin (catalog #370,959) was purchased from Sigma-Aldrich (St. Louis, MO, USA). It is from Norway spruce (softwood) and extracted by hot alkaline (sulfate) method, with 2% sulfur impurities and a density of 1.3 g/mL at 25 °C [31]. The incubators contained sterilized solid medium (10 g agar powder and 5 g phytagel in 1 L artificial seawater with a 1% (wt%) lignin) and were fixed in the intertidal zone by ropes to experience the tidal flows. Beginning in April 2019, the sixteen incubators, with eight replicates, were enriched in situ for 6 and 18 months. However, four incubators were lost during the enrichment period. As a result, the samples, with six replicates, were collected at 6 and 18 months, kept on dry ice for transport to the lab, and then stored at – 80 °C, respectively.

Fig. 1
figure 1

The in situ enrichment of lignin degrading consortia in coastal wetlands. A Location of in situ enrichment on the Zhairuoshan Island of East China Sea (122°6′14.05" E and 29°56′48.90" N). B Percentage of lignin consumed by the L6 and L18 consortia. C Bacterial abundance in the L6 and L18 consortia. The 16S rRNA gene copy in each community was monitored by qPCR. D Shannon diversity of each prokaryotic community. E PCoA profile of the L6 and L18 consortia based on the Bray–Curtis dissimilarities. Ellipses indicate the 95% confidence intervals, as represented by different colors. Significant differences between samples are indicated by asterisks (*p < 0.05). Data are mean ± standard deviation, n = 6 biological replicates

Full size image

DNA extraction and metagenomic DNA sequencing

Each 20 g enriched sample was used to extract DNA, based on the CTAB extraction method [32]. As a result, ~ 9.99–57.18 μg DNA/sample was generated and stored at − 80 °C (Table S1). Triplicate DNA samples (200 ng/sample) were randomly selected from the six replicates for metagenomic DNA sequencing, which were performed on an Illumina PE150 platform at Novogene Co., Ltd., Beijing, China. ~ 20 Gbp Illumina short reads were obtained for each sample. In addition, the V4 region of 16S rRNA gene was amplified by the primer pair 515F/806R (515F: 5′-GTGCCAGCMGCCGCGGTAA-3′; 806R: 5′-GGACTACHVGGGTWTCTAAT-3′). The 16S rRNA amplification products were sequencing by an Illumina PE250 platform at Novogene Co., Ltd., Beijing, China [11], with 150 bp paired-end sequencing. Consequently, a total of 424,609,236 reads were included in the 6 metagenome data sets, with an average of 70,768,206 reads per sample and a standard deviation of 3,878,705. A total of 1,146,699 reads were collected in the 12 16S rRNA amplicon data sets, with an average of 95,558 reads per sample and a standard deviation of 12,376. The sequencing data in this study have been deposited in the NCBI SRA database under the accession numbers PRJNA1113784 and PRJNA836095, respectively.

Metagenomic assembly and binning

Raw reads were assessed and trimmed by Trimmomatic (v0.39) [33, 34] with default parameters (ILLUMINACLIP:TruSeq3-PE.fa:2:30:10:2:True LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36) to remove adaptors and low-quality reads. Quality-controlled reads from each time point (n = 3) were co-assembled to contigs using MEGAHIT (v1.2.9) [35, 36] (parameters: –k-list 21,29,39,59,79,99,119,141), respectively.

The assembled contigs were binned using the Binning module (parameters: -maxbin2 -concoct -metabat2) and consolidated into a final bin set using the Bin_refinement module (parameters: -c 50 × 10) within the metaWRAP pipeline [37, 38]. Afterward, MAGs with > 50% completeness and < 10% contamination were retained for subsequent analysis. All binning results were aggregated and de-replicated for species-level clustering using dRep (v3.4.3) [36, 39] with an average nucleotide identity (ANI) cutoff value of 95% (parameters: -comp 50 -con 10 -sa 0.95). Completeness and contamination of each MAG were estimated using CheckM (v1.2.2) [38, 40].

Taxonomic assignments of MAGs

The taxonomic assessment of each MAG was assigned based on the Genome Taxonomy Database GTDB (release 06-RS202) [41] via the classify workflow of GTDB-Tk (v1.5.1) [42], using the "classify_wf" parameter.

Metagenomic profiling of lignin degrading pathways

For co-assembled shotgun metagenome sequencing reads, the open reading frames (ORFs) were predicted using Prodigal (v2.6.3) [43] in meta mode. Next, the putative protein-coding sequences were annotated to identify lignin degrading genes by searching against the LCdb database using DIAMOND (v0.9.25) BLASTp with identity ≥ 70% and e-value ≤ 1 e−5 [30, 38, 44]. All identified coding sequences from each assembly were then pooled and de-replicated using CD-HIT (v4.8.1) (parameters: -c 0.95 -aS 0.8) [36, 45]. Bowtie2 (v2.5.1) [46, 47] was applied to build the index and perform read mapping against the genes. Sorted bam files generated by SAMtools (v1.17) [46, 48] were used to calculate the relative abundance of genes across samples using CoverM (v0.6.1) [49] (https://github.com/wwood/CoverM) in contig mode (parameters: –min-read-percent-identity 0.95 –min-read-aligned-percent 0.75 –trim-min 0.10 –trim-max 0.90).

For individual MAGs, the relative abundance of MAGs de-replicated at species level was calculated using CoverM (v0.6.1) [49] (https://github.com/wwood/CoverM) in genome mode (parameters: –min-read-percent-identity 0.95 –min-read-aligned-percent 0.75 –trim-min 0.10 –trim-max 0.90). Gene families and LDGCs in each MAG were also predicted using the same procedures in the above-mentioned gene-centric analysis, where LCdb and S cycling database (SCycDB) were both used [50]. Complete degradation pathways indicated all genes in the pathways could be detected, while nearly complete degradation pathways were defined as those where only one gene in a pathway was not annotated.

Quantitative PCR

The abundance of total bacteria at 6 and 18 months was measured by quantitative PCR (qPCR) of the 16S rRNA gene using the primer set 341F/519R (341F: 5′-CCTACGGGWGGCWGCA-3′ and 519R: 5′-TTACCGCGGCKGCTG-3′), based on the standard qPCR protocol [13, 51]. The total DNA was extracted from each 20 g enriched sample to use as qPCR template. Standard curves were generated from tenfold serially diluted bacterial 16S rRNA gene DNA fragment. All experiments were performed in six biological replicates.

Residual lignin substrate measurement

Approximately 20 g each sample was mashed and heated until the gelling agent melted. The samples were cooled to 60 °C and the pH adjusted to 1–2 with 2 M HCl. Filter paper (30–50 µM pore) was used to filter samples, the resulting residues were dried and used for the following assay, as previous described [52]. Briefly, the dried sample was suspended in 72% H2SO4 for 1 h at 30 °C. Subsequently, milliQ pure water was added to dilute to 4%, and then incubated in an autoclave for 1 h at 121 °C. The residues were filtered by filtering crucibles (25 mL, porcelain, medium porosity) and oxidized in a muffle furnace at 575 °C for 8 h. The acid-insoluble lignin (AIL) content was measured by the weight loss method [52], whereas acid-soluble lignin content (ASL) was determined by UV–Vis spectroscopy using absorbance measurements at 320 nm. Unenriched blank samples were used as reference controls. The experiments were performed in six biological replicates.

Statistical analyses

All statistical analyses were performed in R (v4.1.3). Alpha and beta diversity of taxonomic and functional genes were calculated using the "vegan" package (v2.5.7) [53]. Calculated alpha diversity indices included Shannon, richness and evenness, and comparisons between groups was tested using Wilcoxon rank-sum test. For beta diversity, principal co-ordinates analysis (PCoA) was performed based on Bray–Curtis dissimilarity [54], and three different complementary nonparametric analyses were used, including nonparametric multivariate analysis of variance (ADONIS) [55], analysis of similarity (ANOSIM) [56], and multi-response permutation procedure (MRPP) [57]. The relative abundance of genes or MAGs at different exposure times were base 2 logarithm-transformed and visualized as heatmaps using "pheatamp" package (v1.0.12) [58] (https://CRAN.R-project.org/package=pheatmap). Sankey diagrams of MAGs at different phylogenetic levels were illustrated using "networkD3" package (v0.4) [59] (https://CRAN.R-project.org/package=networkD3). Spearman’s rank correlations were performed to investigate the influences of environmental factors (temperature, salinity, pH, and dissolved oxygen (DO)) on community variations at the taxonomic and functional gene levels. The environmental parameters were collected during the enrichment period, as our previously described [11]. Statistical differences between different groups were compared using two-tailed Student's t-tests. A significance level of p < 0.05 was considered statistically significant.

Results

Consortia with different lignin degrading capacities exhibited varied taxonomic compositions, yet similar functional traits

As our previously reported [11], an in situ lignin enrichment was performed in the coastal intertidal wetlands of the East China Sea (Fig. 1A). Four lignin/lignocellulose substrates were used in the enrichment experiment over an 18-month period, including Kraft lignin, aspen wood powder (hardwood), pine wood power (softwood) and rice straw powder (herbaceous). Aspen, pine and rice represent natural lignin, whiles Kraft lignin is a byproduct of the paper and pulp industry, which is substantially generated and discharged, with ~ 100 million tons per year [60,61,62]. The four types of lignin contain G-, S- and H-type lignin units, although the contents vary with herbaceous, hardwood and softwood substrates [11, 63]. Our previous study revealed substrate-dependent ligninolytic functional traits between woody and herbaceous substrates for consortia enriched at 6 months, where the compositions of lignin degrading gene families were similar among the three woody substrates [11]. To further investigate the metabolic strategy of woody lignin, consortia, enriched on Kraft lignin, were targeted in this study. Two groups of consortia, designated L6 and L18, were successively obtained through 6- and 18-month in situ Kraft lignin enrichments. The average lignin degradation of the L6 consortia was 7.06%, while in L18 it was 3.6-fold higher, reaching 25.19% (Fig. 1B), which exhibited the most differences in the groups of consortia enriched by aspen, pine and Kraft lignin, respectively (Fig. S1) [11]. Correspondingly, the L18 consortia showed a dramatic enhancement in bacterial abundance, with a 39-fold increase over L6, indicating that the prokaryotic consortia, might play a role in lignin degradation (Fig. 1C). Thus, we firstly explored the bacterial taxonomic diversity through 16S rRNA gene amplicon sequencing. L18, as expected, exhibited significantly higher Shannon index values than L6 (Wilcoxon rank-sum test, p < 0.05), with higher richness and evenness (Fig. 1D and Fig. S2A). Beta diversity, via principal coordinate analysis (PCoA), also showed a temporal separation. Consistent with the two enrichment periods, the consortia grouped into two clusters, demonstrating their varied taxonomic compositions (Fig. 1E). In the L6 group, the most abundant class was Epsilonproteobacteria (17.98 ± 5.55%), followed by Gammaproteobacteria (17.40 ± 2.69%), Bacteroidia (15.71 ± 4.00%), Spirochaetia (12.18 ± 3.71%), Desulfobacteria (5.87 ± 1.11%), Alphaproteobacteria (3.24 ± 1.03%) and Anaerolineae (1.91 ± 0.97%, Fig. S2B). In contrast, the L18 group was highly enriched in Desulfobacteria (15.85 ± 1.17%), Alphaproteobacteria (6.56 ± 2.07%) and Desulfarculia (1.89 ± 0.52%, p < 0.05, Fig. S2B). In all, the L18 group, with its higher lignin degradation capacity, exhibited higher diversity and a different taxonomic composition.

Next, metagenomic sequencing was used to examine the community diversity and composition at the functional gene level. A total of 414 gene families were identified to be involved in lignin degradation (Table S2). In contrast to the taxonomic differences, L6 and L18 exhibited similar Shannon index values for the ligninolytic functional gene families (Wilcoxon rank test, p > 0.05), although higher richness was observed in the L18 group (Wilcoxon rank test, p < 0.05, Fig. 2A and Fig. S3). Moreover, the compositions of lignin degrading gene families were similar between the two groups (p > 0.05, Fig. 2B and Table S3). For lignin depolymerization, the dypB, dypA, lac and mnsod gene families were all detected in both groups of consortia, whereas dypC was specifically identified in the L18 group. In both L6 and L18, the relative abundance of dypB was the highest, followed by lac and dypA (Fig. 2C). This suggested that the consortia might primarily employ dypB for aerobic lignin depolymerization, supplemented with lac and dypA. In addition, gene families encoding auxiliary enzymes, e.g., gox, cat, dld, sod, gaoA and adhA, were identified in both L6 and L18 (Fig. 2C). Glycolate oxidase, encoded by gox, produces H2O2 to assist dypB in lignin depolymerization [64, 65]. Quinone reductase, encoded by nuoEFG, prevents lignin repolymerization [66]. Meanwhile, trxB and gpx encode thioredoxin reductase and glutathione peroxidase, respectively. These enzymes reduce the H2O2 level to protect cells from oxidative damage [67,68,69]. The auxiliary enzyme genes mostly showed similar abundances in both L6 and L18 (p > 0.05), with some exceptions, e.g., trxB, nuoF and gpx which showed higher abundances in the L6 group. Altogether, the composition and abundance of the gene families involved in aerobic lignin depolymerization were similar, the exceptions being minor dypC and several auxiliary enzyme genes.

Fig. 2
figure 2

Lignin degrading functional genes in L6 and L18 consortia. A Shannon alpha diversity in the L6 and L18 consortia at the functional gene level. Data are mean ± standard deviation, n = 3 biological replicates. B The compositions of lignin degrading functional traits in L6 and L18 consortia. C Relative abundance of gene families involved in lignin degradation, including lignin depolymerization and lignin-derived aromatic compound metabolism. Significant differences between L6 and L18 consortia are marked with asterisks (p < 0.05). H: H-type lignin derivates, S: S-type lignin derivates, G: G-type lignin derivates, others: other lignin-derived aromatic compounds. Relative abundance: the number of gene reads/the number of total reads participating in lignin degradation. Log2A represents base 2 logarithm-transformed relative abundance. Data (B, C) are mean values of three biological replicates

Full size image

The 384 gene families involved in lignin-derived aromatic compound degradation were observed in both L6 and L18, including H-, G- and S-type, and other aromatic compound degradation pathways (Fig. 2C and Table S2). For instance, des and lig gene families in the syringate (S-type) pathway, bph gene families in the biphenyl (G-type) pathway, hpa and phc gene families in the 4-hydroxyphenylacetic acid and p-coumarate (H-type) pathways were all detected in both groups (Fig. 2C). Moreover, their relative abundances were very similar (p > 0.05), except for the phcS, todT, vanB, pcaB, mtgB, bbsB and ebdA gene families (Fig. 2C and Table S2). Overall, gene-centric analysis suggested that the compositions and relative abundances of lignin degrading gene families were nearly the same between the two groups. As such the metabolic differences of L6 and L18 cannot simply be attributed to the different abundances of individual gene families. It requires further investigation of the various taxonomic members with different genomic potentials.

L18 consortia, with higher lignin degrading capacity, recruited more anaerobic metagenome-assembled genomes (MAGs)

To explore the genomic potentials of the different taxonomic groups, metagenomic assembly and binning generated 393 MAGs from the L6 and L18 consortia. Among them, 150 MAGs were high-quality (completeness > 90%, contamination < 5%), and 243 MAGs were medium-quality (completeness > 50%, contamination < 10%, Fig. S4 and Table S4). They clustered into 381 bacterial and 12 archaeal species-level clades. The bacterial clades were mostly distributed among the phyla Bacteroidetes (10.73%), Desulfobacterota (9.68%), Pseudomonadota (7.71%), Spirochaetota (3.93%), Campylobacterota (3.60%), Planctomycetota (1.83%), Bacillota (1.67%) and Verrucomicrobiota (1.35%) (Fig. 3 and Table S5). In addition, the phyla Thermoproteota (1.58%) and Nanoarchaeota (0.24%) were highly represented among the archaeal species-level clades (Fig. 3).

Fig. 3
figure 3

The reconstructed MAGs from L6 (A) and L18 (B) consortia. Sankey diagrams showed recovered archaeal and bacterial MAG information at different taxonomic levels (relative abundance > 1%), based on the GTDB classification. Numbers indicate the relative abundance of MAGs recovered for that lineage. Relative abundance was determined by mapping each MAG against quality-filtered metagenome reads using CoverM. Significant differences between L6 and L18 consortia are marked with asterisks (p < 0.05). Data are from three biological replicates

Full size image

The composition of MAGs from the L6 group was enriched in the bacterial classes Bacteroidia (13.15%), Gammaproteobacteria (8.22%) and Spirochaetia (5.22%). The archaeal classes Bathyarchaeia (1.04%) and Nanoarchaeia (0.12%) were also detected among the L6 consortia (Fig. 3A and Table S5). These prokaryotic classes were also present in L18 consortia (Fig. 3B and Table S5). Furthermore, L18 consortia were significantly enriched in the classes Bathyarchaeia (2.11%) and Desulfobacteria (6.36%) (Fig. 3B and Fig. S5), which are widely distributed in anaerobic ecosystems [14, 70, 71]. This suggested that L18 consortia might employ an anaerobic strategy to enhance lignin degradation.

L18 consortia harbored MAGs with diverse and unique LDGCs

To further examine the genomic potential for lignin degradation, we screened these MAGs for the presence of LDGCs (Fig. 4, Fig. S6 and Table S6). The LDGCs were concentrated in 69 MAGs from phylogenetically diverse bacterial classes, including the top six bacterial lineages of Bacteroidia (n = 10), Desulfobacteria (n = 10), Alphaproteobacteria (n = 12), Verrucomicrobiae (n = 6), Desulfarculia (n = 6) and Spirochaetia (n = 4, Fig. 4).

Fig. 4
figure 4

Distributed patterns of lignin biodegradation gene clusters (LDGCs) in the partial MAGs. The left heatmap represents the relative abundance of MAGs with LDGCs at the class level. Log2A represents base 2 logarithm-transformed MAGs as indicated by normalized relative abundance. The right heatmap represents the presence/absence of LDGCs and gene clusters of the dissimilatory sulfate reduction pathway in the MAGs. The displayed MAGs are either among the top six for abundance or carry unique LDGCs. Colors indicate the presence of LDGCs in the corresponding MAGs at the class level, while white grid suggests absence. The LDGCs labeled in red are specific to the L18 consortia. The bottom heatmap represents the relative abundance of MAGs at the species level. The MAGs labeled in red indicate generalists. Significant abundance differences between L6 and L18 consortia are marked with asterisks (p < 0.05)

Full size image

Among the L6 consortia, MAGs with lignin depolymerase genes were mostly from the classes Bacteroidia, Verrucomicrobiae, Spirochaetia and Alphaproteobacteria. The highly abundant dypB gene family was primarily encoded by Bacteroidia, while the lac gene family was mostly encoded by Verrucomicrobiae and Spirochaetia (Fig. 4 and Fig. S6). In addition, MAGs from the Alphaproteobacteria, Polyangia, Desulfobacteria and Desulfarculia classes harbored 9 complete or nearly complete lignin-derived aromatic compound pathways, which were further clustered into 12 LDGCs and 48 gene families (Table S6). The 8 LDGCs involved in aerobic degradation of aromatic compounds were mostly retrieved from Alphaproteobacteria, including lig-2 in the aryl ether pathway, vdh and van in the vanillin pathway, ben in the benzoate pathway, box in the benzoyl-CoA pathway, mhp in the 3-hydroxycinnamic acid pathway, as well as pca and lig-3 in the PCA 3,4- and 4,5-cleavage pathways, respectively. An additional 4 LDGCs involved in the anaerobic degradation of aromatic compounds were largely retrieved from Desulfobacteria and Desulfarculia, and included bcl, bzd, hba and hcr in the 4-hydroxybenzoate pathway (Fig. 4 and Table S6). Notably, 11 MAGs in the L6 consortia harbored multiple metabolic pathways, and were designated as metabolic generalists. These could be divided into three groups. One group was involved in both lignin depolymerization and aromatic compound degradation. For example, Alphaproteobacteria E_bin.85 encoded dypB and the pathways for benzoate and 3-hydroxycinnamic acid degradation (Fig. 4). A second group harbored multiple aromatic compound degradation pathways. It was composed of six MAGs, four of which were from the bacterial class Alphaproteobacteria. Alphaproteobacteria E_bin.95 not only harbored aerobic pathways for vanillin and benzoyl-CoA degradation, but also encoded oxidoreductases, e.g., dihydrolipoamide dehydrogenase (dld), thioredoxin reductase (trxB), glycolate oxidase (gox), quinone reductase (nuo) and glutathione peroxidase (gpx), to assist the oxidative degradation of these aromatic compounds (Fig. 4 and Fig. S6). Alphaproteobacteria E_bin.143 also exhibited similar metabolic flexibility. Additionally, Alphaproteobacteria E_bin.26 contained pathways for vanillin and benzoyl-CoA degradation, as well as PCA 3,4-cleavage, while Alphaproteobacteria E_bin.176 bore pathways for vanillin degradation and PCA 4,5-cleavage (Fig. 4 and Fig. S6). Meanwhile, SAR324 E_bin.419 harbored pathways for aryl ether degradation and PCA 3,4-cleavage. Polyangia E_bin.356 encoded pathways for PCA 3,4- and catechol 1,2-cleavage (Fig. 4 and Fig. S6). The third group carried anaerobic aromatic compound degradation and dissimilatory sulfate reduction pathways. It contained four MAGs, Desulfobacteria E_bin.128, Desulfobacteria E_bin.55, Desulfarculia E_bin.156 and JACQYL01 E_bin.154 (Fig. 4 and Fig. S5-S6). This demonstrated the importance of sulfate reduction in anaerobic lignin degradation, even though the enrichment of sulfate reducing bacteria might be caused by the presence of 2% sulfur in Kraft lignin.

Among the L18 consortia, the MAGs with LDGCs were from many of the same bacterial classes as in the L6 consortia (Fig. 4 and Fig. S6). However, some unique and diverse LDGCs were observed in these MAGs that were absence in the L6 consortia. Alphaproteobacteria E_bin.147 with des and lig in the aerobic syringate degradation pathway, Desulfobacteria E_bin.53 with pad in the anaerobic phenylacetate degradation pathway and Desulfarculia E_bin.71 with bcr, bzd and bam in the anaerobic benzoyl-CoA degradation pathway, were specifically detected in L18 group (Fig. 4). Moreover, more generalists (n = 15) carrying multiple metabolic pathways were recruited by the L18 consortia. First, the common generalists within L6 and L18 consortia, showed significantly higher abundance in the L18 group, e.g., Alphaproteobacteria E_bin.143 and Alphaproteobacteria E_bin.95. Second, three generalists unique to L18 consortia enhanced metabolic flexibility in aerobic lignin degradation. Bacteroidia E_bin.12 not only encoded lignin depolymerase dypB, but also harbored the PCA 3,4-cleavage pathway. Alphaproteobacteria E_bin.147 encoded pathways for syringate and aryl ether degradation and the PCA 4,5-cleavage pathways, while Anaerolineae E_bin.30 encoded the aerobic pathways for vanillin degradation and PCA 3,4-cleavege, as well as the anaerobic pathway for 4-hydroxybenzoate degradation (Fig. 4). Third, Desulfarculia E_bin.71, also unique to the L18 consortia, exhibited the genomic potential for anaerobic lignin degradation. It not only harbored LDGCs, bcr, bzd and bam, for anaerobic benzoyl-CoA catabolism, but also encoded dsr, apr and sat for dissimilatory sulfate reduction (Fig. 4). Furthermore, additional MAGs from Desulfobacteria, Desulfovibrionia, Desulfobulbia and Desulfarculia in the L18 consortia harbored the dissimilatory sulfate reduction pathway (Fig. S5). Sulfate-reducing bacteria could utilize sulfate as the final electron acceptor for mineralizing aromatics under anaerobic conditions and further stimulate anaerobic lignin degradation [72, 73]. Their high abundance likely contributed to lignin degradation under anaerobic conditions (p < 0.05, Fig. 4 and Fig. S5).

As stated earlier, L18 consortia, with their higher lignin degradation capacity, recruited more abundant MAGs with diverse and unique LDGCs (Figs. 4, 5 and Fig. S6, S7). Therefore, we inferred that L18 consortia might employ three strategies to enhance lignin degradation (Fig. 5). First, L18 consortia enhanced the abundances of LDGCs that were also present in L6 consortia. On one hand, they increased the present MAGs abundances. Alphaproteobacteria E_bin.95 and Alphaproteobacteria E_bin.143 showed significantly higher abundance in L18 than that in L6. The MAGs harbored several degradation pathways for lignin-derived compounds, e.g., aryl ether, vanillin and benzoyl-CoA. On the other hand, L18 consortia enriched more MAGs to enhance the abundances of LDGCs. An additional seven unique MAGs, carrying DypB, were detected in L18 consortia, including E_bin.50, E_bin.138, E_bin.216 and E_bin.12 in the Bacteroidia class, Gammaproteobacteria E_bin.106, Lentisphaeria E_bin.213, and Phycisphaerae E_bin.142. Similarly, Bacteroidia E_bin.12 and Anaerolineae E_bin.30 with pca in PCA 3,4-cleavage pathway, were specifically detected in L18 consortia. These highly abundant MAGs, undoubtedly increased the genomic potential for lignin degradation in L18 consortia.

Fig. 5
figure 5

The reconstructed lignin degradation pathways in L6 (A) and L18 (B) consortia. The presented MAGs were significantly enriched in either the L6 or L18 consortia. 14 MAGs from L6 and 19 MAGs from L18 are displayed. The solid arrows represent existent gene families, and dashed arrows represent absent gene families. Reactions labeled in red indicate unique pathways in the L18 consortia. Pathways from the same MAG are marked by the same background color

Full size image

Second, L18 consortia recruited the MAGs with extra pathways to expand substrate utilization range (Fig. 5). Alphaproteobacteria E_bin.147 encoded des and lig for syringate degradation. In addition, Desulfobacteria E_bin.53 harbored pad for anaerobic degradation of phenylacetate, whiles Desulfarculia E_bin.71 contained bcr, bzd and bam for anaerobic degradation benzoyl-CoA. Neither the MAGs nor LDGCs were observed in L6 consortia. L18 consortia utilized these LDGCs to improve their metabolic potentials in lignin degradation.

Third, L18 consortia harbored more generalists with flexible metabolic pathways to enhance adapted advantage in the fluctuating coastal intertidal zones. These generalists either were absent or showed significantly lower abundance in L6 consortia (Fig. 4 and Fig. S6). (i) L18 recruited Bacteroidia E_bin.12 as a lignin degrading generalist, including lignin depolymerization and PCA 3,4-cleavage pathway (Figs. 4, 5); (ii) L18 enriched Alphaproteobacteria E_bin.147 for multiple aromatic compound degradation, including syringate and aryl ether degradation pathways, as well as PCA 4,5-cleavage pathway (Figs. 4, 5); (iii) MAGs with anaerobic pathways were commonly accompanied by the dissimilatory sulfate reduction pathway in L18 consortia, e.g., Desulfarculia E_bin.71. Anaerobic sulfate reduction is essential for anaerobic lignin degradation, indicating the strong metabolic potentials under anaerobic conditions; and (iv) L18 was enriched in Anaerolineae E_bin.30, which harbored the aerobic vanillin degradation and PCA 3,4-cleavage pathways, as well as the anaerobic 4-hydroxybenzoate degradation pathway. The ability to switch between aerobic and anaerobic metabolism could contribute to its environmental adaptability, as coastal intertidal zones experience strong oxygen fluctuations in micro-environments. Together, the MAGs in the L18 group exhibited more abundant, diverse and flexible metabolic pathways. These genomic characteristics would contribute to lignin degradation in coastal wetlands.

Discussion

In this study, we developed two groups of lignin degrading consortia via 6- and 18-month in situ enrichment in coastal intertidal zones, respectively. They exhibited great differences with respect to lignin degradation (7.06% vs 25.19%). Genomic potential analysis of the complex degradation process of lignin via metagenomics is usually restricted due to the limited number of genes that can be annotated via the widely used CAZy and KEGG databases. For instance, 63 gene families from the cultured communities in the nearshore sediments of the East and South China Seas, 75 gene families from the Amazon River microbiomes and 13 gene families from a North American forest consortium were revealed to participate in lignin degradation, respectively [3, 4, 9]. Here, we employed our recently developed lignin degradation functional gene database (LCdb) [30] and revealed that the 414 gene families in the L6 and L18 consortia were involved in lignin degradation via the metagenomic gene-centric analysis, with the similar the abundances and compositions. In contrast, they exhibit significant taxonomic variations, in which the taxon varied with environmental factors (Fig. 2, Fig. S2, Fig. S8 and Table S3). Our previous study also reported that environmental factors, pH, temperature, salinity and DO, all significantly influenced the variations of community composition in the in situ enrichment experiment (r = 0.28–0.47, p = 0.001) [11]. It coincided with the recent studies that environmental selects functional traits, instead of species [20]. Importantly, it revealed that individual functional traits just indicated the metabolic routes, but were not strongly associated with the lignin metabolism capacity. In contrast, LDGC-based genome-centric analysis uncovered the genomic potential differences between L6 consortia and L18 consortia. L6 consortia harbored 12 LDGCs that participated in 9 complete or nearly complete lignin-derived aromatic compound pathways, whiles L18 consortia contained 16 LDGCs, involved in 12 aromatic compound pathways (Fig. 4, Fig. S6 and Table S6). Specifically, the des in the aerobic syringate degradation pathway, bcr and bam in the benzoyl-CoA degradation pathway, and pad in the anaerobic phenylacetate degradation pathway were specifically detected in L18 consortia (Figs. 4, 5). Moreover, the abundance of these MAGs was significantly higher in L18 than in L6, especially for the MAGs from the classes Bathyarchaeia and Desulfobacteria (Fig. 3). Archaeal Bathyarchaeia are reported to utilize lignin as the sole carbon source under anaerobic conditions [13]. Anaerobic lignin degradation is commonly accompanied by nitrate, iron and sulfate reduction, which allow for electron transfer during the degradation process [36, 74, 75]. Coinciding with this, the relative abundance of Desulfobacteria, as sulfate reducers [70, 76], was increased in L18, although sulfur in Kraft lignin also could contribute to their enrichment (Fig. S5). Moreover, a higher abundance of the mtgB gene family was observed in L18, which encodes methyltransferase and was reported to be a critical step in anaerobic lignin degradation [14]. This indicated that L18 employed more anaerobic members to enhance the metabolic capacity. The LDGCs differences were also associated with the fluctuating coastal intertidal zones. Compared to the LDGCs in aerobic G- and H-type lignin unit degradation pathways, lig in the aerobic S-type pathway showed significantly associated with the environmental factors (Fig. S8). Moreover, more LDGCs in anaerobic degradation pathways exhibited significant correlation with the environmental factors, in contrast to the aerobic LDGCs (Fig. S8). Together, L18 consortia recruited more abundant MAGs that encoded diverse and unique LDGCs.

Aggregation is a general strategy that microbial communities utilize to efficiently perform complex tasks [77]. Multiple members could perform the same function, and thus enhance efficiencies when forming a union. In this study, the aggregation strategy was used by both L6 and L18 consortia, where each LDGC was encoded by several MAGs for lignin degradation, including lignin depolymerization, and aerobic/anaerobic aromatic compound degradation (Fig. 4 and Fig. S6). Moreover, L18 consortia exhibited a cooperative advantage over L6 consortia. They recruited more MAGs to enhance the LDGCs abundances and further efficiently accomplish each task. For instance, the L6 group recruited 14 MAGs with 1.81% abundance that encoded dypB, the dominate lignin oxidizing gene in coastal regions (Fig. 2C) [11, 30, 78], whereas the L18 group employed 17 MAGs that harbor dypB, with a 4.08% abundance. Similarly, 4 MAGs, at 0.24% abundance, in L6 harbored the pca in aerobic PCA 3,4-cleavage, while 6 MAGs with 0.42% abundance in L18 contained the complete pathway. In particular, Desulfobacteria and Desulfarculia MAGs were significantly increased in L18 (Fig. S5-S6), which not only participated in anaerobic degradation of 4-hydroxybenzoic acid, benzoyl-CoA and phenylacetate, but also were responsible for electron transfer during anaerobic lignin degradation.

Besides aggregation, division of labor (DOL) is another cooperative strategy evolved by microbial communities to accomplish complex tasks, reduce metabolic burden, accelerate the metabolite catabolism, and increase toxic compound resistance [79,80,81]. One of the potential mechanisms for DOL is complementing differences in gene content [82]. Here, complementary functions were observed in the L6 and L18 consortia. MAGs from the classes Bacteroidia, Verrucomicrobiae and Spirochaetia are mainly responsible for lignin depolymerization. The dypBs are mostly encoded by Bacteroidia, whereas laccases are generally encoded by Verrucomicrobiae and Spirochaetia (Fig. 4). In contrast, Alphaproteobacteria encode numerous LDGCs that participate in aerobic degradation of lignin-derived aromatic compounds, while Desulfobacteria and Desulfarculia encode LDGCs involved in anaerobic lignin degradation.

Although Bacteroides species from the class Bacteroidia are well-known members of the gut microbiota, members of the phylum Bacteroidetes are prevalent in a variety of marine environments, e.g., coastal, offshore, sediments and hydrothermal vents [83]. Marine Bacteroidetes members commonly attach to particles and increase in abundance during phytoplankton blooms, as they encode gene families of carbohydrate-active enzymes (CAZymes) to degrade polysaccharides, e.g., cellulose [83]. Recent studies also revealed they play a role in degradation of lignin-derived aromatic compounds in the Pearl River [15]. Here, we revealed that members from the Bacteroidia encode dypB, laccase and mnsod for lignin depolymerization (Fig. 4). Marine Verrucomicrobiota are thought to excel at the degradation of complex polysaccharides, including digesting fucoidan from brown macroalgae [84], as well as sulfated and fructose-containing polysaccharides during diatom blooms [85]. The class Spirochaetia in the phylum Spirochaetota was reported to be more abundant in environments with high concentrations of hydrocarbons, as they harbor polycyclic aromatic hydrocarbon ring-hydroxylating dioxygenase (PAH-RHDGNɑ) [86]. Here, we expanded their roles to the degradation of aromatic carbon polymers, revealing they also encode dypB and/or laccase to depolymerize lignin. In addition, the class Alphaproteobacteria from phylum Pseudomonadota is widely distributed in terrestrial and aquatic ecosystems and harbors various types of metabolic processes [87, 88]. Previous research has suggested that Alphaproteobacteria associates with wood rot fungi to decompose wood [89]. In the Pearl River freshwater environment, Alphaproteobacterial MAGs harbor genes for the degradation of ferulic acid, syringyl fragment and diarylpropane biphenyl fragment [15, 90]. Here, we further demonstrated that Alphaproteobacteria in coastal intertidal zones contain multiple LDGCs involved in the aerobic degradation of a wide range of lignin-derived aromatic compounds, including syringate, aryl ether, vanillin, benzoate, benzoyl-CoA, protocatechuic acid and 3-hydroxycinnamin acid (Fig. 4 and Fig. S6). In contrast to aerobic lignin degradation, MAGs affiliated with Desulfobacteria and Desulfarculia exhibited the genomic potential for anaerobic lignin degradation. They harbored dissimilatory sulfate reduction pathways to allow elemental sulfur as an electron acceptor during anaerobic lignin degradation (Fig. 4 and Fig. S5, S6). Besides their well-known role in sulfur reduction [70, 71, 76], they also carried LDGCs for the anaerobic catabolism of lignin-derived aromatic compounds (4-hydroxybenzoate, phenylacetate and benzoyl-CoA), demonstrating their central multi-role in lignin degradation under anaerobic/anoxic environments. Interestingly, the archaeal class Bathyarchaeia, affiliated with Desulfobacteria and Desulfarculia, was also enriched in the L6 and L18 consortia. Bathyarchaeia has been isolated from estuarine sediments and are reported to mediate anaerobic lignin degradation [13]. A recent study further suggested that it is widely distributed in multiple anoxic marine ecosystems, particularly anoxic coastal sediments and encodes methyltransferases (mtgBs) for O-demethylation of lignin monomers [14]. However, the Bathyarchaeia MAGs recovered in the current study did not contain mtgB, although the gene family was detected in both the L6 and L18 consortia by gene-centric analysis (Fig. 2C). This indicated that unrecognized genes or LDGCs with anaerobic lignolytic activity might be present in the Bathyarchaeia MAGs. Together, the complementary gene contents suggested that a task division strategy should exist in the enriched communities, in which each taxon performs a specific job.

Furthermore, metabolic flexibility contributes to the execution of these cooperative strategies. We observed that L18 exhibited an increased abundance in generalist taxa, e.g., Alphaproteobacteria E_bin.143 and Alphaproteobacteria E_bin.95 (Fig. 4). Moreover, additional generalists were specifically recruited by L18, e.g., Bacteroidia E_ bin.12, Anaerolineae E_ bin.30 and Alphaproteobacteria E_bin.147 (Fig. 4). The enriched generalists in L18 suggested that metabolic flexibility should a key factor to perform lignin degradation in coastal intertidal zones. Lignin is a highly heterogeneous aromatic polymer that varies from plant to plant, and a variety of aromatic compounds are released during lignin depolymerization [22, 91]. Generalists with metabolic flexibility could well utilize the available heterogenous compounds. A similar situation was observed in a bioreactor system where the glucose and xylose co-utilizing generalist outcompeted specialists [92]. Furthermore, the coastal intertidal zone is a highly disturbed ecosystem [93]. The specialization-disturbance hypothesis suggests that disturbances positively stimulate generalists while being detrimental to specialists [94, 95]. Consequently, Anaerolineae E_ bin.30, as a typical generalist that shows genomic potential for both aerobic and anaerobic metabolism, was expectedly increased in L18 in response to the strong fluctuations of dissolved oxygen (Figs. 4 and 5).

Conclusions

In conclusion, this genome-centric metagenome analysis revealed the metabolic potential of lignin degradation in coastal intertidal zones. Microbial LDGCs were discovered across 13 bacterial phyla, covering depolymerization and catabolism under aerobic/anaerobic conditions. Investigating the genomic potential of the L18 consortia with higher lignin degradation capacity broadens our understanding of the metabolic strategies used by natural microbial consortia in the highly disturbed ecosystem, with versatile abiotic gradients. Moreover, it provides inspiration for the “bottom-up” design of synthetic microbial communities in lignin valorization. On one hand, the knowledge about MAGs and LDGCs guides us to employ members from the Bacteroidia, Verrucomicrobiae and Spirochaetia classes with dypB and laccase for lignin depolymerization. Species from the class Alphaproteobacteria could be the well candidates for aerobic degradation of lignin-derived aromatic compounds, whiles Desulfobacteria and Desulfarculia species might be considered for anaerobic lignin-derived aromatic compound degradation. On the other hand, the cooperative strategies in natural consortia, e.g., aggregation, DOL and metabolic flexibility, lay foundations to design inter-species interactions within synthetic communities. Considering the complex and amorphous structures of lignin, understanding the various metabolic strategies for different lignin types would provide more accurate and comprehensive clues for lignin bioconversion in the near future.

Availability of data and materials

The 16S rRNA gene amplicon sequencing raw data were deposited in the NCBI SRA database under the accession number PRJNA836095 and in the NODE (https://www.biosino.org/node/) under project ID OEP005460 (Experimental ID OEX00028628). The metagenome sequencing data were deposited in the NCBI SRA database under the accession number PRJNA1113784, as well as in the NODE (https://www.biosino.org/node/) under project ID OEP005460 (Experimental ID OEX00028629).

Abbreviations

CAZymes:

Carbohydrate-active enzymes

DesA:

Syringate O-demethylase

dld :

Dihydrolipoamide dehydrogenase

DOL:

Division of labor

G-:

Guaiacyl-

gox :

Glycolate oxidase

gpx :

Glutathione peroxidase

H-:

p-Hydroxyphenyl-

LCdb:

Lignin degradation functional gene database

LDGCs:

Lignin degradation gene clusters

MAGs:

Metagenome-assembled genomes

mtgBs :

Methyltransferases

nuo :

Quinone reductase

PAH-RHDGNɑ:

Polycyclic aromatic hydrocarbon ring-hydroxylating dioxygenase

PCA:

Protocatechuic acid

PCoA:

Principal coordinate analysis

PobA:

p-Hydroxybenzoate hydroxylase

ROS:

Reactive oxygen species

S-:

Syringyl-

TerrOC:

Terrestrial organic carbon

trxB :

: Thioredoxin reductase

Vdh:

: Vanillin dehydrogenase

DO:

Dissolved oxygen

References

  1. McDonald N, Achterberg EP, Carlson CA, Gledhill M, Liu ST, Matheson-Barker JR, Nelson NB, Parsons RJ. The role of heterotrophic bacteria and archaea in the transformation of lignin in the open ocean. Front Mar Sci. 2019;6:743.

    Article  Google Scholar 

  2. Feng SZ, Su YR, He XY, Hu YJ, Zhang ZH, He HB, Kariman K, Wu JS, Chen XB. Effects of long-term straw incorporation on lignin accumulation and its association with bacterial laccase-like genes in arable soils. Appl Microbiol Biot. 2019;103(7):3237.

    Article  CAS  Google Scholar 

  3. Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2019;13(2):413–29.

    Article  CAS  PubMed  Google Scholar 

  4. Santos CD, Sarmento H, de Miranda FP, Henrique-Silva F, Logares R. Uncovering the genomic potential of the Amazon river microbiome to degrade rainforest organic matter. Microbiome. 2020;8(1):151.

    Article  Google Scholar 

  5. Leadbeater DR, Oates NC, Bennett JP, Li Y, Dowle AA, Taylor JD, Alponti JS, Setchfield AT, Alessi AM, Helgason T, et al. Mechanistic strategies of microbial communities regulating lignocellulose deconstruction in a UK salt marsh. Microbiome. 2021;9(1):48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Talling PJ, Hage S, Baker ML, Bianchi TS, Hilton RG, Maier KL. The global turbidity current pump and its implications for organic carbon cycling. Annu Rev Mar Sci. 2024;16(1):105–33.

    Article  Google Scholar 

  7. Li JF, Wang BL, Yang ML, Li WZ, Liu N, Qi YL, Liu CQ. Geographical constraints on chemodiversity of sediment dissolved organic matter in China’s coastal wetlands. Appl Geochem. 2022;147: 105506.

    Article  CAS  Google Scholar 

  8. Ma K, Li YY, Song W, Zhou JY, Liu X, Wang MQ, Gong XF, Wang LL, Tu QC. Disentangling drivers of mudflat intertidal DOM chemodiversity using ecological models. Nat Commun. 2024;15(1):6620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ley Y, Cheng XY, Ying ZY, Zhou NY, Xu Y. Characterization of two marine lignin-degrading consortia and the potential microbial lignin degradation network in nearshore regions. Microbiol Spectr. 2023;11(3):e04424-e4522.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ma WW, Lin L, Peng QN. Origin, selection, and succession of coastal intertidal zone-derived bacterial communities associated with the degradation of various lignocellulose substrates. Microb Ecol. 2023;86(3):1589–603.

    Article  CAS  PubMed  Google Scholar 

  11. Peng Q, Lin L, Tu Q, Wang X, Zhou Y, Chen J, Jiao N, Zhou J. Unraveling the roles of coastal bacterial consortia in degradation of various lignocellulosic substrates. mSystems. 2023;8(4):01283–322.

    Google Scholar 

  12. Ferrer A, Heath KD, Canam T, Flores HD, Dalling JW. Contribution of fungal and invertebrate communities to wood decay in tropical terrestrial and aquatic habitats. Ecology. 2020;101(9): e03097.

    Article  PubMed  Google Scholar 

  13. Yu TT, Wu WC, Liang WY, Lever MA, Hinrichs KU, Wang FP. Growth of sedimentary Bathyarchaeota on lignin as an energy source. Proc Natl Acad Sci USA. 2018;115(23):6022–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yu TT, Hu HN, Zeng XH, Wang YZ, Pan DA, Deng LH, Liang LW, Hou JL, Wang FP. Widespread Bathyarchaeia encode a novel methyltransferase utilizing lignin-derived aromatics. mLife. 2023;2(3):272–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li JL, Wang PD, Salam N, Li X, Ahmad M, Tian Y, Duan L, Huang LN, Xiao M, Mou XZ, et al. Unraveling bacteria-mediated degradation of lignin-derived aromatic compounds in a freshwater environment. Sci Total Environ. 2020;749: 141236.

    Article  CAS  PubMed  Google Scholar 

  16. Li JY, Dong CM, Lai QL, Wang GY, Shao ZZ. Frequent occurrence and metabolic versatility of Marinifilaceae bacteria as key players in organic matter mineralization in global deep seas. mSystems. 2022;7(6):e00864-e922.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Levy-Booth DJ, Hashimi A, Roccor R, Liu LY, Renneckar S, Eltis LD, Mohn WW. Genomics and metatranscriptomics of biogeochemical cycling and degradation of lignin-derived aromatic compounds in thermal swamp sediment. ISME J. 2021;15(3):879–93.

    Article  CAS  PubMed  Google Scholar 

  18. Louca S, Jacques SMS, Pires APF, Leal JS, Srivastava DS, Parfrey LW, Farjalla VF, Doebeli M. High taxonomic variability despite stable functional structure across microbial communities. Nat Ecol Evol. 2017;1(1):15.

    Article  Google Scholar 

  19. Nelson MB, Martiny AC, Martiny JBH. Global biogeography of microbial nitrogen-cycling traits in soil. P Natl Acad Sci USA. 2016;113(29):8033–40.

    Article  CAS  Google Scholar 

  20. Song W, Liu JH, Qin W, Huang J, Yu XL, Xu MZ, Stahl D, Jiao NZ, Zhou JZ, Tu QC. Functional traits resolve mechanisms governing the assembly and distribution of nitrogen-cycling microbial communities in the global ocean. MBio. 2022;13(2):e03832-e3921.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bugg TDH, Ahmad M, Hardiman EM, Rahmanpour R. Pathways for degradation of lignin in bacteria and fungi. Nat Prod Rep. 2011;28(12):1883–96.

    Article  CAS  PubMed  Google Scholar 

  22. Weng CH, Peng XW, Han YJ. Depolymerization and conversion of lignin to value-added bioproducts by microbial and enzymatic catalysis. Biotechnol Biofuels. 2021;14(1):84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Konstantinidis KT, Tiedje JM. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc Natl Acad Sci USA. 2004;101(9):3160–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Martiny JBH, Jones SE, Lennon JT, Martiny AC. Microbiomes in light of traits: a phylogenetic perspective. Science. 2015;350(6261):aac9323.

    Article  PubMed  Google Scholar 

  25. Mariadassou M, Pichon S, Ebert D. Microbial ecosystems are dominated by specialist taxa. Ecol Lett. 2015;18(9):974–82.

    Article  PubMed  Google Scholar 

  26. Schaerer L, Putman L, Bigcraft I, Byrne E, Kulas D, Zolghadr A, Aloba S, Ong R, Shonnard D, Techtmann S. Coexistence of specialist and generalist species within mixed plastic derivative-utilizing microbial communities. Microbiome. 2023;11(1):224.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xu QC, Vandenkoornhuyse P, Li L, Guo JJ, Zhu C, Guo SW, Ling N, Shen QR. Microbial generalists and specialists differently contribute to the community diversity in farmland soils. J Adv Res. 2022;40:17–27.

    Article  PubMed  Google Scholar 

  28. Kessler AJ, Chen YJ, Waite DW, Hutchinson T, Koh S, Popa ME, Beardall J, Hugenholtz P, Cook PLM, Greening C. Bacterial fermentation and respiration processes are uncoupled in anoxic permeable sediments. Nat Microbiol. 2019;4(6):1014–23.

    Article  CAS  PubMed  Google Scholar 

  29. Chen YJ, Leung PM, Wood JL, Bay SK, Hugenholtz P, Kessler AJ, Shelley G, Waite DW, Franks AE, Cook PLM, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15(10):2986–3004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen JY, Lin L, Tu QC, Peng QN, Wang XP, Liang CY, Zhou JY, Yu XL. Metagenomic-based discovery and comparison of the lignin degrading potential of microbiomes in aquatic and terrestrial ecosystems via the LCdb database. Mol Ecol Resour. 2024;24(5): e13950.

    Article  CAS  PubMed  Google Scholar 

  31. Lin L, Wang XP, Cao LF, Xu MY. Lignin catabolic pathways reveal unique characteristics of dye-decolorizing peroxidases in Pseudomonas putida. Environ Microbiol. 2019;21(5):1847–63.

    Article  CAS  PubMed  Google Scholar 

  32. Zhou JZ, Bruns MA, Tiedje JM. DNA recovery from soils of diverse composition. Appl Environ Microb. 1996;62(2):316–22.

    Article  CAS  Google Scholar 

  33. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Crocker K, Lee KK, Chakraverti-Wuerthwein M, Li ZQ, Tikhonov M, Mani M, Gowda K, Kuehn S. Environmentally dependent interactions shape patterns in gene content across natural microbiomes. Nat Microbiol. 2024;9(8):2022–37.

    Article  CAS  PubMed  Google Scholar 

  35. Li DH, Luo RB, Liu CM, Leung CM, Ting HF, Sadakane K, Yamashita H, Lam TW. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.

    Article  CAS  PubMed  Google Scholar 

  36. Zhang CW, Fang YX, Yin XR, Lai HF, Kuang ZG, Zhang TXY, Xu XP, Wegener G, Wang JH, Dong XY. The majority of microorganisms in gas hydrate-bearing subseafloor sediments ferment macromolecules. Microbiome. 2023;11(1):37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kang LY, Song YT, Mackelprang R, Zhang DY, Qin SQ, Chen LY, Wu LW, Peng YF, Yang YH. Metagenomic insights into microbial community structure and metabolism in alpine permafrost on the Tibetan Plateau. Nat Commun. 2024;15(1):5920.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11(12):2864–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, Hugenholtz P. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36(10):996–1004.

    Article  CAS  PubMed  Google Scholar 

  42. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36(6):1925–7.

    Article  CAS  Google Scholar 

  43. Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60.

    Article  CAS  PubMed  Google Scholar 

  45. Fu LM, Niu BF, Zhu ZW, Wu ST, Li WZ. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang ZF, Liu LR, Pan YP, Pan J, Li M. Long-read assembled metagenomic approaches improve our understanding on metabolic potentials of microbial community in mangrove sediments. Microbiome. 2023;11(1):188.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  49. BJ W. CoverM: read coverage calculator for metagenomics. 2021.

  50. Yu XL, Zhou JY, Song W, Xu MZ, He Q, Peng YS, Tian Y, Wang C, Shu LF, Wang SQ, et al. SCycDB: a curated functional gene database for metagenomic profiling of sulphur cycling pathways. Mol Ecol Resour. 2021;21(3):924–40.

    Article  CAS  Google Scholar 

  51. Wang XP, Lin L, Dong JD, Ling J, Wang WP, Wang HL, Zhang ZC, Yu XW. Simultaneous improvements of Pseudomonas cell growth and polyhydroxyalkanoate production from a lignin derivative for lignin-consolidated bioprocessing. Appl Environ Microb. 2018;84(18):e01469-e1518.

    Article  CAS  Google Scholar 

  52. Sluiter A, Hames B, Ruiz R, Scarlata C, Sluiter J, Templeton D, Crocker D. NREL/TP-510–42618: Determination of structural carbohydrates and lignin in biomass. Golden: National Renewable Energy Laboratory; 2012.

    Google Scholar 

  53. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14(6):927–30.

    Article  Google Scholar 

  54. Zhou JZ, Deng Y, Zhang P, Xue K, Liang YT, Van Nostrand JD, Yang YF, He ZL, Wu LY, Stahl DA, et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc Natl Acad Sci USA. 2014;111(9):e836–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26(1):32–46.

    Google Scholar 

  56. Clarke KR. Nonparametric multivariate analyses of changes in community structure. Aust J Ecol. 1993;18(1):117–43.

    Article  Google Scholar 

  57. Mielke PW, Berry KJ. Permutation methods: a distance function approach. New York: Springer; 2007.

    Book  Google Scholar 

  58. Kolde R. Pheatmap: pretty heatmaps v1.0.12. 2022.

  59. Allaire JJ, Gandrud, C, Russell, K, Yetman, CJ. NetworkD3: D3 javascript network graphs from R. 2017.

  60. Beckham GT, Johnson CW, Karp EM, Salvachúa D, Vardon DR. Opportunities and challenges in biological lignin valorization. Curr Opin Biotech. 2016;42:40–53.

    Article  CAS  PubMed  Google Scholar 

  61. Zhang ST, Xiao JL, Wang G, Chen G. Enzymatic hydrolysis of lignin by ligninolytic enzymes and analysis of the hydrolyzed lignin products. Bioresour Technol. 2020;304: 122975.

    Article  CAS  PubMed  Google Scholar 

  62. Singh AK, Bilal M, Iqbal HMN, Meyer AS, Raj A. Bioremediation of lignin derivatives and phenolics in wastewater with lignin modifying enzymes: status, opportunities and challenges. Sci Total Environ. 2021;777: 145988.

    Article  CAS  PubMed  Google Scholar 

  63. Becker J, Wittmann C. A field of dreams: lignin valorization into chemicals, materials, fuels, and health-care products. Biotechnol Adv. 2019;37(6): 107360.

    Article  CAS  PubMed  Google Scholar 

  64. Sung HJ, Khan MF, Kim YH. Recombinant lignin peroxidase-catalyzed decolorization of melanin using in-situ generated H2O2 for application in whitening cosmetics. Int J Biol Macromol. 2019;136:20–6.

    Article  CAS  PubMed  Google Scholar 

  65. Dong LJ, Zhang X, Wang M, Fu XH, Liu GX, Zhang SL. Glycolate oxidase gene family identification and functional analyses in cotton resistance to Verticillium wilt. Genome. 2023;66(12):305–18.

    Article  CAS  PubMed  Google Scholar 

  66. Majeke BM, Collard FX, Tyhoda L, Görgens JF. The synergistic application of quinone reductase and lignin peroxidase for the deconstruction of industrial (technical) lignins and analysis of the degraded lignin products. Bioresour Technol. 2021;319: 124152.

    Article  CAS  PubMed  Google Scholar 

  67. Dong HL, Zheng LM, Yu PJ, Jiang Q, Wu Y, Huang CX, Yin BS. Characterization and application of lignin-carbohydrate complexes from lignocellulosic materials as antioxidants for scavenging in vitro and in vivo reactive oxygen species. Acs Sustain Chem Eng. 2020;8(1):256–66.

    Article  CAS  Google Scholar 

  68. Maqbool I, Ponniresan VK, Govindasamy K, Prasad NR. Understanding the survival mechanisms of Deinococcus radiodurans against oxidative stress by targeting thioredoxin reductase redox system. Arch Microbiol. 2020;202(9):2345–6.

    Article  CAS  PubMed  Google Scholar 

  69. Guevara-Flores A, Nava-Balderas G, Martínez-González JD, Vásquez-Lima C, Rendón JL, Mena IPD. A physiological approach to explore how thioredoxin-glutathione reductase (TGR) and peroxiredoxin (Prx) eliminate H2O2 in cysticerci of Taenia. Antioxidants-Basel. 2024;13(4):444.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Murphy CL, Biggerstaff J, Eichhorn A, Ewing E, Shahan R, Soriano D, Stewart S, VanMol K, Walker R, Walters P, et al. Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the phylum. Environ Microbiol. 2021;23(8):4326–43.

    Article  CAS  PubMed  Google Scholar 

  71. Langwig MV, De Anda V, Dombrowski N, Seitz KW, Rambo IM, Greening C, Teske AP, Baker BJ. Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups. ISME J. 2022;16(3):899.

    Article  PubMed  Google Scholar 

  72. Davidova IA, Gieg LM, Duncan KE, Suflita JM. Anaerobic phenanthrene mineralization by a carboxylating sulfate-reducing bacterial enrichment. ISME J. 2007;1(5):436–42.

    Article  CAS  PubMed  Google Scholar 

  73. Musat F, Widdel F. Anaerobic degradation of benzene by a marine sulfate-reducing enrichment culture, and cell hybridization of the dominant phylotype. Environ Microbiol. 2008;10(1):10–9.

    Article  CAS  PubMed  Google Scholar 

  74. Zhuang L, Tang ZY, Ma JL, Yu Z, Wang YQ, Tang J. Enhanced anaerobic biodegradation of benzoate under sulfate-reducing conditions with conductive iron-oxides in sediment of Pearl River Estuary. Front Microbiol. 2019;10:374.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Silva IS, Grossman M, Durranta LR. Degradation of polycyclic aromatic hydrocarbons (2–7 rings) under microaerobic and very-low-oxygen conditions by soil fungi. Int Biodeter Biodegr. 2009;63(2):224–9.

    Article  CAS  Google Scholar 

  76. Yu TT, Wu WC, Liang WY, Wang YZ, Hou JL, Chen YR, Elvert M, Hinrichs KU, Wang FP. Anaerobic degradation of organic carbon supports uncultured microbial populations in estuarine sediments. Microbiome. 2023;11(1):81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Vu VNH, Kohari-Farkas C, Filep R, Laszlovszky G, Ban MT, Bujna E, Gupta VK, Nguyen QD. Design and construction of artificial microbial consortia to enhance lignocellulosic biomass degradation. Biofuel Res J. 2023;10(3):1890–900.

    Article  CAS  Google Scholar 

  78. Wang XP, Lin L, Zhou JZ. Links among extracellular enzymes, lignin degradation and cell growth establish the models to identify marine lignin-utilizing bacteria. Environ Microbiol. 2021;23(1):160–73.

    Article  CAS  PubMed  Google Scholar 

  79. Tsoi R, Wu FL, Zhang C, Bewick S, Karig D, You LC. Metabolic division of labor in microbial systems. Proc Natl Acad Sci USA. 2018;115(10):2526–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wang MX, Chen XL, Liu XN, Fang Y, Zheng X, Huang T, Tang YQ, Ackermann M, Nie Y, Wu XL. Even allocation of benefits stabilizes microbial community engaged in metabolic division of labor. Cell Rep. 2022;40(13): 111410.

    Article  CAS  PubMed  Google Scholar 

  81. Rafieenia R, Atkinson E, Ledesma-Amaro R. Division of labor for substrate utilization in natural and synthetic microbial communities. Curr Opin Biotech. 2022;75: 102706.

    Article  CAS  PubMed  Google Scholar 

  82. Giri S, Waschina S, Kaleta C, Kost C. Defining division of labor in microbial communities. J Mol Biol. 2019;431(23):4712–31.

    Article  CAS  PubMed  Google Scholar 

  83. Fernández-Gómez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, Pedrós-Alió C. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7(5):1026–37.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Sichert A, Corzett CH, Schechter MS, Unfried F, Markert S, Becher D, Fernandez-Guerra A, Liebeke M, Schweder T, Polz MF, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5(8):1026–39.

    Article  CAS  PubMed  Google Scholar 

  85. Orellana LH, Ben Francis T, Ferraro M, Hehemann JH, Fuchs BM. Amann R Verrucomicrobiota are specialist consumers of sulfated methyl pentoses during diatom blooms. ISME J. 2022;16(3):630–41.

    Article  CAS  PubMed  Google Scholar 

  86. Muhammad R, Boothman C, Song H, Lloyd JR, van Dongen BE. Assessing the impacts of oil contamination on microbial communities in a Niger Delta soil. Sci Total Environ. 2024;926: 171813.

    Article  CAS  PubMed  Google Scholar 

  87. Woo HL, Hazen TC. Enrichment of bacteria from eastern Mediterranean sea involved in lignin degradation via the phenylacetyl-coa pathway. Front Microbiol. 2018;9:922.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Duran K, Dikkenberg MV, van Erven G, Baars JJP, Comans RNJ, Kuyper TW, Kabel MA. Microbial lignin degradation in an industrial composting environment. Bioresour Tech Rep. 2022;17: 100911.

    Article  CAS  Google Scholar 

  89. Ul Haq I, Hillmann B, Moran M, Willard S, Knights D, Fixen KR, Schilling JS. Bacterial communities associated with wood rot fungi that use distinct decomposition mechanisms. ISME Commun. 2022;2(1):26.

    Article  Google Scholar 

  90. Li JL, Duan L, Wu Y, Ahmad M, Yin LZ, Luo XQ, Wang X, Fang BZ, Li SH, Huang LN, et al. Unraveling bacteria-mediated degradation of lignin-derived aromatic compounds in a freshwater environment. Sci Total Environ. 2020;749: 141236.

    Article  CAS  PubMed  Google Scholar 

  91. Azubuike CC, Allemann MN, Michener JK. Microbial assimilation of lignin-derived aromatic compounds and conversion to value-added products. Curr Opin Microbiol. 2022;65:64–72.

    Article  CAS  PubMed  Google Scholar 

  92. Rombouts JL, Mos G, Weissbrodt DG, Kleerebezem R, Van Loosdrecht MCM. The impact of mixtures of xylose and glucose on the microbial diversity and fermentative metabolism of sequencing-batch or continuous enrichment cultures. FEMS Microbiol Ecol. 2019;95(8):112.

    Article  Google Scholar 

  93. Legrand E, Riera P, Pouliquen L, Bohner O, Cariou T, Martin S. Ecological characterization of intertidal rockpools: seasonal and diurnal monitoring of physico-chemical parameters. Reg Stud Mar Sci. 2018;17:1–10.

    Google Scholar 

  94. Vazquez DP, Simberloff D. Ecological specialization and susceptibility to disturbance: conjectures and refutations. Am Nat. 2002;159(6):606–23.

    Article  PubMed  Google Scholar 

  95. Heritier-Robbins P, Karthikeyan S, Hatt JK, Kim M, Huettel M, Kostka JE, Konstantinidis KT, Rodriguez LM. Beach sand oil spills select for generalist microbial populations. ISME J. 2021;15(11):3418–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the National Key Research and Development Project (2023YFC3403500), National Natural Science Foundation of China (32370115 and 91951116), and the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP218). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, Shandong, China

    Qiannan Peng & Lu Lin

Authors
  1. Qiannan Peng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lu Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

LL conceived and designed the study. PQN performed the experiments and analyzed the data. LL and PQN wrote the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to Lu Lin.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, Q., Lin, L. Comparative metagenomics reveals the metabolic flexibility of coastal prokaryotic microbiomes contributing to lignin degradation. Biotechnol. Biofuels Bioprod. 18, 9 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13068-025-02605-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13068-025-02605-w

Keywords

Biotechnology for Biofuels and Bioproducts

ISSN: 2731-3654

Contact us