Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium

Peng, Qiannan; Zhao, Cheng; Wang, Xiaopeng; Cheng, Kelin; Wang, Congcong; Xu, Xihui; Lin, Lu

doi:10.1038/s41467-025-56012-8

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Published: 14 January 2025

Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium

Nature Communications volume 16, Article number: 639 (2025) Cite this article

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Abstract

Lignin, as the abundant carbon polymer, is essential for carbon cycle and biorefinery. Microorganisms interact to form communities for lignin biodegradation, yet it is a challenge to understand such complex interactions. Here, we develop a coastal lignin-degrading bacterial consortium (LD), through “top-down” enrichment. Sequencing and physiological analyses reveal that LD is dominated by the lignin degrader Pluralibacter gergoviae (>98%), with additional rare non-degraders. Interestingly, LD, cultured in lignin-MB medium, significantly enhances cell growth and lignin degradation as compared to P. gergoviae alone, implying a role of additional outliers. Using genome-scale metabolic models, metabolic profiling and culture experiments, modeling of inter-species interactions between P. gergoviae, Vibrio alginolyticus, Aeromonas hydrophila and Shewanella putrefaciens, unravels cross-feeding of amino acids, organic acids and alcohols between the degrader and non-degraders. Furthermore, the sub-population ratio is essential to enforce the synergy. Our study highlights the unrecognized role of outliers in lignin degradation.

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Introduction

Lignin is the most abundant aromatic carbon polymer on earth and possesses a recalcitrant and heterogenous nature¹. Microbes have evolved various enzymes and pathways to decompose lignin and drive carbon and energy cycles. Moreover, in nature, various microorganisms co-exist to form consortia that display improved lignin conversion and resist environmental perturbations^2,3,4, indicating that consortia are superior to single species in lignin conversion. Thus, investigation of microbial ecological roles and interaction modes in communities is essential to understand and optimize lignin bioconversion for bioenergy production and climate change alleviation.

Recently advanced high-throughput sequencing technologies have revealed the diversities of in situ and cultivated lignin-degrading microbiomes from various ecosystems. For instance, North American forest soil harbored a lignin-degrading consortium composed of Sphingomonadaceae, Comamonadaceae and Caulobacteraceae families⁵. Another lignin-degrading consortium (LigMet) was enriched from sugarcane plantation soil and revealed to be composed of Alcaligenaceae, Micrococcaceae, Phyllobacteriaceae and Paenibacillaceaea families⁶. Recently, coastal wetlands were also reported to be enriched in lignocellulolytic degrading consortia^7,8. Various coastal lignin-/lignocellulose degrading consortia were obtained⁹. Among them, the prevalence of Vibrio, Aeromonas, Desulfovibrio and Shewanella genera was observed⁸. Metagenome sequencing further revealed the multiple lignin oxidative enzymes (e.g., dye-decolorizing peroxidases (DyP) and laccases) and aerobic-/anaerobic guaiacyl (G), syringyl (S), and p-hydroxyphenyl (H) type lignin unit degradation pathways in these coastal communities⁸. Such studies described taxonomic and functional profiling for various lignin-degrading consortia. However, a comprehensive and mechanistic understanding of these biological systems remains elusive. Notwithstanding the performance of a consortium (e.g., lignin degradation and growth) would be largely attributed to the inter-species interactions within a community. There are six well-known classical bidirectional interaction modes in microbial communities, e.g., commensalism, mutualism, competition, neutralism, amensalism and predation¹⁰. These interactions are driven by the sharing, exchange and competition of metabolites. These metabolites, designated public goods, are commonly produced at high cost to individual members for the benefit of the greater community. Labor division and/or aggregation patterns are mostly used to explain the synergy between lignocellulose degraders^11,12. It is worth noting that communities include not only degraders but also non-degraders that are seen as outliers^13,14,15. What are the exact roles of the non-degraders? Do they contribute to lignin degradation, or are they just ‘cheaters’ within the community?

Experimental verification of the microbial interactions is employed as the direct method^16,17. It requires tedious and trial-and-error exploration and hence underestimates the interactions. Progress in sequencing technologies promotes the development of indirect methods. Molecular ecological networks are constructed based on the statistical correlations of taxa among numerous samples^18,19. The co-occurrence networks are widely used to rapidly elucidate possible interactions in microbial communities, especially for uncultivated microbiomes^15,20. However, correlation-based inference not only commonly overestimates the interaction but also hardly provides a mechanistic understanding of correlations. Alternatively, meta-genomic/transcriptomic and metabolomic analyses reveal the metabolic pathways of the community, demonstrating the metabolic potential of the community^8,21,22. It is a challenge to definitely assign biochemical reactions to those different members in the community, restricting to uncover inter-species interactions¹⁷. Parallel computational efforts have enabled in silico simulation of microbial interactions through genome-scale metabolic models (GSMM) and constraint-based reconstruction and analysis (COBRA) models^23,24,25,26. The biologically realistic metabolic models, coupled with the experimental verification by synthetic communities (the complementary reductionist communities), provide a promising approach to explicitly describe the microbial interactions at the genome scale.

In this study, we “top-down” enriched the lignin-degrading community from the coastal intertidal wetlands of the East China Sea. Through high-throughput sequencing and physiological analyses of the community and isolates, we not only characterized the taxonomic and functional gene profiles of the community but also observed that the low abundant non-degraders supported the community’s growth and lignin degradation. To understand the observed advantage of community, an integrative system biology strategy was employed, which combined multiple biological information from metagenomics, GSMM, metabolic profiling, and mono-/co-culture experiments. Our results elucidate cross-feeding metabolites (e.g., alcohols, amino acids and organic acids) among the degrader and non-degraders, revealing the underestimated role of non-degraders in the complex polymer degradation. Furthermore, it provides a clue for a “bottom-up” design synthetic community based on defined inter-species interactions for lignin bioconversion.

Results

The bacterial consortium enhanced cell growth and lignin degradation over P. gergoviae alone

We tracked the bacterial enrichment cultures under kraft lignin over the course of 40 transfers, which were inoculated from the coastal intertidal wetlands of the East China Sea, as our previous reports⁸ (Figs. 1A, S1A). Initial culture reached 2.27 × 10⁸ copies/mL at 12 days and degraded 7.71% lignin (transfer 1, Fig. S1B, C). After the 21 transfers, the communities increased to 3.81 × 10⁸ copies/mL at least twice as fast, and lignin degradation reached 8.11%. The consequent consortium, designated LD, was developed over the subsequent transfers (total of 40), maintained stable growth (4.46 × 10⁸ copies/mL at 5 days) and lignin degradation (8.53%, Figs. 1A, B, S1B, C). Correspondingly, the LD consortium substantially accumulated total dissolved organic carbon (tDOC) during lignin degradation (Fig. 1C). The compositions of tDOC were examined via FT-ICR-MS analysis. A total of 5681 and 5812 different molecular formulae in the blank control and LD consortium were identified. Most molecular formulae were representative of lignin (78.13–84.78% of overall intensity), polyphenols (8.30–11.25%) and aliphatics (2.43–4.23%). In contrast, lipids (1.79–7.82%), condensed aromatics (0.15–0.68%), tannins (0.15–0.57%), highly unsaturated and phenolic compounds (0.01–0.06%), and carbohydrates (0.01–0.05%) were very low in each group (Fig. S2A). Further LC-MS/MS metabolomic analysis suggested that the lignin compounds in LD consortium were significantly accumulated compared to the blank, including phenol, 2-hydroxy-4-(4-hydroxyphenyl) butanoic acid, 3-(4-hydroxy-3-methoxyphenyl) propanoic acid and 4-(benzoylamino)-3-hydroxybutanoic acid (Fig. S2B and Supplementary Data 1). Similarly, polyphenols (e.g., isovanillic acid, vanillic acid, 3-phenylpropanoic acid, ferulic acid and 4-methoxycinnamaldehyde), aliphatics (e.g., dodecanedioic acid, tetradecanedioic acid, suberic acid and azelaic acid) and condensed aromatics (e.g., chrysen-6-ol, 3-chloro-4,5-dihydroxybenzoic acid and 1,4,5,8-tetraazaphenanthrene) in LD consortium were more abundant over the blank (Fig. S2B and Supplementary Data 1). This demonstrated that LD consortium played a role in lignin conversion, generating more DOC.

**Fig. 1: Cell growth, lignin degradation and community composition of LD community during long-term enrichment under lignin.**

To track community succession under the lignin selection pressure, we analyzed the community compositions across transfers via 16S rRNA gene amplicon sequencing. Similar community compositions and alpha diversity were observed across transfers (Figs. 1D, S1D). The community at T41 consisted of 78 amplicon sequence variants (ASVs), of which ASV1 (Pluralibacter) exhibited the highest abundance, accounting for 98% of sequences (Fig. 1D). In addition, ASV5 (Vibrio, 0.04–0.33%), ASV6 (Sulfurovum, 0.11–0.33%), ASV18 (Aeromonas, 0.02–0.10%) and ASV20 (Shewanella, 0.02–0.11%) exhibited relatively low, yet stable, abundances over time.

To investigate the genomic potentials of LD community in lignin degradation, the metagenomes were sequenced. One high-quality metagenome-assembled genome (MAG, completeness >90%, contamination <5%) was recovered and taxonomically assigned to Pluralibacter gergoviae with the most abundance (>99%). Meanwhile, Vibrio natriegens, Vibrio alginolyticus, Aeromonas hydrophila, Shewanella putrefaciens, Halomonas beimenensis, Acinetobacter junii and Comamonas testosteroni were also annotated by the metagenomes, although their MAGs could not be assembled, due to the very low abundance (Fig. S3A). It is coincided with the 16S rRNA gene amplicon sequencing, where Pluralibacter ASV1 is the major member in LD consortium, and additional ASVs occupy relative lower abundance. Moreover, our recently constructed lignin catabolism functional gene database²⁷, LCdb, was employed to analyze the lignin-degrading functional gene families in LD consortium. The genes encoding lignin oxidizing enzymes (dypB and dypA) and an array of gene families encoding auxiliary enzymes (e.g., cat, dld, gox, gpx, nuoEFG, sod and trxB) were all detected in the MAG of P. gergoviae for lignin depolymerization (Fig. S3B). The gox encodes glycolate oxidase, which generates H₂O₂ to assist the lignin oxidizing enzymes, dypA and dypB, for lignin depolymerization^28,29. The nuoEFG and dld respectively encode quinone reductase and dihydrolipoamide dehydrogenase to prevent lignin repolymerization^30,31. In addition, the cat, trxB and gpx individually encode catalase, thioredoxin reductase and glutathione peroxidase to reduce the H₂O₂ level in response to oxidative stress^32,33,34,35. Furthermore, 82 gene families involved in lignin-derived aromatic compound degradation were also identified in P. gergoviae, including benABCD for benzoate degradation, pobA for 4-hydrozybenzoate degradation, pcaGHBCDIJ for protocatechuic acid (PCA) 3,4-cleavage and catABC for catechol 1,2-cleavage (Fig. S3B). In contrast, gene families involved in lignin depolymerization or complete lignin-derived aromatic compound pathways were not detected to other species in LD consortium. Just a few aromatic compound degrading genes were scattered across several species, e.g., benAB gene in V. natriegens, pcaI and pcaJ genes in Microterricola viridarii (Fig. S3B). It suggested that P. gergoviae should be the only identified lignin degrader, whereas other members shall be non-degraders.

To investigate whether low abundant non-degraders affect the growth and lignin degradation of LD consortium, The four ASVs were targeted. The Pluralibacter ASV1, which harbored 96 lignin-degrading gene families and occupied the majority of LD, was first selected. In addition, three potential non-degraders, Vibrio ASV5, Aeromonas ASV18 and Shewanella ASV20, were chosen. They not only represent the relatively stable members during the LD succession (Fig. 1D) but also are ubiquitous in various coastal lignin/lignocellulose degrading consortia⁸, implying their roles in lignin degradation (Fig. S3A). Isolation screening of LD consortium was performed, in parallel to the high-throughput sequencing. Two isolates were identified and classified as Pluralibacter gergoviae L1 and Vibrio alginolyticus 45, which represented Pluralibacter ASV1 (100% identity in 16S rRNA sequence) and Vibrio ASV5 (98% identity), respectively (Table S1). Meanwhile, Aeromonas hydrophila 07 and Shewanella putrefaciens 63 were used to potentially represent Aeromonas ASV18 and Shewanella ASV20 respectively, based on highly-scored hits (100% identity, Table S1) and the similar phenotypes (e.g., substrate utilization and growth period, Figs. S4, S5).

The four species were individually cultured in lignin-MB mineral medium (Fig. 2A, B). As expected, P. gergoviae showed the best growth under lignin, with 2.18% lignin degradation (Fig. 2). Conversely, V. alginolyticus 45 exhibited slight growth, and the other two species cannot grow alone under lignin, only maintaining a similar cell density with the initial inoculation level (10⁷ copies/mL, Fig. 2A). Moreover, the lignin degradation was not observed in these three mono-culture systems (Fig. 2C). Together with the metagenome data, the results confirmed that P. gergoviae is the lignin degrader, while V. alginolyticus, A. hydrophila and S. putrefaciens are non-degraders. It is worth noting that each mono-culture showed significantly poorer growth than LD consortium (Fig. 2B, C). On one hand, the three low abundant species can reach 10⁶ copies/mL in LD consortium with 10⁵ copies/mL initial inoculation (Fig. 2B). Conversely, they all lysed and decreased to 10⁴ copies/mL in each monoculture system (Fig. 2B). On the other hand, a 2.2-fold reduced cell density of P. gergoviae was also observed in the mono-culture than LD consortium (Fig. 2B).

**Fig. 2: Growth differences of the four species between the mono-culture and LD consortium.**

Consistent with the poor growth, lower lignin degradation (2.18%) was observed in the P. gergoviae mono-culture (Fig. 2C). Correspondingly, lower tDOC was accumulated, compared to LD consortium (Fig. S2C). FT-ICR-MS analysis suggested that the abundance of aliphatics category was significantly decreased by 0.62% in the P. gergoviae mono-culture than LD consortium. LC-MS/MS analysis further revealed that the abundances of dodecanedioic acid, tetradecanedioic acid, suberic acid and azelaic acid in the aliphatics group were greatly lower in P. gergoviae alone than LD consortium (Fig. S2D). In addition, reduced compounds in the lignin and polyphenols categories were observed in P. gergoviae, including 2-hydroxy-4-(4-hydroxyphenyl) butanoic acid, modiolide A, sedanolide, vanillic acid and ferulic acid (Fig. S3D).

Together, these indicated that LD consortium showed advantage of cell growth and lignin conversion than P. gergoviae alone, even though P. gergoviae, as the only identified degrader, accounted for the majority of the consortium. Three additional species, V. alginolyticus, A. hydrophila and S. putrefaciens, also benefitted from the community for cell growth. Hence, mutualistic interactions should occur between the degrader and non-degraders in LD consortium.

Modeling bacterial communities simulated the growths of multi-species combinations

To explore mechanisms of inter-species interactions, GSMMs for P. gergoviae, A. hydrophila, S. putrefaciens and V. alginolyticus were individually constructed and proofed based on the biological information from genomics and laboratory-based culture experiments (Figs. 3A, S4 and Table 1). Moreover, considering that lignin is an amorphous biopolymer formed by radical polymerization of guaiacyl (G), syringyl (S), and p-hydroxyphenyl (H) monolignols³⁶, the metabolic reactions involved in G-, H- and S-type lignin unit degradation pathways were manually added to the GSMM of P. gergoviae, due to its lignin degradation capability, and subsequently modified by growth experiments under lignin derivatives (Fig. S4). As a result, the single-species GSMM of P. gergoviae, namely, iLL1097, GSMM (iLL770) of A. hydrophila, GSMM (iLL743) of S. putrefaciens and GSMM (iLL861) of V. alginolyticus were generated (Supplementary Data 2–5). The four GSMMs displayed high consistency scores (98–99%) though the MEMOTE evaluation³⁷. The overall model statistics for the four species are presented in Table 1.

**Fig. 3: Metabolic modeling analysis for the inter-species interactions.**

Table 1 Overall genome-scale metabolic model statistics for the individual species and four-species combination

Full size table

To verify whether the multi-species combinations could exhibit the growth advantage over mono-culture, the total biomass for all the possible multi-species combinations was simulated via parsimonious flux balance analysis (pFBA) in a constraint-based modeling (CBM), where the four GSMMs were reconciled to unify the metabolites and reaction formats, followed by network integration (Fig. 3A and Supplementary Data 6A–D). Uptake and secretion of several lignin derivates, glycerol, amino acids and organic acids were observed among the species (Figs. 3B, S6 and Supplementary Data 6E). Growths of all the combinations, in simulations, were feasible in 1% lignin-MB mineral medium. The four-species combination showed the best performance, followed by the three-species combination of P. gergoviae, V. alginolyticus and A. hydrophila, the three-species combination of P. gergoviae, S. putrefaciens and V. alginolyticus, and the two-species combination of P. gergoviae, and V. alginolyticus, while P. gergoviae alone exhibited the lowest biomass (Fig. 3C). Dynamic flux balance analysis (dFBA) further simulated that the growth advantage was dependent on the sub-population ratio of the four species (Fig. 3C).

The subsequent co-culture experiments validated the in silico simulation. The growth performances of multi-species combinations were better than all mono-cultures (Figs. 4A, S5). Moreover, the four-species combination showed the highest growth, a 2-fold increase over P. gergoviae alone, and was also the closest to the LD consortium among these combinations (Figs. 4A, B, S5). In addition, lignin degradation of the four-species combination also increased to 3.39% (Fig. 4B). In support of the dFBA prediction, the sub-population ratio of the four species greatly affected the growth performance of the co-culture systems. The synergy was present with the initial population ratio (10⁷: 10⁵: 10⁵: 10⁵ copies/mL). It not only stimulated P. gergoviae growth but also enhanced the growth of V. alginolyticus, S. putrefaciens and A. hydrophila, which could not grow at such low initial inoculation levels in mono-culture (10⁵ copy number/mL, Figs. 4A, S5C–E). In contrast, decreased copy number was observed for each combination with the same initial inoculation (10⁷ copies/mL), suggesting that higher abundances of the three non-degraders would inhibit P. gergoviae growth (Fig. S7). These results confirmed that the four-species combination showed the similar growth advantage with LD community, which should be stimulated by cross-feeding metabolites among the four species.

**Fig. 4: Validation of the inter-species interactions by culture growth and metabolic profiles in the combinations.**

The four species benefited from the characterized cross-feeding of metabolites

To evaluate the potential cross-feedings, we predicted the mutual exchange fluxes in the four-species combination via pFBA (Fig. 3A and Supplementary Data 6E). P. gergoviae secretes succinate, serine and malate to support the growth of V. alginolyticus, S. putrefaciens and A. hydrophila under lignin, whereas the three species supply glycerol, fumarate, aspartate and alanine for P. gergoviae (Fig. 3B, D). In addition, P. gergoviae provides PCA and 4-carboxymuconolactone during lignin degradation to V. alginolyticus. In return, V. alginolyticus secretes 3-carboxy-cis, cis-muconate and 3-oxoadipate-enol-lactone to P. gergoviae for energy conservation (Fig. 3B, D). Consequently, eleven metabolites were targeted, based on the export and import metabolic fluxes between P. gergoviae and a single non-degrader.

To elucidate the metabolic interactions, we first examined a targeted metabolic profiling in the four-species combination. Six of the eleven predicted metabolites were identified in the supernatant of the whole culture system, including PCA, glycerol, succinate, L-malate, L-alanine and L-aspartate (Figs. 4C, S8, S9 and Supplementary Data 7). Additional metabolites, fumarate and serine, were not detected in the co-culture supernatant, possibly due to their instability or rapid turnover. Moreover, it is challenging to detect 4-carboxymuconolactone, 3-carboxy-cis, cis-muconate, and 3-oxoadipate-enol-lactone, due to the absence of standards. Of note, PCA, the precursor of these three metabolites, was detected in the co-culture system, indicating that the cross-feedings could occur between P. gergoviae and V. alginolyticus (Figs. 3B, 4C). Next, the metabolic analysis was further performed by a strain dropout strategy. Leaving out either a single non-degrader from the four-species combination led to an obvious change in the metabolic profile (Fig. 4C and Supplementary Data 7). On one hand, malate and succinate, which were predicted to be provided by P. gergoviae, were both detected in the P. gergoviae mono-culture. It confirmed that P. gergoviae can generate malate and succinate, which could be the carbon source to support the growth of non-degraders under lignin. Moreover, significantly higher concentrations of malate were observed in the combinations containing P. gergoviae, V. alginolyticus and A. hydrophila, further revealing the cross-feeding between P. gergoviae and V. alginolyticus/ A. hydrophila (Figs. 3D, 4C). Unexpectedly, succinate was only detected in the four-species combination and P. gergoviae mono-culture, rather than three-species combinations. It might be the result of a higher turnover rate of succinate in the three-species combinations. A substantially higher exchange flux of succinate was required than malate (823 vs 175, Fig. 3B), while it cannot be sufficiently supplied by P. gergoviae in these three-species combinations due to its limited growth (Fig. S5B). On the other hand, aspartate, glycerol and alanine, which were predicted to be generated by the non-degraders, were significantly more abundant in the combinations that contained the relevant suppliers. Aspartate is a typical example. It was only observed in the combinations, containing V. alginolyticus and A. hydrophila, validating that V. alginolyticus and A. hydrophila provided aspartate for P. gergoviae. More glycerol and alanine were also observed in the four-species combination as well as combinations with multiple suppliers. These cross-feedings stimulated the growth of four species in the whole culture. Consequently, PCA, a typical lignin deconstructed metabolite, was substantially produced in the whole culture. It suggested that bacterial interactions promoted lignin degradation, consistent with the higher substrate utilization (Fig. 4B). Overall, the metabolite differences between the four-species culture and co-culture lacking either a non-degrader verified the metabolic interactions in the whole culture.

To verify whether the predicted exchange metabolites supported the growth of each species, we subsequently investigated the growth for the species when the relevant exchange metabolites were supplemented in mono-culture systems. To exclude the strains’ benefit from the metabolites as universal nutritional resources, two negative controls were used. The P. gergoviae mono-culture system control was supplemented with the metabolites being generated by itself (GEM). The other control was the lignin degrader Pseudomonas gessardii (being isolated from the same site⁹), supplemented with the metabolites (EM1) being generated by the three non-degraders. In agreement with the in silico simulations, the growth of each mono-culture was significantly enhanced by ~2 folds through relevant metabolite supplements (Fig. 4D). In contrast, the addition of metabolites did not significantly improve the growth in the two controls, and even inhibited P. gergoviae growth (Fig. 4D). This demonstrated that the positive roles of predicted exchange metabolites in the growth of these four species. Together, our results demonstrated a synergistic interaction mode between the degrader and non-degraders, providing inspiration for the non-degraders. P. gergoviae, as the lignin degrader in the consortium, degraded lignin and generated succinate and malate to support the growth of non-degraders. In return, the non-degraders supplied glycerol, aspartate and alanine to stimulate the growth of P. gergoviae. The enhanced growth further accelerated lignin degradation, causing the accumulation of PCA.

In addition, the whole extracellular metabolome profiles were also investigated (Supplementary Data 8). A total of 218 metabolites were specificity present in the four-species combination, including aromatic compounds (e.g., benzoic acid), alkanes (e.g., 2,4-Dimethyl hexane), alcohols (e.g., tyrosol and octadecane-1,2-diol,4-hydroxybenzyl alcohol) and ketones (e.g., 3’-Hydroxyacetophenone, Supplementary Data 8B). Conversely, ~100 metabolites uniquely existed in the co-culture lacking either a non-degrader and P. gergoviae mono-culture (Supplementary Data 8C–F). They were mostly involved in alkanes (e.g., 5-butyl-5-ethylpentadecane), organic acids (e.g., 2,3-dihydroxy-2-methylpropanoic acid) and ketones (e.g., 2-ethyl-4-methoxy-Cyclohexanone). Moreover, the abundances of aromatic alcohols were significantly higher in the four-species combination, compared to the three-species combinations and P. gergoviae mono-culture (Fig. S10). Aromatic alcohols are not only lignin catabolites but also involved in microbial quorum sensing molecules³⁸. It suggested that the inter-species interactions could have an effect on the whole metabolome profiles.

Discussion

Low abundant non-degraders boost the performance of LD consortium

In this study, we developed the coastal lignin-degrading LD consortium via “top-down” enrichment. The LD consortium was dominated by P. gergoviae, encoding 96 gene families involved in lignin degradation. In contrast, the additional ASVs not only just occupied ~ 2% (Fig. 1D) but also did not harbor the gene families, participating in the complete lignin-degrading pathways (Fig. S3B). Mono-culture investigation of P. gergoviae further confirmed that it can grow in the MB mineral medium with kraft lignin as the sole carbon source (Fig. 2A). Consequently, P. gergoviae is the only identified species in LD community to perform the task of lignin degradation. P. gergoviae, a Gram-negative, facultatively anaerobic bacterium, is widely distributed in various environments (e.g., soil, plant leaf, aquatic sources and coastal wetlands). It can grow on chlorphenesin and ethylhexylglycerin, and resist parabens and other preservatives³⁹. However, its capacity for lignin degradation had been previously unrecognized. Our study not only reveals that it can degrade kraft lignin but also develops the P. gergoviae-dominated lignin-degrading consortium, LD.

Interestingly, the growth of LD consortium was superior to P. gergoviae alone. Lignin degradation of LD consortium was also higher, generating more abundant DOC than P. gergoviae mono-culture (Fig. S2C, D). Furthermore, we noticed that some of the rare ASVs in LD consortium, e.g., Vibrio ASV5, Aeromonas ASV18 and Shewanella ASV20, also exist in the other coastal lignin/lignocellulose degrading consortia^8,9, implying that they might be commensal with P. gergovia in LD consortium, instead of random partnerships (Fig. S3A). Overall, the low abundant non-degraders in LD consortium, overshadowed by P. gergoviae, play unrecognized roles in lignin conversion.

Cross-feeding of metabolites between degraders and non-degraders promote community growth and lignin degradation

Microbial interactions in communities are key to perform their ecological function (e.g., polymer degradation). Numerous studies explored aggregation and division of labor, based on their distinct physical or metabolic features^40,41. Taking the lignocellulose degradation community as an example, both Citrobacter freundii and Sphingobacterium multivorum are lignocellulosic degraders. Co-culturing of these organisms greatly enhances cellulose degradation, compared to the individual mono-cultures⁴². Alternatively, Sphingomonas sp. in a bisphenol A (BPA)-degrading microbial community is responsible for the degradation of BPA, whereas the additional members, e.g., Pseudomonas sp., degrades the downstream intermediates of 4-DM (4,4′-dihydroxyl-α-methylstilbene) to drive the complete mineralization of BPA¹⁴. In contrast, most species in a community, especially those with low abundance, are harder to disentangle, as they have not definitely roles in the degradation of complex polymers. Here, the lignin degrader P. gergoviae was identified in LD consortium, growth experiments supported its metabolic capabilities for lignin and its derivates (Fig. S4). In contrast, the other members, e.g., V. alginolyticus, A. hydrophila and S. putrefaciens, do not contain any of the lignin-degrading gene families and, thus, could not individually grow with these substrates (Figs. S3B, S4). These species are easy to be classified as “cheaters” and, thus, occupy a tiny niche in LD community. In fact, the low abundant non-degraders boost the performances of LD consortium and four-species combination (Fig. 4B). An uncharacterized boosting strategy should be employed in the LD community.

Mathematical models (e.g., GSMM and COBRA), hence, were employed to pinpoint the potential inter-species metabolic interactions, providing mechanistic interpretation. Our model, coupled with an array of metabolic analysis and mono-/co-culture experiments, suggested that the lignin degrader (P. gergoviae) degraded lignin and provided succinate and malate to non-degraders as labile carbon sources. The enhanced growth of non-degraders, in return, supplied glycerol, alanine and aspartate as nutrients to foster P. gergoviae growth and further stimulate lignin degradation. In addition, the cross-feedings also might have an influence on the whole metabolome, generating the various unique extracellular compounds in the four-species combination. Each species, by releasing and sensing these compounds, would harness interactions to stabilize cooperative behaviors^43,44. Eventually, degraders and non-degraders, at the sub-population ratio (100:1), generated a positive feedback loop via mutualistic cross-feeding interactions. Interestingly, when the populations of non-degraders were artificially increased to levels similar to the degrader, the reconstructed communities immediately collapsed (Fig. S7). This could be interpreted with the “snow-drift game”⁴⁵, in which degraders and non-degraders can co-exist in a system where only degraders can acquire enough net interest. Degraders commonly have a lower growth rate, as they produce public goods at high cost. When non-degraders invade and dominate the community, public goods cannot be produced and shared to the required degree, resulting in community collapse⁴⁶. This study not only uncovers the positive roles of non-degraders in lignin degradation but also demonstrates that cross-feeding amino acids, organic acids and alcohols, rather than lignin metabolites, contribute to lignin degradation.

Synthetic communities pave the road for more accurate modeling of natural communities

With the rapid development of high-throughput sequencing technologies, numerous in situ microbiomes or “top-down” enriched communities have been well-studied, including their compositions, ecological functions and succession properties^8,9,47,48. In contrast, far fewer “bottom-up” synthetic communities have been constructed, in view of defined microbial interactions^49,50. Moreover, successfully constructed synthetic communities are mostly of bi-culture design, based on either aggregation or division of labor^49,51. This is because each member in these strategies has desirable biological function that promotes the performances of the community. Co-culturing of cellulolytic Ruminiclostridium cellulolyticum and sulfate-reducing Desulfovibrio vulgaris was observed to contribute to cellulose degradation⁴⁹. A bi-culture of Acinetobacter sp. AG3 and Bacillus sp. R45, both of which can degrade bromoxynil octanoate (BO), was shown to accelerate BO degradation⁵². Furthermore, R45 also can degrade bromoxynil, the downstream metabolite of BO. However, these observed interactions are pairwise between two organisms. Much more complex interactions, tertiary interactions, should occur in natural communities. Here, the model-based analysis guided us in exploring interactions of the four-species synthetic community (Fig. 3A). A large number of variables (1421–1886 reactions and 1418–1771 metabolites) in each strain were first considered. Then, mathematical algorithms were constructed to explore all the possible strategies for metabolic exchanges in the four-species synthetic community (Fig. 3A, B). Therefore, syntrophic exchanges of small metabolites among the four bacteria were clearly predicted and targeted (Fig. 3). Finally, the quad-culture not only showed a more than 2-fold higher biomass than either mono-culture of P. gergoviae or bi-culture of P. gergoviae with any one of the other 3 species but also exhibited higher lignin degradation (Fig. 4B), laying foundations for the application of synthetic communities in lignin valorization.

In summary, this study explores the outliers in LD consortium. Our work demonstrates that cross-feeding of metabolites between degraders and non-degraders boost community performance, highlighting the key roles of non-degraders in lignin degradation. Moreover, we provide new insights into the “bottom-up” construction of a synthetic community for lignin utilization, which holds promise to efficiently manipulate microbiomes. Limitations of the synthetic community, due to the current state of technology (e.g., microbial isolation and cultivation, and scale of metabolic models), should also be acknowledged^13,53. The performance (e.g., growth and lignin degradation) of the enriched LD community still surpasses the synthetic community. It not only indicates that the additional ASVs in the LD consortium should also provide positive synergies but also implies that much complex cross-feedings, e.g., quorum sensing signals, exoenzymes, and nanowires, might support the cooperation³⁸. In the near future, with the advancement of in silico mathematical models, the facilitation, reliability, and throughput of in silico simulations will increase, and provide more accurate and comprehensive predictions about the cooperative and competitive behaviors in natural communities.

Methods

Enrichment culture

Samples were collected from the intertidal zone (122°6’14.05“E, 29°56’48.90“N) of the south-eastern portion of Zhairuoshan Island, Zhejiang Province, China, in our previous studies and stored at 4 °C until inoculation (Fig. S1A)^8,9,54. Three independent enrichment lines were inoculated from transfer 1 (T1) and were subsequently transferred up to 41 times. Commercial insoluble lignin, kraft (catalog# 370959), was used in this study, which was purchased from Sigma-Aldrich, St Louis, MO, USA. Each enrichment was performed in 100 mL 1% lignin-MB mineral medium, supplemented with 50 μg/mL nystatin (catalog # N814558, Macklin, China)⁹. They were aerobically cultured at 150 rpm, 30 °C. 16S rRNA gene qPCR was performed with universal primer set 1 to monitor consortia growth (Table S2)⁸. 5 mL culture was transferred to 100 mL fresh medium when 16S rRNA gene copies peaked. Two negative controls, without the lignin substrate or addition of microbes, were also sequentially transferred during the enrichment procedure.

Bacterial 16S rRNA gene amplicon sequencing and data analysis

Approximately 15 mL cell culture for each sample was collected to extract genomic DNA via the CTAB extraction method⁵⁵. The DNA was preserved at −80 °C for subsequent 16S rRNA gene amplicon sequencing. The V4 region of the bacterial 16S rRNA gene with unique barcodes was amplified using primer set 2 (515F/806R, Table S2). The amplified products were high-throughput sequenced on the Illumina PE250 platform at Novogene Co., Ltd., Beijing, China. A total of 1,367,732 reads were collected in the 16S rRNA amplicon data sets, with an average of 105,210 reads per sample and a standard deviation of 5,843. The sequencing data displays high Good’s coverage values (>99%) and have been deposited in the NCBI SRA database under the accession number PRJNA1164422.

The sequencing data were analyzed via the “DADA2 R” package (v 1.22.0)⁵⁶ and associated pipeline for primer removal, denoising, filtering, merging, and chimera removal from these sequences, generating amplicon sequence variants (ASVs) with 100% sequence identity. To normalize sequencing depth, sequencing results were subsequently rarefied to 89528 reads, which is based on the sample with the lowest sequence number. Taxonomic identification of ASVs, with a 100% identity, was assigned against the Ribosomal Database Project (RDP) 16S rRNA gene training set 18 through the Bayesian classifier at an 80% confidence level⁵⁷. Shannon index, characterizing alpha diversity, was calculated by the “vegan” package (v2.5.7) in R (v4.1.3)⁵⁸.

Metagenomic DNA sequencing and data analysis

The extracted genomic DNA of LD consortium was performed on an Illumina PE150 platform at Novogene Co., Ltd., Beijing, China, with 150 bp paired-end sequencing. A total of 335,203,100 reads were included in the three metagenome data sets, with an average of 111,734,367 reads per sample and a standard deviation of 61,029,572. The metagenome data have been deposited in the NCBI SRA database under the accession number PRJNA1156935.

Raw reads were assessed and trimmed by Trimmomatic (v0.39)^59,60 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 were co-assembled to contigs using MEGAHIT (v1.2.9)^61,62 (parameters: --k-list 21,29,39,59,79,99,119,141). 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 -x 10) within the metaWRAP pipeline^63,64. Completeness and contamination of the MAG were estimated using CheckM (v1.2.2)^63,65.

The taxonomic assessment of the MAG was assigned based on the Genome Taxonomy Database GTDB (release 06-RS202)⁶⁶ via the classify workflow of GTDB-Tk (v1.5.1)⁶⁷, using the “classify_wf” parameter. In addition, the taxonomic assignment of the metagenomic reads was analyzed by the tool Kraken 2^68,69.

The MAG and co-assembled metagenomic reads were both searched against the LCdb database to generate the functional profiles of lignin-degrading gene families²⁷. The DIAMOND program (v0.9.25) and BLASTp with identity ≥ 70% and e-value ≤ 1 e⁻⁵ were used^27,63,70.

Isolation assay

Serial dilutions of the enrichment cultures were spread on marine broth 2216E agar plates⁷¹. Plates were incubated at 30 °C until single colonies were observed. Individual colonies were picked according to distinct colony morphology and cultured on 2216E agar plates to obtain pure isolates. The full length of 16S rRNA gene for each isolate was amplified using primer set 3 (27F/1492R, Table S2) and sequenced by a 3500XL Genetic Analyzer (Applied Biosystems, ThermoFisher). The 16S rRNA gene sequences were deposited in the GenBank nucleotide database of the National Center for Biotechnology Information (NCBI) under the accession numbers OR945738, OR945739, OR945740, and OR999868, respectively. The whole-genome sequencing of Pluralibacter gergoviae L1 was performed on the Pacific BioSciences (PacBio) sequencing platform at Novogene Co., Ltd., Beijing, China, and deposited in the NCBI SRA database under the accession number PRJNA1041589.

Quantitative PCR (qPCR)

16S rRNA gene qPCR was performed to measure the growth of consortia or individual isolates in lignin-MB mineral medium. 2 mL cell suspension from each culture was collected to extract genomic DNA by the CTAB method⁵⁵. The qPCR was performed with universal primer set 1 and specific primer sets, respectively (Table S2)⁹. The cell growth in each sample was assessed by the 16S rRNA copy numbers/mL. The experiments were performed with three biological replicates.

Lignin degradation using Prussian Blue assay

At the end of culture period (day 10), lignin in cell culture medium was completely dissolved by adjusting the pH to 12.5 using 2 M NaOH. 1.5 mL sample was mixed with 100 μL 8 mM K₃Fe(CN)₆ and 100 μL 0.1 M FeCl₃ for 5 min at room temperature (RT). The absorbance at 700 nm was monitored to evaluate the concentration of residual lignin in each culture system⁷². The standard curve was generated from serially diluted kraft lignin (2–10 × 10⁻⁶ g/mL, Supplementary Data 9). The lignin concentration in each sample was calculated by the following formula. Lignin concentration = ((OD₇₀₀ + 0.0163)/33750) × Dilution × Volume. Volume indicates the 100 mL cell culture, and dilution represents the 1000-fold dilution. The experiments were performed with three biological replicates.

DOC measurement and analysis

Before the assay, the glassware was combusted at 450 °C for 6 h, while other instruments (e.g., filters) were cleaned with acidified ultrapure water to avoid exogenous contamination. Enriched culture was grown in MB mineral medium with 1% lignin as the sole carbon source⁹. ~10 mL culture at day 0, 5 and 10 were filtered through GF/F (0.7 μm, 1825-025, Whatman, America) glass fiber filters, respectively. The resulting total dissolved organic carbon (tDOC)-containing filtrate was stored at 4 °C until further processing. The DOC samples were 1000-fold diluted with ddH₂O, 30 mL of which was transferred to a pre-cleaned 40-mL glass VOC vial (CNW, VAAP-340024-2895A-100). Next, 40 μL phosphoric acid was added to each sample and vigorously shaken to remove inorganic carbon. DOC concentrations were measured by a TOC-V CPH total organic carbon analyzer (Shimadzu, Kyoto, Japan), according to the published method⁷³. The experiments were performed with three biological replicates.

FT-ICR-MS spectrometry for DOC analysis

DOC was extracted from bacterial cultures by the solid-phase extraction method⁷⁴. Samples were acidified to pH 2 using formic acid (CNW, CAEQ-4-014784-0250, 98%), and then extracted by 500 mg Agilent Bond Elut PPL cartridges, which were subsequently rinsed with acidified pure water, dried, and eluted with 10 mL methanol (Sigma-Aldrich Chromasolv LC-MS®). All DOC samples were analyzed on a 9.4 T Apex Ultra FT-ICR mass spectrometer (Bruker Daltonics) equipped with an electrospray ionization (ESI) source (Bruker Apollo II) at the State Key Laboratory of Heavy Oil Processing, Beijing, China⁷⁵. The details of FT-ICR-MS data analysis are described in Text S1. The experiments were performed with three biological replicates.

Liquid chromatography tandem mass spectrometry (LC-MS/MS) for DOC analysis

All DOC samples were also analyzed by a high-performance liquid chromatography (HPLC) system coupled with LTQ Orbitrap velos pro ETD mass spectrometry (Thermo Scientific, Germany), according to the reported method^74,76. Acquired raw mass data were processed using Compound Discoverer (v3.3). Relative feature intensities of extracted ion chromatograms (XICs) were defined through integration of peak areas. For molecular annotation of features, molecular formulas were calculated based on exact masses of the XICs from negative and positive ionization experiments, and metabolic annotation was made by searching MS and MS/MS information against the mzCloud database. The details of LC-MS/MS assay are described in Text S1. The experiments were performed with three biological replicates.

Reconstruction of single-species metabolic network models

P. gergoviae L1 and V. alginolyticus 45 were isolated from the LD consortium. A. hydrophila 07 (MCCC 1A00007) and S. putrefaciens 63 (MCCC 1A00763) were obtained from the Marine Culture Collection of China (MCCC), after being determined as the closest species for ASV18 and ASV20 in LD consortium. The GSMMs for the four stains were then constructed. The genome sequence for P. gergoviae, which was sequenced in this study, was used for P. gergoviae model construction, while the genome sequences representing V. alginolyticus, A. hydrophila and S. putrefaciens were targeted based on BLAST search screening and retrieved from GenBank (Table S1 and Fig. S11). The GSMMs were constructed as previously reported method^13,52,77. Initial draft metabolic models were generated by the Model Seed⁷⁸ based on a previous annotation with RAST⁷⁹. Each draft model was, first, manually curated by checking all the gene-reaction associations and correcting dubious annotations according to the literature and other available resources, e.g., UniProt⁸⁰, BiGG⁸¹, KEGG⁸² and MetaCyc⁸³. They were further checked to (1) add potentially missed reactions based on literature and/or genome annotations of phylogenetically close species; (2) debug and futile or erroneously energy-generating loops were removed. Next, species growth was simulated for each model under different carbon sources. The simulated reactions were added, corrected and validated by growth data and available carbon source spectra (Fig. S4, Supplementary Data 2–5 and Text S1), ensuring that the models were able to produce all biomass components in MB mineral medium, supplemented with different carbon sources. Furthermore, all reactions were screened to verify that they were stoichiometrically balanced. Errors in reaction elemental balance and directionalities were corrected according to MetaCyc, BiGG or KEGG. In the assumed pseudo-steady-state, flux balance analysis (FBA) was used to predict activities via COBRAToolbox in MATLAB, when GLPK was used as the linear programming solver⁸⁴. Finally, the constructed GSMMs coincided with the experimental knowledge of the nutrients required for each species’ growth. The quality of each model was evaluated by the metabolic model testing suite (MEMOTE)³⁷.

Consortium network model construction

The seven multi-species combination models were constructed by the function “GetMultiSpeciesModel” of LD in MATLAB⁵². Parsimonious Flux Balance Analysis (pFBA)^52,85 was used to simulate the steady-state flux distributions in each combination model, while COBRAToolbox⁸⁴ was employed to predict the growth of the combinations or species in the presence of lignin. In the simulations, benzoate, benzoyl-CoA, vanillin, vanillate, 4-coumarate, 4-hydroxybenzoate, coniferyl aldehyde, ferulate, 4-hydroxyphenylacetate, protocatechuate and catechol, which are lignin derivatives, were set as carbon sources in MB mineral medium. The biomass of each combination was set as the sum of biomass of each species. In order to identify the key exchange compounds that limit the biomass yields of the four species, pFBA was performed to minimize the sum of flux while maintaining the optimal flux of biomass. Each reversible reaction in pFBA was split into two irreversible reactions (${v}_{{irrev}}$). Each reaction was constrained to carry a non-negative, steady-state flux. The biomass flux was set to the maximum and then the objective function was defined to minimize the net flux (sum of all irreversible reaction fluxes):

$$\min {\sum }_{j=1}^{i}{v}_{{irrev},j}$$

$${{\rm{Subject\; to}}}:\,\begin{array}{c}{S}_{{irrev}}\cdot {v}_{{irrev}}=0\\ 0\le {v}_{{irrev},j}\le {v}_{\max }\end{array}$$

Where: ${S}_{{irrev}}$ represents a stoichiometric matrix of irreversible model. ${v}_{{irrev},j}$: represents the reaction flux vector in the irreversible model. i is the number of gene-associated irreversible reactions in the combination network⁸⁵. The negative and positive fluxes of exchange reactions represented the uptake and secretion of metabolites, respectively. The exchanged compounds were further selected to (1) include cross-feedings between P. gergoviae and a single non-degrader; (2) show a negative flux (<−10) in one species, yet a positive flux (>10) in another species^86,87; (3) exclude the components in 1% lignin-MB mineral medium^87,88, for the following metabolite analysis.

Dynamic flux balance analysis (dFBA)^89,90,91 was used to simulate the growth of the four-species combination in 1% lignin-MB medium across time, with two sub-population ratios (100:100 and 100:1). The total time period of each simulation cycle was subdivided into predefined discrete time intervals. At each time interval, the nutrients were distributed among the four species based on the biomass ratio. FBA was then used to predict growth (biomass fluxes), nutrient uptake and metabolite secretion rates of each species. Following each time step, media uptake bounds and species biomass were updated. The new concentrations were then used as a starting point for the next iteration. The details are described in Text S1.

Co-culture experiments

The isolates P. gergoviae L1, V. alginolyticus 45, A. hydrophila 07 and S. putrefaciens 63 were used for co-culture experiments to examine the in silico predictions. The co-culture systems were cultured in 1% lignin-MB mineral medium at 30 °C for 7 days and included two-species combinations (P. gergoviae-V. alginolyticus, P. gergoviae-A. hydrophila and P. gergoviae-S. putrefaciens), three-species combinations (P. gergoviae-V. alginolyticus-A. hydrophila and P. gergoviae-V. alginolyticus-S. putrefaciens) and a four-species combination (P. gergoviae-V. alginolyticus-A. hydrophila-S. putrefaciens). They were inoculated with two sub-population ratios (100:100 and 100:1), and growth was monitored by 16S rRNA gene qPCR (Table S2). The co-culture experiments were performed with three biological replicates.

Extracellular metabolomic profiles

P. gergoviae mono-culture, three-species combinations (P. gergoviae-V. alginolyticus-A. hydrophila, P. gergoviae-V. alginolyticus-S. putrefaciens and P. gergoviae-A. hydrophila-S. putrefaciens) and the four-species combination (P. gergoviae-V. alginolyticus-A. hydrophila-S. putrefaciens) were cultured in 1% lignin-MB mineral medium at 30 °C. Co-culture supernatants were collected at day 5 by centrifugation at 4 °C, 13,800×g for 10 min. The derivatized samples were injected into the Shimadzu single quadrupole GCMS-QP2020 NX gas chromatograph-mass spectrometer (GC-MS) equipped with an HP-5MS column (thickness 0.25 μm; length 30 m; diameter 0.25 mm)⁷². Ethylvanillin (33 μg/mL, catalog # E107635, aladdin) was used as an internal standard. Relative metabolite abundances were calculated by the following formula. Relative metabolite abundance = (peak areas (unique mass) of identified metabolites) / (peak areas of ethylvanillin). The details of GC-MS assay are described in Text S1. The experiments were performed with three biological replicates.

Exchange metabolites measurements

The targeted metabolites, glycerol and PCA, were detected by gas chromatography–mass spectrometry (GC-MS). Meanwhile, fumarate, succinate, L-malate, L-serine, L-aspartate and L-alanine were identified by liquid chromatography–mass spectrometry (LC-MS), respectively. Mono-/co-culture supernatants were collected by centrifugation at 4 °C, 13,800×g for 10 min. For GC-MS analysis, the samples were detected and screened by Shimadzu single quadrupole GCMS-QP2020 NX gas chromatograph-mass spectrometer (GC-MS) equipped with a HP-5MS column (thickness 0.25 μm; length 30 m; diameter 0.25 mm)⁷². For LC-MS analysis, each sample was lyophilized and then analyzed by the LC-MS (G2-XS QTof, Waters). A C₁₈ reverse phase column (3.0 × 150 mm Agilent Poroshell 120 EC-C18 column containing 2.7 μm particles) was used for liquid chromatography. Mass spectrometry was performed using electrospray ionization in the positive or negative ion mode with MS^e acquisition mode, with a selected mass range of 50–1200 m/z. The metabolite standard samples were purchased from Solarbio, Beijing, China, including glycerol (catalog# SG9250), PCA (catalog# SD8310), fumarate (catalog# ST8390), succinate (catalog# SS9520), L-malate (catalog# SM8110), L-serine (catalog# SS8430), L-aspartate (catalog# SA8560) and L-alanine (catalog# SA8080). They were analyzed by either LC-MS or GC-MS with the above procedure, at a concentration gradient (1–100 mg/L). The retention time (RT), patterns of fragment ion peaks for standards, and resulting mass spectra were used to measure the metabolites. The details of GC-MS and LC-MS analysis are described in Text S1. The experiments were performed with three biological replicates.

Measurement of individual species growth supplemented with the predicted exchange metabolites

To test the contributions of predicted exchange metabolites (EM) for the species growth, each species was cultivated by itself in 1% lignin-MB medium at 30 °C for 7 days, supplemented with the hypothesized exchange metabolites (60 mg/L for each metabolite). Glycerol, fumarate, L-aspartate and L-alanine, as EM1, were added in P. gergoviae mono-culture system, while succinate, L-malate and L-serine, as EM2, were added in V. alginolyticus mono-culture medium. Meanwhile, L-malate and fumarate, as EM3, were added in the A. hydrophila mono-culture system, whereas succinate and L-serine, as EM4, were introduced in the S. putrefaciens mono-culture system. In addition, P. gessardii L6, a lignin degrader, was used as the reference strain. It was isolated from the same site in our previous study⁹. Mono-cultures of P. gessardii L6, supplemented with EM1, and P. gergoviae L1, supplemented with its secreted metabolites (succinate, L-malate and L-serine), were both used as the negative controls. The culture experiments were performed with three biological replicates.

Statistical analysis

Statistical differences between the two groups were compared using two-sided unpaired Student’s t-tests. p < 0.05 was considered statistically significant, and p < 0.01 was deemed highly significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The 16S rRNA gene amplicon sequencing raw data was deposited in the NCBI SRA database under the accession number PRJNA1164422. The metagenome sequencing raw data was deposited in the NCBI SRA database under the accession number PRJNA1156935. The 16S rRNA gene sequence for each isolate was deposited in the GenBank nucleotide database of the NCBI under the accession numbers OR945738, OR945739, OR945740, and OR999868, respectively. The whole-genome sequencing data of P. gergoviae was deposited in the GenBank nucleotide database of the NCBI under the accession number CP139937. The FT-ICR-MS raw data were deposited at https://doi.org/10.5281/zenodo.13846299. The mass spectrometry raw data for GC-MS, LC-MS and LC-MS/MS in this study was deposited in the Mass spectrometry Interactive Virtual Environment (MassIVE) under the accession number MSV000095829, MSV000095821 and MSV000095828, respectively. The RAST job ID of P. gergoviae, V. alginolyticus, A. hydrophila and S. putrefaciens were 1235504, 1229613, 1230523 and 1235857, respectively. The GSMM files generated from this study are available at https://github.com/pengqiannan/Modeling-bacterial-interactions/tree/main/data. Source data are provided with this paper.

Code availability

The codes for pFBA, dFBA, Memote and DADA2 R are available in the GitHub repository (https://github.com/pengqiannan/Modeling-bacterial-interactions/tree/main/code) and are also available on Zenodo under https://doi.org/10.5281/zenodo.14252387⁹².

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32370115 (L.L.) and 91951116 (L.L.)), National Key Research and Development Program of China (2023YFC3403500 (L.L.), 2019YFA0606704 (L.L.) and 2023ZD04076 (C.Z.)), National Natural Science Foundation of China (32300239 (C.Z.)) and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP218 (L.L.)). This study contributes to the science plan of the Ocean Negative Carbon Emissions (ONCE) Program. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to Wanpeng Wang in the Marine Culture Collection of China (MCCC) for providing the strains for this work. Moreover, we thank Guannan Lin, from the Core Facilities for Life and Environmental Sciences, State Key Laboratory of Microbial Technology of Shandong University, for assistance in high-resolution Q-TOF mass spectrometry and data processing.

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These authors contributed equally: Qiannan Peng, Cheng Zhao.

Authors and Affiliations

Institute of Marine Science and Technology, Shandong University, Qingdao, China
Qiannan Peng & Lu Lin
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
Cheng Zhao, Kelin Cheng & Congcong Wang
Key Laboratory of Aquacultral Biotechnology, Chinese Ministry of Education, Ningbo University, Ningbo, China
Xiaopeng Wang
Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, China
Xihui Xu
Qingdao Key Laboratory of Ocean Carbon Sequestration and Negative Technology, Shandong University, Qingdao, China
Lu Lin

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Qiannan Peng
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Cheng Zhao
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Xiaopeng Wang
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Kelin Cheng
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Congcong Wang
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Xihui Xu
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Lu Lin
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L.L. conceived and designed the study. Q.P., C.Z., K.C., C.W. and X.X. performed in silico simulation. Q.P. and X.W. performed experiments. L.L., Q.P. and C.Z. wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Lu Lin.

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Nature Communications thanks Tito Damiani, Tomáš Pluskal and the other anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

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Peng, Q., Zhao, C., Wang, X. et al. Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium. Nat Commun 16, 639 (2025). https://doi.org/10.1038/s41467-025-56012-8

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Received: 24 April 2024
Accepted: 06 January 2025
Published: 14 January 2025
DOI: https://doi.org/10.1038/s41467-025-56012-8

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