Microbial Communication Drives Division of Labor in SynComs for Herbicides Degradation

Microbial Communication Drives Division of Labor in SynComs for Herbicides Degradation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Microbial Communication Drives Division of Labor in SynComs for Herbicides Degradation Haiyan Chu, Yuxiao Zhang, Jack A. Gilbert, Li Nie, Yu-Meng Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8076029/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The persistence of multiple herbicide residues poses a major challenge to agricultural productivity and soil health in the Northeast Black Soil region. To address this issue, we employed a “top-down” strategy to construct two synthetic microbial communities (SynComs YKB and HKB) that significantly enhanced the co-degradation of multiple herbicides under both laboratory and soil conditions. Multi-omics analyses demonstrated that each SynCom maintained a distinct metabolic potential, facilitated a metabolic division of labor among its constituent members, and utilized quorum sensing (QS) as the key regulatory mechanism governing this division of labor. Specifically, Acyl-homoserine lactones (AHLs) governed the functional dynamics of SynCom YKB and autoinducer-2 (AI-2) primarily regulated SynCom HKB. QS molecules also regulated and enhanced downstream metabolic functions, further promoting stable microbial interactions and the herbicide degradation process. Maize pot experiments combined with metagenomic profiling demonstrated that the SynComs provided many benefits for the native black soil ecosystem, and confirmed the critical role of QS-mediated interactions between the SynComs and indigenous microorganisms. This study presents an efficient bioaugmentation strategy to alleviate herbicide residues in black soil ecosystems, while simultaneously offering new insights into the communication mechanisms, interaction models, and design principles of SynComs for environmental bioremediation. Biological sciences/Microbiology/Microbial communities/Symbiosis Biological sciences/Microbiology/Environmental microbiology/Soil microbiology Biological sciences/Microbiology/Bacteria/Bacterial synthetic biology Biological sciences/Microbiology/Microbial communities/Microbiome SynComs co-herbicides degradation multi-omics analyses quorum sensing division of labor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Significance This study presents a new framework for bioremediation that unites microbial ecology, synthetic biology, systems modeling, and sustainable agriculture. We constructed two synthetic microbial communities (SynComs) capable of simultaneously degrading multiple herbicides, while maintaining stability and promoting crop growth. Through integrated multi-omics, we reveal that distinct quorum-sensing (QS) systems (acyl-homoserine lactone (AHL)-mediated and autoinducer-2 (AI-2)-mediated signaling) coordinate metabolic cooperation, division of labor, and ecological resilience among community members. These findings identify QS-mediated communication as a governing principle for designing effective SynComs, enabling predictable and scalable microbial strategies for soil restoration. Our findings advance microbial systems design and biotechnological engineering for pollution control and provide a new conceptual and technological foundation for harnessing microbial cooperation to ensure food security and ecosystem sustainability. Introduction The northeast black soil region of China, recognized as one of the world’s four major chernozem zones, play a fundamental role in Chinese national food security 1-5 . However, decades of intense agriculture have led to an accumulation of herbicide residue pollution 6, 7 . While the application of herbicides has effectively suppressed the growth of weeds in farmlands to a certain extent 8 , high frequency and high-dose applications coupled with declining utilization efficiency have led to significant herbicide residues in soils, which reduces soil biodiversity, accelerate nutrient loss, degrades environmental quality, and poses potential health risks via food chain accumulation 9-12 . The main herbicide residues include acetochlor, atrazine, nicosulfuron, and fomensafen, spanning multiple types such as amides, triazines, sulfonylureas and ethers, with co-contamination being most common 13-15 . The primary crops cultivated in the northeast chernozem region are maize, soybean, and rice 16, 17 and herbicide types and application rates vary considerably across cropping systems and farming practices, leading to significant regional differences in herbicide residue levels 18, 19 . These disparities complicate remediation and management efforts, making the development of effective strategies to eliminate co-occurring herbicide residues and restore the ecological functions of Northeast chernozems an urgent scientific and technological challenge. Microbial remediation represents a promising approach for mitigating herbicide pollution 20, 21 . However, bioremediation with a single microorganism has inherent limitations when addressing complex mixed contaminants 22-24 , including narrow substrate specificity, which restricts degradation capacity to targeted pollutants 25 , and limited environmental adaptability, which impacts the survival and remediation efficacy of the bacteria 26, 27 . Synthetic microbial communities (SynComs), comprising the artificial combination of two or more microorganisms with clearly defined taxonomic status and functional characteristics at predetermined ratios, overcome the limitations of a single strain strategy, so as to both enhance degradation efficiency of complex herbicide pollution and support better ecosystem adaptation and survival 28, 29 . SynComs are often designed to provide a parsimonious combination of functional traits to facilitate a defined outcome with limited redundancy among community members 30 . However, the SynCom membership is also selected so as to facilitate metabolic coordination between members. This metabolic interaction is thought to contribute to the structural stability, adaptation to diverse environments, and the execution of complex functions by the assemblage. Recent advances in AI, synthetic microbiology, and multi-omic analyses have accelerated effective SynCom design, yet we still lack a fundamental understanding of the key biological features that facilitate metabolic interaction between members and stable ecological dynamics when introduced to a natural soil 31, 32 . We posit, that systems ecology theory provides a foundation on which to understand the interactions and regulatory mechanisms within SynComs, and can help decipher their functional networks, mechanisms of interindividual communication, and the resulting division of metabolic activity among constituent strains 28, 30 . Recent work has provided proof-of-concept that such communities can be engineered to achieve this goal 11 . In a previous study, we constructed a four-member SynCom that simultaneously degraded 8 herbicides in black soils, while also enhancing microbial diversity, stabilizing colonization, and stimulating soil carbon metabolism. This demonstrated that SynComs can both remediate herbicide residues and restore critical soil functions. However, the regulatory mechanisms enabling these communities to coordinate activity and divide labor remained unclear. Microbial interactions within SynComs are fundamentally shaped by quorum sensing (QS) and metabolic cross-feeding, which collectively determine the community's structure and function 33, 34 . The QS system enables bacteria to perceive population density through the production, release, and detection of extracellular signaling molecules 35, 36 . This facilitates coordination of gene expression and behaviors among community members to optimize spatiotemporal organization, functional output, ecosystem integrity, and controllability 37 , as well as inter-species interactions, microbial environmental functions, and host-microbe relationships 38, 39 . QS constitutes a complex signaling network comprising multiple pathways 40, 41 . Extensive research has identified and characterized numerous distinct QS signal molecules (Table S1) 42-46 : (1) Acyl-homoserine lactones (AHLs); (2) Autoinducers (AI-2/AI-3); (3) Autoinducing peptides (AIPs); and (4) Others, such as Pseudomonas quinolone signal (PQS) and diketopiperazines (DKPs). Metabolic complementarity within microbial communities is regulated by QS, enabling assemblages to circumvent metabolic bottlenecks and the accumulation of toxic intermediates within single cells 47, 48 . Therefore, better characterization of the QS activity among members will enable the development of more robust SynComs for applied solutions to pollution. Here we present two new SynComs designed to degrade complex multi-herbicide pollution. Through integrated multi-omics analyes we characterized the metabolic interactions, signal transduction pathways, and functional regulation that enable each SynCom to degrade soil herbicide residues. We also developed a predictive model that explains how QS mediates SynCom ecological dynamics. In sum, we present a robust, highly adaptive, and efficient bioremediation tool for herbicide pollution. Results Screening of herbicide-degrading strains in northeast black soil and construction of SynComs We used enrichment and laboratory domestication techniques to isolate 29 potential herbicide-degrading strains from six soil samples (Fig. S1 , Table S2). In monoculture each of the 29 strains exhibited different herbicide degradation abilities at an initial concentration of 100 mg/L, though the degradation rates were generally below 50% within 60 h (Table S3). However, Pseudomonas sp. B1, Ralstonia sp. R5, Bacillus sp. H1, Klebsiella sp. L3, Enterobacter sp. E3, Acinetobacter sp. K1, and Comamonas sp. Y1 all demonstrated degradation rates exceeding 50% for specific herbicides. Through random combination experiments with SynComs comprising 2 to 5 strains, we identified two 3-member SynComs with the ability to perform 80% degradation efficiency for 8 herbicides (atrazine, acetochlor, butachlor, metolachlor, metribuzin, nicosulfuron, pyrazosulfuron, fomensafen; Fig. S2a). SynCom YKB comprised Comamonas sp. Y1, Acinetobacter sp. K1, and Pseudomonas sp. B1; while SynCom HKB comprised Bacillus sp. H1, Acinetobacter sp. K1, and Pseudomonas sp. B1. Further analysis revealed that the SynCom YKB not only exhibited enhanced degradation performance but also showed a significant increase in herbicide tolerance, with resistance levels of ~ 100% (Fig. S2b). Conversely, SynCom HKB showed no significant improvement in resistance to herbicides compared to the monoculture strains (Fig. S2b). The ability of each SynComs to degrade a combination of 8 herbicides at varying concentrations were investigated. Both SynComs could efficiently degrade the herbicide mix, following a first-order kinetic model (Extended Data Fig. 1 a, b). Notably, both SynComs had optimal observed performance when the initial concentration of the 8-herbicide mixture was 100 mg/L. Therefore, this condition was selected as the standard treatment for subsequent studies. Throughout the degradation process, all members of each SynCom maintained consistent growth trends (Extended Data Fig. 1 c), providing a solid foundation for stable microbial interactions and functional performance. Furthermore, the overall biomass in both the SynComs and monoculture systems tended to be consistent, indicating that the enhanced herbicides degradation capacity in SynComs was not achieved through a simple increase in cell numbers (Fig. S3). Proteomics reveals metabolic functions and division of labor among SynComs members To elucidate the interaction patterns within the SynComs and the mechanisms underlying herbicide degradation, we conducted a comparative proteomic analysis under the following conditions: (1) monocultures versus SynComs, and (2) presence or absence of 100 mg/L of the 8-herbicide mix. Compared to monocultures, the SynComs demonstrated significant up-regulation of numerous proteins in the absence of herbicide pollution. SynCom YKB showed upregulation of 1155, 877, and 577 proteins, and downregulation of 81, 36, and 213 proteins compared to monocultures of Y1, K1, and B1, respectively (Extended Data Fig. 1 d). Similarly, SynCom HKB had upregulation of 812, 189, and 319 proteins, and downregulation of 33, 282, and 12 proteins, compared to the monocultured strains H1, K1, and B1, respectively. In the presence of the 8 herbicides, SynComs YKB and HKB showed up-regulation of 185 and 79 proteins and down-regulation of 69 and 76 proteins, respectively, compared to the no herbicide control. Analysis of differentially expressed proteins (DEPs) annotated to each member revealed that in SynCom YKB, strain K1 contained the most DEPs, with 63 up-regulated and 19 down-regulated proteins (Extended Data Fig. 1 e). In SynCom HKB, strain B1 showed the highest number of DEPs, including 48 up-regulated and 31 down-regulated proteins. Simultaneously, subcellular localization analysis of the DEPs indicated that a majority were localized in the cytoplasm. These findings may suggest distinct metabolic partitioning among members within different SynComs, with different members showing different levels of protein expression across each SynCom and in monoculture (Extended Data Fig. 1 c and Fig. S4). We further employed GO, COG, and KEGG databases to compare the DEPs and functional metabolic pathways enriched among members of the SynComs. In the GO enrichment analysis of SynCom YKB, compared to monoculture, strain K1 exhibited greater enrichment of proteins involved in cellular biosynthetic process, cellular metabolic process, and macromolecule biosynthetic process. Strain B1 showed significant upregulation in catalytic activity, nucleotide binding, and metabolic process. Strain Y1 predominantly contributed to transporter complex, localization, and amino acid/organic acid metabolic processes (Fig. S5). Notably, strain Y1 demonstrated greater metabolic functional divergence compared to the other two members. In SynCom HKB, GO analysis revealed that strain K1 was mainly enriched in chemotaxis, signaling receptor activity, and nucleotide binding. Strain B1 upregulated functions associated with localization, transporter complex, and oxidoreductase activity, and Strain H1 dominated DNA catalysis, DNA-binding transcription factor activity, and sulfur biosynthetic process (Fig. S6). Compared to SynCom YKB, the members in SynCom HKB system showed lower overlap in GO enrichment, indicating more distinct metabolic partitioning among strains. The COG and KEGG enrichment analyses also elucidated the key functional roles undertaken by different members within the SynComs (Fig. 1 a, Extended Data Fig. 2 ). Upon herbicide induction, SynCom YKB significantly up-regulated pathways related to herbicide degradation, quorum sensing (QS), tryptophan metabolism, and carbohydrate metabolism, while SynCom HKB primarily enhanced herbicide degradation, QS, sulfur metabolism, and carbohydrate metabolism. Compared to monocultures, each strain also exhibited distinct metabolic functional enrichment when in the SynCom. In SynCom YKB, strain Y1 was mainly enriched in pathways related to carbohydrate and energy metabolism, strain K1 significantly upregulated herbicide degradation and QS, while strain B1 contributed to tryptophan metabolism and carbohydrate metabolism. Similarly, in SynCom HKB, strain H1 was primarily responsible for DNA repair and QS, strain K1 was enriched in key pathways including tryptophan metabolism, carbohydrate metabolism, and herbicide degradation; and strain B1 significantly up-regulated herbicide degradation and carbohydrate metabolism. These results suggest metabolic division of labor and synergistic interactions between the SynComs members (Fig. 1 b, Table S4–S5). To elucidate metabolic interactions we constructed protein-protein interaction (PPI) networks based on the enriched proteins of each strain. In SynCom YKB, strain Y1, K1, and B1 exhibited core functional modules centered around carbohydrate metabolism, QS, and tryptophan metabolism, respectively (Fig. S7a). Similarly, in SynCom HKB, the core modules of strains H1, K1, and B1 were primarily associated with QS, sulfur metabolism, and herbicide degradation, respectively (Fig. S7a). Network centrality analyses (degree, closeness, and betweenness) showed that strains Y1 and H1 were the most central nodes in their respective SynComs, indicating that they played the largest role in shaping overall network complexity. (Fig. S7b). In the two SynComs, we further analyzed the enriched protein sets associated with strains K1 and B1. Strain K1 showed strong overlap in differentially enriched proteins across both systems, while strain B1 displayed marked divergence, indicating functional plasticity and context-dependent roles of B1 in different symbiotic environments. Additionally, we employed weighted gene co-expression network analysis (WGCNA) to identify key modules significantly correlated with herbicide degradation rates (Fig. S8). In SynCom YKB, three key modules were detected, among them, the MEblue module was significantly enriched with proteins involved in QS, herbicide degradation, and tryptophan metabolism (Fig. S8). In SynCom HKB, four key modules were identified: MEblue was enriched in proteins related to QS and herbicide degradation, while MEbrown was predominantly associated with sulfur metabolism (Fig. S8). Finally, RT-qPCR was employed to validate the expression levels of genes encoding key proteins under different treatment conditions. The results demonstrated that the expression levels were significantly greater in the SynComs than in monocultures, which was consistent with the proteomics analysis (Extended Data Fig. 3). QS-mediated regulation of microbial interactions and metabolic functions To further elucidate the interaction patterns and regulatory mechanisms within each SynCom, we quantified 6 key metabolites under different treatments: biofilm (extracellular polysaccharides, EPS), energy molecules (ATP and NADH), succinate (SA), tryptophan (trp), and a sulfur cycle metabolite (taurine, tau). Compared to monocultures, the SynComs exhibited significantly greater accumulation of all 6 metabolites during herbicide degradation (Fig. 2 a and 2 b), but the SynComs showed differential accumulation patterns, e.g. SynCom YKB accumulated more biofilm, SA, and trp, whereas SynCom HKB accumulated more tau. Furthermore, we quantified the levels of QS signal molecules across different systems. The results revealed that, compared to the monocultures or herbicides-free control, N-hexanoyl-L-homoserine lactone (C6-HSL) consistently accumulated at elevated levels in SynCom YKB, whereas high concentrations of (S)-4,5-dihydroxy-2,3-pentanedione (DPD/AI-2) were consistently detected in SynCom HKB. Therefore, we postulate that C6-HSL and AI-2 serve as the key QS signal molecules in their respective SynComs (Fig. 2 c). To determine if QS might play a role in stimulating metabolic activity in the SynComs, we supplemented the SynComs with C6-HSL and DPD/AI-2. This exogenous supplementation significantly enhanced both herbicide degradation efficiency and tolerance in both monocultures and each SynCom (Fig. 2 d). Furthermore, the addition of C6-HSL and DPD/AI-2 promoted the accumulation of biofilm, ATP, NADH, SA, trp, and tau, indicating stimulation of overall cellular metabolism (Fig. 2 d). To validate these findings, we used RT-qPCR to quantify the effect of adding the C6-HSL, DPD/AI-2, SA, trp and tau on the expression levels of the proteins in related metabolic pathways. The results demonstrated that C6-HSL and DPD/AI-2 markedly upregulated the expression of key proteins (Fig. 2 e). Specifically, C6-HSL significantly enhanced the expression of key enzymes in pathways associated with herbicide degradation, AHL signaling, biofilm formation, carbohydrate metabolism, and tryptophan metabolism in SynCom YKB (Fig. 2 e). Interestingly in SynCom HKB, it was DPD/AI-2 and not C6-HSL that promoted the expression of enzymes in herbicide-degradation and carbohydrate metabolism, as well as the AI-2 signaling pathway and sulfur metabolism (Fig. 2 e). This suggests that each SynCom has a unique QS regulatory system, with SynCom YKB primarily being induced by AHL-type signals, and SynCom HKB mainly regulated by AI-2-type signals. As expected, when we inhibited the key enzymes involved in herbicide degradation, the degradation capacity within each SynCom was significantly reduced (Fig. S9, Table S6, S7). This impact was most significant for those herbicide degradation pathways in strain K1 for SynCom YKB, and strain B1 for SynCom HKB. We conducted an in-depth analysis of the impact of inhibiting the expression of key proteins essential for target metabolic functions on both herbicide degradation and core metabolic activities within each SynCom. inhibition of key genes in the QS pathways, specifically the AHL and AI-2 pathways, generally led to significant suppression of herbicide degradation in each SynCom. In validation of the importance of different QS pathways in each SynCom, inhibition of the AHL pathway had a more substantial effect on SynCom YKB, while AI-2 inhibition had a bigger impact for SynCom HKB (Table S8). Moreover, inhibition of the QS pathways markedly suppressed downstream metabolic processes including biofilm formation, carbohydrate metabolism, energy metabolism, tryptophan, and sulfur metabolism, as measured by enzyme expression and metabolite concentration (Fig. S10, Table S8). To validate this importance, we added key metabolites (C6-HSL, DPD/AI-2, SA, trp, or tau) back to strains with inhibited pathways. The addition of C6-HSL and DPD/AI-2 significantly rescued the herbicide degradation capacity in all inhibited strains. In contrast, the addition of other small metabolites (SA, trp, tau) only restored degradation capability in their respective target RNAi strains (Fig. S11). Furthermore, the application of C6-HSL and DPD/AI-2 also enhanced the accumulation of key downstream metabolites within the RNAi systems, with as expected based on the results above, differential restorative effects of C6-HSL and AI-2 on SynCom YKB and HKB, respectively (Extended Data Fig. 4 ). Collectively, these findings underscore the critical regulatory role of QS signaling molecules in the SynComs. Given the inherent cytotoxicity of herbicides, we assessed the levels of cellular toxicity during the degradation process by measuring intracellular β -galactosidase activity (Fig. S12). Upon herbicide addition to the SynComs, high levels of cytotoxicity were observed within the first 12–24 hours, indicating the accumulation of both the parent herbicide and its intermediate metabolites (Fig. S12). As degradation progressed, the cytotoxicity gradually decreased and returned to baseline levels, demonstrating rapid and complete herbicides breakdown (Fig. S12). In contrast to the wild-type strains, the RNAi strains consistently exhibited sustained high cytotoxicity throughout the degradation process (Fig. S12). Among them, the inhibition of AHLs showed the greatest cytotoxicity for SynCom YKB while inhibition of AI-2 and sulfur metabolism had the greatest cytotoxicity for SynCom HKB. Metabolomics reveals QS-regulated metabolic dynamics To further elucidate the differences in interaction patterns of QS-regulation, we conducted untargeted metabolomic analyses on each SynCom with and without the inhibition of the key metabolic pathways (QS, tryptophan metabolism, and sulfur metabolism). Significant alterations in metabolites were observed across all inhibited SynComs compared to the wild-type (Fig. S13). Among them, YKB-QSR2 ( lux I RNAi), YKB-TMR2 ( hpa BA RNAi), HKB-QSR1( lux S RNAi), and HKB-SMR2 ( cys D RNAi) exhibited the greatest number of significantly differentially expressed metabolites (DEMs) (Fig. S13). KEGG categorization revealed that DEMs in interfered systems were predominantly enriched in lipid metabolism, amino acid metabolism, and biosynthesis of other secondary metabolites. Specifically, QS-inhibited systems in SynCom YKB showed greater enrichment of metabolites related to membrane transport, signaling, and cellular community (which refers to genes/proteins associated with cell–cell interactions, junctions, or multicellular structures; Fig. S14). Similarly, QS-inhibited systems in SynCom HKB were notably enriched in signaling molecules and interactions, as well as signal transduction (Fig. S15). We focused on significantly down-regulated differential metabolites (DDEMs) in the inhibited systems to uncover correlations underlying functional inhibition. KEGG enrichment analysis indicated that in SynCom YKB, disruption of QS pathways markedly suppressed tryptophan metabolism, purine metabolism, sphingolipid metabolism, sphingolipid signaling, and herbicide degradation pathways, whereas interference with tryptophan metabolism had minimal impact on overall metabolic activity (Fig. S16). In SynCom HKB, QS disruption significantly reduced sulfur metabolism, glycerophospholipid metabolism, amino acid metabolism, nucleotide metabolism, and benzoxazinoid biosynthesis pathways. Metabolic network analysis of DDEMs revealed higher network complexity in YKB-QSR2, HKB-QSR1 and HKB-SMR2, indicating substantial metabolic differences between SynComs upon interference with different QS signaling pathways (Fig. S17 and S18). To further evaluate the functional importance of DDEMs across treatment groups, we performed KEGG topology analysis (Extended Data Fig. 5 a). In SynCom YKB, tryptophan metabolism, atrazine degradation, and amino acid metabolism consistently played central roles. Specifically, within YKB-QSR2, aminobenzoate degradation and ABC transporters emerged as key hubs among down-regulated pathways. Compared to YKB-TMRNAi system, the AHLs-interfered system had enrichment of tryptophan metabolism and higher relative importance of key DDEMs. In SynCom HKB, atrazine degradation, sulfur metabolism, taurine and hypotaurine metabolism, and carbon metabolism were consistently prominent. In HKB-QSR2, cAMP signaling, nucleotide metabolism, and amino acid metabolism also served as critical metabolic functions. Relative to HKB-SMRNAi, AI-2 interference led to higher functional importance of suppressed metabolites within sulfur metabolism. These results further support our prior data showing that inhibition of AHL signaling in SynCom YKB and AI-2 signaling in SynCom HKB significantly inhibits overall metabolic activity and regulates multidimensional functions including herbicide degradation, amino acid metabolism, carbon metabolism, and transport. We also integrated compound classifications and enrichment levels of DEMs using metabolite set enrichment analysis (MSEA). Results revealed that DEMs included abundant herbicide intermediates and secondary metabolites, such as diazines, benzofurans, benzenes, and heteroaromatic compounds, further confirming severely inhibited herbicide degradation in interfered systems (Extended Data Fig. 5 b). Additionally, YKB-QSR2 and HKB-QSR1 exhibited the greatest DEM enrichment levels, indicating that interference in these metabolic functions most strongly suppresses herbicide degradation. SynCom application influences natural soil metabolism. A maize pot experiment was conducted to validate the metabolic impact of each SynCom in field-derived soils. Compared to the control group, both SynCom YKB and SynCom HKB significantly promoted maize growth (Fig. 3a, b). Specifically, root length increased by 90.57% and 92.24%, root fresh weight by 102.69% and 66.84%, shoot length by 76.85% and 77.46%, and shoot fresh weight by 249.56% and 219.31%, respectively (Fig. 3a). The constituent strains of each SynCom exhibited stable growth in the black soil substrate, with both communities sustaining a biomass of 10⁵ colony-forming units (CFU) 21 days after inoculation even in the presence of an endogenous microbiome (Fig. S19). All inhibited systems exhibited reduced plant-growth-promoting effects, with the most pronounced suppression observed in YKB-QSR2 and HKB-QSR1, highlighting the importance of QS for plant growth promotion. Compared to SynCom YKB, YKB-QSR2 inhibited root length, root weight, shoot length, and shoot fresh weight by 24.73%, 52.28%, 35.59%, and 66.08%, respectively. Similarly, compared to SynCom HKB, HKB-QSR1 reduced these parameters by 40.14%, 48.43%, 34.70%, and 60.19%, respectively. Moreover, both SynCom YKB and SynCom HKB significantly enhanced the activity of nutrient metabolism-related enzymes in the rhizosphere soil, improved root vitality, increased the photosynthetic rate in leaves, and elevated the content of nutritional components, further confirming their pronounced beneficial effects on maize seedling growth (Fig. 3b, c, S20). Additionally, throughout the maize growth period, both SynCom YKB and SynCom HKB efficiently degraded the endogenous herbicide pollution in these field-derived soils, with a degradation rate consistently exceeding 95%. In contrast, the herbicide degradation capacity was significantly impaired in the YKB-QSR2 and HKB-QSR1 systems, which achieved only approximately 40% degradation (Fig. 3d). Metagenomics reveals the regulatory mechanisms of SynCom metabolism in field-derived soils To elucidate the regulatory functions and metabolic division of labor of the SynComs in a field-derived soil environment, we conducted metagenomic analyses. Compared to the control group, SynCom application increased taxonomic and functional α -diversity of the soil microbiome (Extended Data Fig. 6 a). Although this increase was partially attenuated upon the introduction of inhibited strains, it still remained greater than that of the control. β -diversity analysis revealed clear differences in species composition and functional profiles among treatment groups, with relatively smaller differences observed between YKB and YKB-QSR2, and between HKB and HKB-QSR1 (Extended Data Fig. 6 b). Meanwhile, the normalized stochasticity ratio (NST) analysis indicated that the introduction of the SynComs shifted the bacterial community assembly from stochastic processes toward deterministic processes (Extended Data Fig. 6 c), suggesting that environmental filtering and ecological selection became the dominant forces governing community structure and stability. Furthermore, as expected, due to the membership of the assemblages, the addition of SynCom YKB significantly increased the relative abundance of Pseudomonas , Comamonas , and Acinetobacter , while SynCom HKB enrichment led to an enrichment of Pseudomonas , Bacillus , and Acinetobacter . These trends are highly consistent with the taxonomic composition of the introduced SynComs, providing further evidence for their successful colonization (Extended Data Fig. 6 d). KEGG enrichment analysis of annotated metabolic functions across treatment groups demonstrated that the application of both SynCom YKB and HKB significantly increased the relative abundance of functional genes related to herbicide degradation, QS, carbohydrate and energy metabolism, tryptophan metabolism, and sulfur metabolism compared to the control, which were consistent with earlier findings (Fig. 4 a). In contrast, these key pathways were significantly down-regulated in the inhibited systems compared to the wild-type SynComs, underscoring the inhibitory effect of metabolic disruption on overall regulatory function. We further evaluated the relative contributions of bacterial taxa to these key metabolic pathways. The results indicated functional partitioning among microbial members; for example, in SynCom YKB-treated soils, Comamonas , Acinetobacter , and Pseudomonas collectively played key roles in QS, herbicide degradation, and tryptophan metabolism, while Comamonas and Pseudomonas together contributed prominently to carbohydrate and energy metabolism. In SynCom HKB-treated soils, Pseudomonas and Bacillus were crucial for QS and herbicides degradation, whereas genus of Bacillus and Acinetobacter jointly dominated carbohydrate and energy metabolism. All three genera contributed significantly to central carbon and energy metabolic pathways (Fig. 4 a). To validate these metagenomic insights, the accumulation of key metabolites in the soil were quantified, and the abundance of key herbicide degradation proteins and QS signal molecules were quantified (Fig. 4 b, c, S21). The results demonstrated that both SynComs promoted the abundance of key herbicide degradation genes and QS signal molecules, while promoting soil accumulation of SA. Additionally, SynCom YKB enhanced tryptophan accumulation, while SynCom HKB increased taurine levels. In QS-interfered systems, the levels of these key metabolites and signal molecules were significantly reduced, confirming that QS-mediated communication underpins the metabolic coordination within the SynComs. Correlation analyses were employed to investigate the relationships between soil ecological changes and bacterial taxonomic or functional characteristics. First, a significant positive correlation was observed between the concentration of herbicides in the soil, growth of maize and the taxonomic and functional microbial β -diversity, indicating consistency between functional potential and phenotypic outcomes (Fig. S22 and S23). Mantel tests revealed a significant negative correlation between metagenomic profiles of the control group and maize growth phenotypes as well as soil enzyme activities, whereas the treatment groups showed the opposite trend (Fig. S24a). Furthermore, variance partitioning analysis (VPA) confirmed that the selected environmental factors explained 30.84% and 86.37% of the variance in soil species composition and KEGG functions, respectively, underscoring a stronger deterministic and explanatory influence of environmental factors on functional traits (Fig. S24b). WGCNA was also used to identify key functional modules significantly associated with treatments and environmental factors, and yielding four major modules. MEyellow was enriched for biofilm formation and tryptophan metabolism, MEblue for QS and two-component systems, MEgreen for carbon metabolism, herbicide degradation, and signal transduction, and MEred for sulfur metabolism and glycoside metabolism (Extended Data Fig. 7 ). The influence of soil species and specific functions on environmental factors was further analyzed. The results indicated that the relative abundances of genus Comamonas , Pseudomonas , Acinetobacter , Bacillus , Enterobacter , and Sphingomonas were significantly positively correlated with soil environmental factor levels, suggesting that the SynComs collaboratively modulate soil ecological functions and maize growth alongside certain indigenous microorganisms. In contrast, the relative abundance of some native taxa, such as genus Cupriavidus , showed a significant negative correlation with environmental factors, potentially indicating competitive or antagonistic interactions (Extended Data Fig. 8). Metagenome-Assembled Genomes (MAGs) reflect functional partitioning and consortia stability Metagenomic data from the SynCom YKB and HKB treatment groups were assembled and binned, yielding 28 MAGs (Extended Data Fig. 9). KEGG annotation identified four MAGs (MAG12, MAG46, MAG57, and MAG82) with over 99% genomic similarity to the SynCom strains Y1, K1, B1, and H1, respectively (Fig. 5 a). Analysis of MAG proportions revealed that the SynCom YKB treatment group was enriched with MAG12, MAG46, and MAG57, while the HKB group was enriched with MAG46, MAG57, and MAG82, which was consistent with the taxonomic makeup of the respective SynComs (Fig. 5 a). Moreover, the relative abundance of these four target MAGs was significantly positively correlated with soil environmental factors, further supporting their putative key roles in regulating soil functionality. KEGG functional annotation of the four MAGs revealed substantial involvement in signal transduction, transcription, amino acid metabolism, energy metabolism, and carbohydrate metabolism (Fig. 5 b). Specifically, MAG12 was most prominently associated with signal transduction and energy production/conversion; MAG46 primarily regulated membrane biogenesis and amino acid metabolism; MAG57 was responsible for amino acid metabolism and carbohydrate transport/metabolism; while MAG82 dominated signal transduction and DNA repair, which illustrated a clear functional partitioning and cooperation among members of the different SynComs. Discussion SynComs represent a promising strategy for the comprehensive utilization of beneficial microorganisms in sustainable agriculture. Numerous studies have demonstrated the significant potential of SynComs in the bioremediation of pollutants, with different consortia exhibiting distinct functional traits and degradation mechanisms 49 – 52 . For instance, a synthetic co-culture of Pseudomonas sp. ADP and Arthrobacter sp. degraded 95% of atrazine at an initial concentration of 100 mg/L within just 7 days under laboratory conditions 53 . However, most currently constructed SynComs only address the remediation of single herbicide types, overlooking the practical issue of combined herbicide contamination 54 , 55 . Moreover, it is essential to investigate the interactions among SynCom members and elucidate their regulatory roles in natural ecosystems to clarify communication and division of labor, thereby addressing common application challenges such as insufficient synergy or functional failure 56 . Our previous work demonstrated that SynComs could simultaneously degrade multiple herbicides while enhancing soil microbial diversity and carbon metabolism 11 . The present study builds on that foundation by uncovering the regulatory basis of this cooperative activity, showing that distinct quorum-sensing systems can orchestrate division of labor in different SynComs. This shift from functional demonstration to mechanistic understanding provides a framework for more rational design of synthetic communities for bioremediation. We constructed two SynComs, YKB and HKB, by a top-down approach. Guided by the Design-Build-Test-Learn (DBTL) framework, we employed multi-omics analyses, cross-validation, and pot experiments to reveal interaction models and regulatory mechanisms mediated by QS signaling and facilitated by downstream metabolic pathways. We first uncovered the metabolic division of labor among members within SynCom YKB and SynCom HKB. In SynCom YKB, strain K1 serves as the primary degrader of herbicides, strain Y1 acts as an energy supplier, while strain B1 functions as a “clean-up” agent that further metabolizes intermediate products generated by strain K1 via tryptophan metabolism, ultimately yielding acetyl-CoA, which enters the TCA cycle to supply additional energy to the system. Similarly, in SynCom HKB, strain H1 mitigates cytotoxicity through DNA repair, while strains K1 and B1 collaboratively perform the main herbicide degradation tasks. Additionally, strains H1 and B1 activate their sulfur metabolism pathways, forming an electron transport chain linked to herbicides degradation that promotes and regulates redox processes while supplying energy (Fig. 6 a). Notably, we demonstrated that the same microbial strain can perform significantly different metabolic roles across different SynComs systems. Herbicides degradation and resistance assays revealed that SynCom YKB exhibited simultaneous enhancement in both degradation efficiency and resistance, whereas resistance in SynCom HKB did not increase alongside its degradation capacity. This observation led us to hypothesize that distinct regulatory mechanisms might govern community interactions and degradation processes in the two systems. Proteomic analysis of differentially expressed proteins in each member supported this hypothesis: strains indeed activate different interaction patterns depending on their consortium context. While strain K1 showed minimal functional variation between the two systems, serving consistently as a primary degrader, strain B1 displayed remarkable functional divergence. In the SynCom YKB system, it acted as a helper strain, facilitating downstream degradation and tryptophan metabolism. However, in the HKB system it functioned as a key degrader, primarily mediating herbicides breakdown and sulfur metabolism. Previous studies indicate that functional adaptation of strains within SynComs is influenced by environmental factors, microbial interactions, and signaling molecules 57 – 59 . Thus, we proposed that the observed functional divergence of strain B1 may be attributed to: 1. Different signaling molecules mediating inter-strain interactions in the two systems, leading to early divergence in metabolic division of labor; 2. In SynCom YKB, lower herbicides-induced cytotoxicity allows strain K1, supported energetically by strain Y1, to efficiently initiate degradation. Consequently, strain B1 only need to undertake the downstream processing within a stable partnership; 3. In SynCom HKB, higher cytotoxicity necessitates rapid joint degradation by both strain B1 and K1 to mitigate systemic damage. We elucidated the regulatory role of QS in mediating interactions within the two SynComs. Exogenous metabolite complementation demonstrated that QS signal molecules enhance both the expression of key enzymes involved in herbicides degradation and the overall degradation capability of the SynComs. Notably, QS signal molecules also up-regulate key genes in downstream metabolic pathways and modulate the accumulation of critical metabolites. Furthermore, interference with QS signaling resulted in the most pronounced suppression of both degradation function and downstream metabolic activities, underscoring the essential role of QS in establishing microbial interactions, regulating metabolic division of labor, and maintaining functional stability. QS signal molecules accumulate with increasing bacterial density and, upon reaching a threshold concentration, trigger intracellular signal transduction via ligand-receptor interactions, ultimately leading to the activation or repression of specific genes 36 , 60 . Numerous studies focusing on bacterial isolates have documented QS-mediated behaviors—such as coordinated population activities, biofilm formation, regulation of virulence and pathogenicity, and control of growth and metabolism—providing compelling evidence for the role of QS in bacterial collective behavior 61 – 64 . Consistent with previous reports, SynComs in this study exhibited significant up-regulation of QS-related pathways compared to axenic cultures, directly correlating with enhanced metabolic and degradative functions. Prior research also has confirmed that QS signaling regulates microbial biodegradation of pollutants. For instance, several studies have leveraged AHLs-mediated QS systems to improve pollutant removal in wastewater treatment processes 65 . Hu et al. reported that the addition of 5 nM AHLs significantly enhanced biofilm activity in a sequencing biofilm batch reactor (SBBR), increasing COD removal by approximately 7% 47 . Valle et al. demonstrated that AHLs promote the abundance of phenol-degrading bacteria in wastewater 40 , and Zhou et al. found that QS facilitates bacterial degradation of the antibiotic florfenicol 48 . More importantly, our study reveals distinct QS regulatory patterns between the two SynComs: in SynCom YKB, AHLs-based signaling predominates, whereas AI-2 plays a major role in regulating metabolic functions in SynCom HKB. AHLs are among the most common autoinducers (AIs), produced by over 100 species of Proteobacteria , including numerous Gram-negative bacteria, and are centrally governed by the LuxI/LuxR system 66 . They are widely reported to regulate intraspecies communication, biofilm morphogenesis, EPS production, and bacterial activities 67 . Meanwhile, AI-2 operates in the QS systems of both Gram-positive and Gram-negative bacteria (over 60 species) and is considered pivotal in inter-species communication 68 , 69 . The lux S gene is recognized as the hallmark AI-2 synthase. Structural analyses of LuxS proteins across bacteria reveal a conserved homodimeric zinc-binding metalloprotein structure, with conserved residues His54, His58, and Cys126 forming the active site 69 . This structural conservation suggests that AI-2 molecules may be similarly recognized across species 70 . Accordingly, as all three members of SynCom YKB are Gram-negative and encode intact LuxI/LuxR systems, AHL signaling dominates QS regulation in this system. While in SynCom HKB, which includes both Gram-positive (Genus of Bacillus ) and Gram-negative strains, AI-2 is highly activated and facilitates inter-strain communication. Interestingly, this divergence in QS signals suggests distinct communication patterns: in SynCom YKB, intra-species signaling may prevail, where metabolic division of labor is maintained via feedback through secreted metabolites; whereas in SynCom HKB, robust inter-species signaling enables high-level communication that coordinates differential metabolic expression among members as cell density increases. QS not only regulates the microbial degradation of herbicides but also activates distinct downstream metabolic pathways that facilitate the further breakdown of herbicide-derived intermediates. Metabolomic analyses revealed that inhibition of QS signaling significantly down-regulated the activity of multiple downstream pathways, including amino acid metabolism, sulfur metabolism, carbon metabolism, and nucleotide metabolism, underscoring the central regulatory role of QS in the metabolic function of the microbial consortium. In SynCom YKB, disruption of the AHLs-mediated QS system led to a loss of activity in the tryptophan metabolic pathway, whereas supplementation with exogenous C6-HSL, a key signaling molecules of AHLs, restored the accumulation of key metabolites in this pathway, confirming the positive regulatory role of AHLs in tryptophan metabolism. Previous studies have shown that the tryptophan metabolic pathway mediates the complete degradation of organic pollutants such as herbicides 71 . Tryptophan metabolism involves multiple hydrolytic-oxidoreductive reactions (e.g., the iaa pathway) and decarboxylative degradation routes (e.g., the iac pathway), serving as a hub for the biodegradation of catechol, benzoate, aminobenzoate, and nicotinamide metabolism 72 . This process facilitates the complete conversion of herbicide intermediates into acetyl-CoA. Consequently, tryptophan metabolism not only promotes thorough herbicide degradation but also generates additional acetyl-CoA, which feeds into glycolysis, pyruvate metabolism, and the TCA cycle to supply energy to the microbial community 73 , 74 . Meanwhile, in SynCom HKB, AI-2-mediated inter-species communication significantly enhanced sulfur metabolic activity. Similarly, QS interference resulted in the loss of downstream metabolic function, which was restored upon AI-2 supplementation. Numerous studies have established a close relationship between sulfur metabolism and herbicide degradation , primarily manifested in the following aspects: 1. Involvement of key sulfur metabolism genes. For example, the sulfur metabolism-related gene CysJ has been demonstrated to participate in the degradation of chlorimuron-ethyl by Chenggangzhangella methylocysteaceae , suggesting that sulfur metabolism may supply energy or intermediate products that assist in herbicide breakdown 75 . 2. Sulfate generation may alter soil chemical properties, influencing herbicide adsorption, mobility, or microbial activity, thereby indirectly promoting or inhibiting herbicide degradation 76 . 3. The sulfate reduction pathway can form a complete electron transport chain with herbicide redox reactions, thereby accelerating herbicide degradation 77 . In summary, within SynCom HKB, AI-2-mediated up-regulation of sulfur metabolism promotes herbicides degradation and generates substantial bioactive compounds such as acetyl-CoA. This study also provided a deeper understanding of SynCom YKB and SynCom HKB on regulatory mechanisms within the black soil ecosystem. The SynComs significantly enhanced maize growth and promoted both taxonomic and functional diversity in the soil, likely attributable to reduced herbicides toxicity and improved soil ecological conditions (Fig. 6 b). The shift in bacterial community assembly toward deterministic processes following inoculation further confirms that SynComs are key drivers of soil microbial structure and function. Notably, such beneficial effects were markedly inhibited when the key metabolic pathways described above were disrupted. In particular, interference with QS severely impaired the functionality in the black soil ecosystem, with AHLs and AI-2 playing predominant roles in SynCom YKB and SynCom HKB, respectively. QS not only regulated interactions within the SynComs but also plays a central role in mediating interactions between the inoculants and indigenous soil microorganisms. Metagenomic analyses revealed that the SynComs enhanced key soil metabolic functions, including herbicides degradation, carbohydrate and energy metabolism, and QS. Different members occupied distinct ecological niches across key functions, highlighting the division of labor and communication within the constructed communities. Compared to single-strain applications, the SynComs exhibited greater ecological stability and adaptive capacity to changing environmental and physicochemical conditions 78 , 79 . Furthermore, MAGs further elucidated the functional partitioning among members in the natural soil environment. Consistent with in vitro findings, all four strains significantly activated signal transduction pathways. Strain Y1 primarily functioned as an energy producer and transporter, strains K1 and B1 were responsible for organic compound metabolism and transfer, and strain H1 dominated DNA repair and carbon metabolism activities. It is precisely the well-coordinated division of labor and functional complementarity that underpins the stable interactions and highly efficient degradation/regulatory performance of the SynComs in the natural ecosystem. Methods Media, primers, and chemical standards A 1/10 LB medium was used throughout this study for bacterial isolation/screening, strain cultivation, and functional validation. The medium contained 1 g tryptone, 0.5 g yeast extract, and 1 g NaCl per liter, and adjusted to pH 7.2. For solid plates, 18 g/L agar was added. All media were sterilized by autoclaving at 121°C for 15 minutes. The primers used in this study are listed in Table S9. Analytical standards of atrazine (purity > 98%), acetochlor (purity > 99%), butachlor (purity > 98%), metolachlor (purity > 98%), metribuzin (purity > 98%), nicosulfuron (purity > 98%), pyrazosulfuron (purity > 98%), and fomesafen (purity > 98%) (Table S10) were purchased from the Pesticide Research Institute (Shanghai, China). Stock solutions of each herbicide (10 g/L) were prepared in HPLC-grade methanol. All other chemicals were of analytical grade and obtained from Scrbio Biotechnology Co., Ltd. (Shanghai, China). Soil sampling and strain enrichment Six sampling sites were selected within the typical black soil region of Northeast China, covering Liaoning, Jilin, and Heilongjiang provinces. Sites included Changtu (CT), Kuancheng (KC), Acheng (AC), Beilin (BL), Hailun (HL), and Beian (BA) (Table S2). Soil sampling and basic climatic information for the black soil were listed in the supplementary materials. For strain enrichment, 10 g of soil was inoculated into 100 mL of sterile water supplemented with 50 mg/L each herbicides, and incubated at 28°C for 5 days. Enriched microbial communities were isolated using the dilution plate count method. Soil suspensions were allowed to settle for 15 minutes at room temperature, then serially diluted from 10⁻¹ to 10⁻⁶ using sterile water. Aliquots (100 µL) of dilutions between 10⁻³ and 10⁻⁶ were spread onto 1/10 LB agar plates. Each dilution was plated in triplicate. Plates were incubated inverted at 30°C and monitored regularly for colony formation. Isolated colonies were purified by streaking onto fresh solid medium. Strain identification was based on morphological characteristics (For details, see the supplementary materials) and 16S rRNA gene sequencing. Genomic DNA was extracted using the method described by Marmur (1961) 80 . The 16S rRNA gene was amplified with universal primers 27F and 1492R 81 and sequenced by Tsingke Biotechnology (Beijing, China). Sequences were analyzed using the GenBank BLAST program ( http://www.ncbi.nlm.nih.gov/BLAST ). A neighbor-joining (NJ) phylogenetic tree was constructed, and branch stability was assessed using bootstrap analysis with 1,000 replicates 82 . The pure culture strains were stored frozen at -80°C with glycerol (25%, w/v) as preservative. Validation of strain resistance and degradation capacity To assess herbicides resistance, various concentrations of herbicides were added to 1/10 LB medium, mixed thoroughly, and poured into plates. A 10 µL aliquot of bacterial suspension was spotted onto the plates, which were then incubated inverted at 28°C for 2 days. Bacterial growth was observed and recorded, and the maximum herbicides concentration allowing normal growth was determined as the minimum inhibitory concentration (MIC) 83 . For SynComs, strains were mixed in equal volumes, and 10 µL of the mixture was used for resistance testing. HPLC-MS was employed to determine the content of herbicides, with specific operational details provided in the supplementary materials 11 . Single herbicide degradation assay Strains were pre-cultured overnight in 5 mL of 1/10 LB medium at 28°C with shaking at 150 rpm. Then, 1 mL of the culture (OD 600 = 0.8) was inoculated into 100 mL of 1/10 LB medium containing 100 mg/L of individual herbicides and incubated for 60 h. For SynComs, a 1% inoculum (v/v) consisting of equal proportions of each strain was used. Mixed herbicide degradation assay A mixture of eight herbicides was prepared at final concentrations of 50, 100, 150, and 200 mg/L each in 100 mL of 1/10 LB medium. Then, 1 mL of pre-cultured seed culture (OD 600 = 0.8) was inoculated into the mixed herbicide medium and incubated at 28°C with shaking at 150 rpm for 60 hours. Samples were taken every 12 hours to monitor bacterial growth and residual herbicide content. The total residual herbicide concentration was defined as the sum of the concentrations of all eight herbicides. Degradation kinetics were modeled using a first-order reaction model. Proteomic processing and analysis Strains Y1, K1, B1, and H1 were cultured individually and in co-culture in 1/10 LB medium containing 100 mg/L of mixed herbicides for 36 hours. Cells were harvested by centrifugation (12,000 rpm, 4°C), and the pellets were lyophilized. Protein extraction was performed using the Majorbio Microprotein Kit (details in the supplementary materials). Protein concentration was determined by the Bradford method. Equal amounts of peptides were dissolved in mass spectrometry loading buffer and analyzed by data-independent acquisition (DIA). Raw DIA data were processed using Spectronaut™ 19 for database searching. The specific parameters are compiled in the supplementary materials. Separate and combined protein databases of the four strains were used in this study. Functional annotation of differentially expressed proteins was conducted on the Gene Ontology (GO) database ( http://geneontology.org/ ) across three categories: biological process, cellular component, and molecular function. Metabolic pathway analysis was performed using the KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/ ) and COG (Cluster of Orthologous Groups) databases. PPI networks were constructed on STRING version 11.5. Real-time quantitative PCR and RNA interference technology Real-time quantitative PCR (RT-qPCR) was used to examine the expression levels of key genes associated with herbicides degradation, QS, biofilm synthesis, energy metabolism, tryptophan metabolism, and sulfur metabolism 71 . Strains were inoculated at a total initial concentration of 1% into 1/10 LB medium and cultured at 28°C with shaking at 150 rpm for 36 h. Cell pellets were collected for subsequent RNA extraction and RT-qPCR analysis. Total RNA was extracted using TRIzol reagent (Invitrogen) (details in the supplementary materials). The cDNA was diluted 10-fold and subjected to RT-qPCR analysis by SYBR Green real-time PCR master mix (Toyobo). Amplification was carried out on an iQ5 real-time PCR detection system (Bio-Rad, USA), and data were analyzed using the 2 −ΔΔCT method. Quantitative RT-qPCR was performed on an ABI ViiA7 instrument using 0.1 mL fast optical 96-well reaction plates (Applied Biosystems). Due to the broad antibiotic resistance profiles of strains Y1, K1, B1, and H1, RNA interference (RNAi) was employed for targeted gene silencing 71 . A strong Tac promoter 84 and gene-specific silencing fragments were cloned into the plasmid pUC19, which carries a red fluorescent protein (RFP) marker (Fig. S25) 85 . RNAi targets were designed against specific genes of interest in each strain; primers used for amplifying target genes are listed in Table S9. The target fragments were inserted into the lacZ site of the plasmid via Hind III and Sac I restriction sites. Recombinant plasmids were then introduced into the respective strains via electroporation. Wild-type strains without plasmids formed white colonies with no red fluorescence on LB plates. Strains carrying the empty vector exhibited red fluorescence and blue colonies, while RNAi strains with inserted target fragments showed white colonies with red fluorescence. The strains were iteratively cultured for five generations, and RT-qPCR confirmed stable silencing of the target genes in the RNAi strains. Phenotypic validation of RNAi strains and stability testing of gene interference are presented in Fig. S26. Detailed information on all constructed interference strains is summarized in Table S6. Detection of key metabolites and reporter gene assays Biofilm content was determined by the method of crystal violet staining, with absorbance measured at a wavelength of 595 nm (details in the supplementary materials) 71 . ATP and NADH were analyzed using a high-performance liquid chromatography system (HPLC, 2690 series, Waters, USA) with a mobile phase consisting of 90% 50 mM phosphate buffer, 10% acetonitrile, and 3.22 g/L tetrabutylammonium bromide (pH 6.8), with a flow rate of 1 .0 mL/min 86 . SA, trp, and tau were also measured via HPLC under the following conditions 87 : 1. SA: Mobile phase—acetonitrile : deionized water : acetic acid (25:75:5, v/v); flow rate: 1.0 mL/min; detection wavelength: 303 nm. 2. Tryptophan: Mobile phase—acetonitrile : phosphate buffer (87:13); flow rate: 1.0 mL/min; detection wavelength: 280 nm. 3. Taurine: Mobile phase—0.05 mol/L potassium dihydrogen phosphate solution (pH adjusted with phosphoric acid) : methanol (1:1); flow rate: 1.0 mL/min; detection wavelength: 240 nm. Reporter gene construction and β -Galactosidase assay: The promoter-recA sequence was amplified by PCR and cloned into the EcoRI-BamHI site of the plasmid pLSP-kt2lacZ, resulting in the recombinant plasmid pLSP-promoter-recA. The construct was verified by PCR and sequencing (Fig. S27) 86 . The plasmid was then transformed into the target strains, which were cultured in 1/10 LB medium. β -Galactosidase activity was measured every 12 hours across different experimental systems. Metabolomic analysis Wild-type and RNAi strains of Y1, K1, B1, and H1 were co-cultured in 1/10 LB for 48 h. Then, 100 µL of culture was transferred to a 1.5 mL centrifuge tube, mixed with 400 µL of extraction solvent (acetonitrile:methanol = 1:1, v/v) containing four internal standards (including L-2-chlorophenylalanine at 0.02 mg/mL), and vortexed for 30 seconds. Metabolomics preprocessing and specific parameters are compiled in the supplementary materials 71 , 87 . Raw data were processed using the ropls package (v1.6.2) for multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). A 7-round cross-validation was applied to evaluate model robustness. Significantly altered metabolites were identified based on a variable importance in projection (VIP) value > 1 from the OPLS-DA model and a p-value < 0.05 from Student’s t-test. Metabolic pathway annotation of differential metabolites was performed using the KEGG database ( https://www.kegg.jp/kegg/pathway.html ). Pathway enrichment analysis was conducted using the scipy.stats package in Python, and the most relevant biological pathways were identified via Fisher’s exact test. Maize pot experiment To distinguish the target strains from indigenous microorganisms, recombinant strains labeled with red fluorescent protein (RFP) were constructed. The plasmid pTn7-RFP was kindly provided by Dr. Tim Tolker-Nielsen (Fig. S28). Transformants of Y1::pTn7RFP, K1::pTn7RFP, B1::pTn7RFP, and H1::pTn7RFP were selected using LB agar containing 50 µg/mL ampicillin 87 . After incubation at 28°C for 2 days, fluorescent colonies were visualized using a ChemiDoc MP imaging system. Each target strain was inoculated at 1% (v/v) into 1/10 LB liquid medium and cultured at 28°C for 36 h. Cells were harvested by centrifugation (5,000 rpm, 20 minutes) and resuspended in sterile water to an OD 600= 0.5. Maize seeds were surface-sterilized by soaking in sterile water for 20 h, treated with 75% ethanol for 30 seconds, followed by 1% sodium hypochlorite for 30 min, and rinsed 4–5 times with distilled water. Seeds were germinated in vermiculite, and after 7 days, uniformly sized seedlings were selected and transplanted into black soil 88 . The pot experiment used air-dried and sieved black soil collected from the Modern Agricultural Demonstration Base in Minzhu Township, Harbin. Basic physicochemical properties of the soil are provided in Table S11. Each plastic pot (11 cm in diameter) was filled with 1.0 kg of soil and watered to maintain 60% of the field water holding capacity. After equilibration for one day, one maize seedling was transplanted per pot. Plants were grown under controlled conditions: 25°C, with a photoperiod of 14 h of light and 10 h of darkness. The light intensity during the photoperiod was 600 µmol photons·m⁻²·s⁻¹ 89 . The experimental treatments were as follows: control (CK, receiving sterile water only), synthetic consortium groups (SynCom YKB and SynCom HKB), and interference strain groups (YKB-QSR1, YKB-QSR2, YKB-TMR1, YKB-TMR2, HKB-QSR1, HKB-QSR2, HKB-SMR1, HKB-SMR2). Each treatment included four replicates. A total of 15 mL of bacterial suspension was applied per pot (5 mL per strain in consortia treatments). Plants were irrigated with 250 mL of water every 5 days. Sampling was conducted 21 days after inoculation. The specific testing methods for maize growth-related indicators and soil enzymes were compiled in the supplementary materials. Metagenomic Analysis Total DNA from soil samples was subjected to whole-genome sequencing with three biological replicates per condition (details in the supplementary materials). Raw sequencing reads were quality-controlled and assembled using FASTp (v0.24.2) 90 and MEGAHIT (v1.1.2) 91 , respectively. Gene prediction was performed with MetaGene, and sequence clustering and alignment were carried out using CD-HIT (v4.6.1) 92 and SOAPaligner (v2.21) 93 . Contigs derived from metagenomic assembly were binned on a per-sample basis. Binning, consolidation, and refinement were conducted using MetaBAT2, MaxBin2, and CONCOCT, followed by bin refinement, quality assessment, taxonomic annotation, and selection of medium-quality MAGs. The amino acid sequences of the non-redundant gene catalog were aligned against the NR database using BLASTP implemented in DIAMOND (v2.0.13; e-value ≤ 1e⁻⁵). Taxonomic annotation was assigned based on the corresponding classification information from the NR database, and species abundance was estimated from the summed abundance of genes assigned to each taxon. Non-redundant gene sequences were also aligned against the KEGG, COG, and Quorum Sensing databases to annotate functional traits, KEGG orthologous pathways, EC numbers, and functional modules. Functional abundance profiles were derived from these annotations 11 . Declarations Data Availability All bacterial 16S and genome sequencing data reported in this paper were deposited in GitHub https://github.com/ZhangY306/raw-data-for-QS. All raw data analyzed in this studu were listed in the Supplementary information. Acknowledgment This work was supported by the National Key R&D Program of China (2022YFD1500202), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA28020202), the National Natural Science Foundation of China (42407176), the Jiangsu Youth Foundation (BK20241701), the Self-Deployment Program of Nanjing Soil Research Institute of Chinese Academy of Sciences (ISSAS2402), the Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB437), and the China Postdoctoral Science Foundation (2024M753333). Author Contributions H.C. and Y.Z. conceptualized and designed the research. Y.Z., L.N., M.Y.Z, P.H.,W.L., X.X., R.S., performed the research. Y.Z., X.L. and H.C analyzed the data. H.C. supervised the study. Y.Z., J.A.G., and H.C. wrote the paper. H.C., J.A.G., K.S., H.X.X., N.Z. and J.Z reviewed the paper. Competing interests The authors declare no competing interest. References Xuan F et al (2023) Mapping crop type in Northeast China during 2013–2021 using automatic sampling and tile-based image classification. Int J Appl Earth ObS 117 Fang HY, Sun LY, Qi DL, Cai QG (2012) Using 137Cs technique to quantify soil erosion and deposition rates in an agricultural catchment in the black soil region, Northeast China. Geomorphology 169–170:142–150 Xu XZ, Xu Y, Chen SC, Xu SG, Zhang HW (2010) Soil loss and conservation in the black soil region of Northeast China: a retrospective study. Environ Sci Policy 13(8):793–800 Zhang Y et al (2014) Interaction between microbes DNA and atrazine in black soil analyzed by spectroscopy. CLEAN Soil Air Water 43(6):867–871 Wang Y et al (2017) Characterization of spectral responses of dissolved organic matter (DOM) for atrazine binding during the sorption process onto black soil. Chemosphere 180:531–539 Rotich HK, Zhang ZY, Zhao YS, Li JC (2004) The adsorption behavior of three organophosphorus pesticides in peat and soil samples and their degradation in aqueous solutions at different temperatures and pH values. Int J Environm Anal Chem 84:289–301 Rama Krishna K, Philip L (2008) Adsorption and desorption characteristics of lindane, carbofuran and methyl parathion on various Indian soils. J Hazard Mater 160:559–567 Li Z et al (2023) Improved method to characterize leaf surfaces, guide adjuvant selection, and improve glyphosate efficacy. J Agric Food Chem 71(3):1348–1359 Richardson JR, Fitsanakis V, Westerink RHS, Kanthasamy AG (2019) Neurotoxicity of pesticides. Acta Neuropathol 138(3):343–362 Bast A, Semen KO, Drent M (2021) Pulmonary toxicity associated with occupational and environmental exposure to pesticides and herbicides. Curr Opin Pulm Med 27(4):278–283 Zhang Y et al (2025) SynCom-mediated herbicide degradation activates microbial carbon metabolism in soils. iMeta e70058 Rabelo JS, Santos EAD, Melo EI, Marcal G, Vieira Vaz M, Mendes GO (2023) Tolerance of microorganisms to residual herbicides found in eucalyptus plantations. Chemosphere 329:138630 Joly P, Bonnemoy F, Charvy GC, Bohatier J, Mallet C (2013) Toxicity assessment of the maize herbicides S-metolachlor, benoxacor, mesotrione and nicosulfuron, and their corresponding commercial formulations, alone and in mixtures, using the Microtoxtest. Chemosphere 93:2444–2450 Braga RR et al (2016) Effect of growing Brachiria brizantha on phytoremediation of picloram under different pH environments. Ecol Eng 94:102–106 Hagner M, Mikola J, Saloniemi I, Saikkonen K, Helander M (2019) Effects of a glyphosate-based herbicide on soil animal trophic groups and associated ecosystem functioning in a northern agricultural field. Sci Rep 9(1):8540 Gfrerer M et al (2002) Occurrence of triazines in surface and drinking water of Liaoning Province in eastern China. J Biochem Biophys Methods 53(1–3):217–228 Yu XF et al (2018) Herbicide accumulations in the Xingkai lake area and the use of restored wetland for agricultural drainage treatment. Eco Eng 120:260–265 Shi RG, Lv JG, Feng JM (2011) Assessment of pesticide pollution in suburban soil in south Shenyang, China. Bull Environ Contam Toxicol 87(5):567–573 Wang XC, Liu QL (2020) Spatial and temporal distribution characteristics of triazine herbicides in typical agricultural regions of Liaoning, China. Bull Environ Contam Toxicol 105(6):899–905 Zhu J, Fu L, Jin C, Meng Z, Yang N (2019) Study on the isolation of two atrazine-degrading bacteria and the development of a microbial agent. Microorganisms 7:80 Wahlaa AQ, Anwara S, Muellerc JA, Arslanc M, Iqbal S (2020) Immobilization of metribuzin degrading bacterial consortium MB3R on biochar enhances bioremediation of potato vegetated soil and restores bacterial community structure. J Hazard Mater 390:121493 Liu J et al (2022) Construction and comparison of synthetic microbial consortium system (SMCs) by non-living or living materials immobilization and application in acetochlor degradation. J Hazard Mater 438:129460 Caron E, Lafrance P, Auclair JC (2010) Impact of grass and grass with poplar buffer strips on atrazine and metolachlor losses in surface runoff and subsurface infiltration from agricultural plots. J Environ Qual 39:617–629 Szewczyk R, Różalska S, Mironenka J, Bernat P (2020) Atrazine biodegradation by mycoinsecticide Metarhizium robertsii : Insights into its amino acids and lipids profile. J Environ Manage 262:11030 Chang J et al (2022) Toxicological effects, environmental behaviors and remediation technologies of herbicide atrazine in soil and sediment: a comprehensive review. Chemosphere 307(Pt 3):136006 Caceres-Jensen L et al (2020) Nicosulfuron sorption kinetics and sorption/desorption on volcanic ash-derived soils: Proposal of sorption and transport mechanisms. J Hazard Mater 385:121576 Zhong J (2023) Current insights into the microbial degradation of nicosulfuron: Strains, metabolic pathways, and molecular mechanisms. Chemosphere 326:138390 Du R et al (2021) Construction of a synthetic microbial community for the biosynthesis of volatile sulfur compound by multi-module division of labor. Food Chem 347:129036 Johns NI, Blazejewski T, Gomes AL, Wang HH (2016) Principles for designing synthetic microbial communities. Curr Opin Microbiol 31:146–153 Jing J, Garbeva P, Raaijmakers JM, Medema MH (2024) Strategies for tailoring functional microbial synthetic communities. ISME J 18(1):wrae049 Grosskopf T, Soyer OS (2014) Synthetic microbial communities. Curr Opin Microbiol 18(100):72–77 Scott SR et al (2017) A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis. Nat Microbiol 2:17083 D'Souza G et al (2018) Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep 35(5):455–488 Coyte KZ, Schluter J, Foster KR (2015) The ecology of the microbiome: Networks, competition, and stability. Science 350(6261):663–666 Gould AL et al (2018) Microbiome interactions shape host fitness. Proc. Natl. Acad. Sci. USA 115(51), E11951-E11960 Mallon CA et al (2015) Resource pulses can alleviate the biodiversity-invasion relationship in soil microbial communities. Ecology 96(4):915–926 Nadell CD, Drescher K, Foster KR (2016) Spatial structure, cooperation and competition in biofilms. Nat Rev Microbiol 14:589e600 Villamizar S, Ferro JC, Caicedo JA, Alves L (2020) Bactericidal effect of entomopathogenic bacterium Pseudomonas entomophila against Xanthomonas citri reduces citrus canker disease severity. Front Microbiol 11:1431 Fuqua WC, Winans SC, Greenberg EP (1994) Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators. J Bacteriol 176(2):269–275 Valle A, Bailey MJ, Whiteley AS, Manefield M (2004) N-acyl-l-homoserine lactones (AHLs) affect microbial community composition and function in activated sludge. Environ Microbiol 6:424e433 Chong G, Kimyon O, Rice SA, Kjelleberg S, Manefield M (2012) The presence and role of bacterial quorum sensing in activated sludge. Microb Biotechnol 5:621e633 Tan CH et al (2014) The role of quorum sensing signalling in EPS production and the assembly of a sludge community into aerobic granules. ISME J 8:1186e1197 Tarighi S, Taheri P (2011) Different aspects of bacterial communication signals. World J Microbiol Biotechnol 27:1267e1280 Sturme MHJ et al (2002) Cell to cell communication by autoinducing peptides in grampositive bacteria. Anton Leeuw 81:233e243 Diggle SP, Cornelis P, Williams P, Camara M (2006) 4-Quinolone signalling in Pseudomonas aeruginosa : old molecules, new perspectives. Int J Med Microbiol 296:83e91 Lee VT et al (2007) A cyclic-di-GMP receptor required for bacterial exopolysaccharide production. Mol Microbiol 65:1474–1484 Hu H et al (2016) Biofilm activity and sludge characteristics affected by exogenous N-acyl homoserine lactones in biofilm reactors. Bioresour Technol 211:339–347 Zhou Q et al (2021) Impact of acyl-homoserine lactones on the response of nitrogen cycling in sediment to florfenicol stress. Sci Total Environ 785:147294–147304 Wang X et al (2020) Immobilization of Cd using mixed Enterobacter and Comamonas bacterial reagents in pot experiments with Brassica rapa L . Environ Sci Technol 54(24):15731–15741 Purnomo AS, Sariwati A, Kamei I (2020) Synergistic interaction of a consortium of the brown-rot fungus Fomitopsis pinicola and the bacterium Ralstonia pickettii for DDT biodegradation. Heliyon 6(6):e04027 Jia X, He Y, Jiang D, Liu C, Lu W (2019) Construction and analysis of an engineered Escherichia coli - Pseudomonas aeruginosa co-culture consortium for phenanthrene bioremoval. Biochem Eng J 148:214–223 Chen Y, Lin M, Zhuang D (2022) Wastewater treatment and emerging contaminants: Bibliometric analysis. Chemosphere 297:133932 Govantes R et al (2010) Regulation of the atrazine-degradative genes in Pseudomonas sp. Strain ADP. FEMS Microbiol Lett 310:1–8 Puri M, Gandhi K, Kumar MS (2023) Emerging environmental contaminants: A global perspective on policies and regulations. J Environ Manage 332:117344 Khan S, Naushad M, Govarthanan M, Iqbal J, Alfadul SM (2022) Emerging contaminants of high concern for the environment: Current trends and future research. Environ Res 207:112609 Wang L et al (2022) Engineering consortia by polymeric microbial swarmbots. Nat Commun 13(1):3879 Oña L et al (2021) Obligate cross-feeding expands the metabolic niche of bacteria. Nat Ecol Evol 5(9):1224–1232 Dolinšek J, Goldschmidt F, Johnson DR (2016) Synthetic microbial ecology and the dynamic interplay between microbial genotypes. FEMS Microbiol Rev 40(6):961–979 Zhou X et al (2022) Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease. Nat Commun 13(1):7890 Luo X et al (2015) Distal modulation of bacterial cell–cell signalling in a synthetic ecosystem using partitioned microfluidics. Lab Chip 15:1842–1851 Algburi A et al (2021) Black cardamom essential oil prevents Escherichia coli O157: H7 and Salmonella Typhimurium JSG 1748 biofflm formation through inhibition of quorum sensing. J Food Sci Technol 58(8):3183–3191 Huang J et al (2016) Acyl-homoserine lactone-based quorum sensing and quorum quenching hold promise to determine the performance of biological wastewater treatments: an overview. Chemosphere 157:137–151 Whiteley M, Diggle S, Greenberg E (2017) Progress in and promise of bacterial quorum sensing research. Nature 551:313–320 Tripathi S, Sharma P, Chandra R (2021) Distillery wastewater detoxiffcation and management through phytoremediation employing Ricinus communis L . Bioresour Technol p. 125192 Tripathi S, Sharma P, Chandra R (2021) Degradation of organometallic pollutants of Distillery wastewater by bacterial community in biostimulation and bioaugmentation process. Bioresour Technol p. 125518 Zhang Z et al (2019) The regulation of N-acyl-homoserine lactones (AHLs)-based quorum sensing on EPS secretion via ATP synthetic for the stability of aerobic granular sludge. Sci Total Environ 673:83–91 Zhang Z et al (2020) N-acyl-homoserine lactones (AHLs) in intertidal marsh: Diversity and potential role in nitrogen cycling. Plant Soil 454:103–119 Markus V et al (2023) Conversations in the gut: The role of quorum sensing in normobiosis. Int J Mol Sci 24:3722 Mayer C et al (2023) Quorum sensing architecture network in Escherichia coli virulence and pathogenesis. FEMS Microbiol Rev 47:fuad031 Lewis H et al (2001) A structural genomics approach to the study of quorum sensing crystal structures of three LuxS orthologs. Structure 9:527–537 Zhang Y, Xu Q, Wang G, Shi K (2022) Indole-acetic acid promotes ammonia removal through heterotrophic nitriffcation, aerobic denitriffcation with mixed Enterobacter sp. Z1 and Klebsiella sp. Z2. Front Microbiol 13:929036 Dai Y et al (2025) Biodegradation of acetochlor by microbial consortium AT1: microcosm centric microbiomic-metabolomics mechanisms and environmental remediation feasibility. J Environ Manage 387:125892 Li L et al (2024) Interplay of humic acid and Cr(Ⅵ) on green microalgae: metabolic responses and chromium enrichment. J Hazard Mater 480:135885 Campbell PGC, Twiss MR, Wilkinson KJ (1997) Accumulation of natural organic matter on the surfaces of living cells: implications for the interaction of toxic solutes with aquatic biota. Can J Fisheries Aquat Sci 54(11):2543–2554 Xu Z et al (2022) A novel pathway of Chlorimuron-Ethyl biodegradation by Chenggangzhangella methanolivorans strain CHL1 and its molecular mechanisms. Int J Mol Sci 23(17):9890 Ma Q et al (2021) Substrate control of sulphur utilisation and microbial stoichiometry in soil: Results of 13C, 15N, 14C, and 35S quad labelling. ISME J 15(11):3148–3158 Abdulinam D et al (2020) ATP sulfurylase activity of sulfate-reducing bacteria from various ecotopes. 3 Biotech. 10(2) Spencer RS et al (2017) A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum‐ regulated lysis. Nat Microbiol 2:17083 Trevor GJ et al (2020) Compartmentalized microbes and co-cultures in hydrogels for on‐demand bioproduction and preservation. Nat Commun 11:563 Marmur J, Schildkraut CL, Doty P (1961) The reversible denaturation of DNA and its use in studies of nucleic acid homologies and the biological relatedness of microorganisms. J Chim Phys Physicochim Biol 58:945–955 Ren Y, Yang L, Liang X (2014) The characteristics of a novel heterotrophic nitrifying and aerobic denitrifying bacterium, Acinetobacter junii YB. Bioresour Technol 171:1–9 Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) Mega 6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30(4):2725–2729 Zhang Y, Xu Z, Chen Z, Wang G (2020) Simultaneous degradation of triazophos, methamidophos and carbofuran pesticides in wastewater using an Enterobacter bacterial bioreactor and analysis of toxicity and biosafety. Chemosphere 261:128054 Kagiya G et al (2005) Generation of a strong promoter for Escherichia coli from eukaryotic genome DNA. J Biotechnol 115:239–248 Han H (2018) RNA interference to knock down gene expression. Methods Mol Biol 1706:293–302 Zhang Y et al (2019) Cooperation between two strains of Enterobacter and Klebsiella in the simultaneous nitrogen removal and phosphate accumulation processes. Bioresour Technol 291:121854 Zhang Y, Xu Q, Wang G, Shi K (2023) Mixed Enterobacter and Klebsiella bacteria enhance soybean biological nitrogen fixation ability when combined with rhizobia inoculation. Soil Biol Biochem 184:109100 Wang Y et al (2022) Suaeda salsa root-associated microorganisms could effectively improve maize growth and resistance under salt stress. Microbiol Spectr 10(4):e0134922 Arbona V, López-Climent MF, Pérez-Clemente RM (2009) Gómez-Cadenas, A. Maintenance of a high photosynthetic performance is linked to flooding tolerance in citrus. Environ Exp Bot 66:135–142 Chen S Ultrafast one-pass FASTQ Data preprocessing, quality control, and deduplication using Fastp. iMeta 2, e107 Li D et al (2015) An ultra-fast single‐node solution for large and complex metagenomics assembly via succinct de bruijn graph. Bioinformatics 31:1674–1676 Bjørn NH et al (2014) Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol 32:822–828 Kang D et al (2019) MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359 Additional Declarations There is NO Competing Interest. Supplementary Files Zhangetal.SupplementaryMaterials.docx Extended data Figure Legends Extended Data Fig.1 Co-degradation capacities and proteomics analysis of SynCom YKB and HKB. (a) Morphological and phylogenetic analysis of the strains. (b) Degradation capabilities and first-order kinetic parameters of the SynComs under varying initial herbicide concentrations. (c) Growth status of individual members of the SynComs at a initial herbicide concentration of 100 mg/L. (d) Volcano plot of differentially expressed proteins identified by proteomics across treatment groups. Red dots denote up-regulated proteins, and blue dots denote down-regulated proteins. (e) Quantification of differentially expressed proteins in each symbiont member under the exposure of herbicides. Red bars represent up-regulated proteins, and green bars represent down-regulated proteins. (f) Subcellular localization analysis of differentially expressed proteins annotated in each symbiont member under the exposure of herbicides. Extended Data Fig.2 COG pathway enrichment analysis on treatment groups. Significantly enriched pathways include: Sulfur metabolism (pink), Tryptophan metabolism (yellow), Glycose and energy metabolism (brown), Herbicide degradation (blue), and EPS and quorum sensing (green). Extended Data Fig.3 Expression levels of functional proteins involved in herbicide degradation, quorum sensing, tryptophan metabolism, sulfur metabolism, energy metabolism, and biofilm formation in different systems by qRT-PCR. Extended Data Fig.4 Resilience capacities of exogenous QS signals on key metabolites concentration in SynComs RNAi strains. Extended Data Fig.5 Functional analysis of down-regulated metabolic pathways and metabolites in the interference system. (a) KEGG topological analysis of down-regulated metabolic pathways in the interference system. Each bubble represents a KEGG pathway, with larger bubbles indicating greater biological relevance. The x-axis represents the relative importance of metabolites in the pathway (Impact value), while the y-axis represents the enrichment significance of metabolites participating in the pathway, expressed as -log10 ( p -value). (b) Down-regulated metabolites classification and enrichment in interference systems by Metabolite Set Enrichment Analysis (MSEA). Color indicates the enrichment rate of metabolites in down-regulated metabolism. Extended Data Fig.6 Metagenomic profiling of soil functional regulation with the inoculation of SynComs or interference strains. (a) α -diversity indices across treatments at taxonomic, KEGG, and COG levels (Chao1 and shannon). (b) PCoA analysis of β -diversity (Bray-Curtis dissimilarity) for taxonomic, KEGG, and COG levels. (c) Neutral Community Model and Stochasticity Threshold (NST) analysis of community structure among different treatment groups. The dashed line in the figure represents the threshold for distinguishing between deterministic and stochastic. D, Venn diagrams and taxonomic composition among different treatment groups. Abundance was calculated using RPKM (Reads Per Kilobase per Million mapped reads), with the top 20 most abundant genera selected for visualization. Extended Data Fig.7 WGCNA of different treatment groups and environmental factors. Correlation coefficients were calculated using the Spearman method, and the top 50 nodes with the highest connectivity within each module were selected for analysis. Modules significantly positively correlated with wild-type systems and environmental factors were selected for KEGG pathway analysis. Extended Data Fig.8 Heatmap of the Spearman correlation of key environmental factors in maize pot culture with taxonomic composition structure and COG functional levels in the metagenomics. The color represents the magnitude of the correlation factors. Extended Data Fig.9 Sankey plot of community abundance of MAGs at different taxonomic levels. Different bars represent different taxonomic levels and groups, color bands in the bars represent species, the length of the bands represent the species abundance, and the connecting lines represent the correspondences of the species at different levels. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8076029","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":549944739,"identity":"1cadd872-5a37-4ac4-bec4-fa26bc5665f9","order_by":0,"name":"Haiyan Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACAwYGNgaGCgmStZwhWQtjGykOM5dIfvaYd56FvXz78QcMP2oY5M0JabGckWZuzLtNInHDmRwDxp5jDIY7Gwg57EYOmzRQS4IBQw4DA28DQ4LBAaK0zJGwl+9//oDxL/FaGiQYG24kGDATZ8uZZ2aSc44B/XLjjcFhmWMShhsIajme/EziTU0d0GHpDx++qbGRJ2gLg0ACgg1UTEyc8hM0dBSMglEwCkY8AAAzEztUoLufrgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9004-8750","institution":"Institute of Soil Science, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Chu","suffix":""},{"id":549944740,"identity":"6dc34e23-9679-4987-8d2e-bc3f2ec5b9c9","order_by":1,"name":"Yuxiao Zhang","email":"","orcid":"","institution":"Institute of Soil Science, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuxiao","middleName":"","lastName":"Zhang","suffix":""},{"id":549944741,"identity":"81e3cb19-c32a-4446-bc70-39fa3036d14a","order_by":2,"name":"Jack A. 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(a) KEGG pathway enrichment analysis on treatment groups.\u003c/strong\u003eSignificantly enriched pathways include: Herbicide catabolism (blue), Glycose and energy metabolism (brown), Quorum sensing and biofilm (green), Tryptophan metabolism (yellow), Sulfur metabolism (purple), and DNA repair (orange). (b) Expression levels of key enzymes in proteomics enriched pathways. The color scale reflects relative protein expression levels within each treatment group.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/6d716da374281f6ee9d95d95.png"},{"id":97322911,"identity":"654b274c-44e5-4843-8004-729a53c2f775","added_by":"auto","created_at":"2025-12-03 08:15:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1510861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential accumulation of key metabolites across experimental systems.\u003c/strong\u003e (a) Quantitative comparison of biofilm biomass, ATP, NADH, succinate (SA), tryptophan (Trp), and taurine (Tau) concentrations in SynComs and monocultures under the exposure of herbicides. (b) Comparative levels of biofilm biomass, ATP, NADH, succinate, Trp, and Tau concentration in herbicide-treated vs. untreated. (c) Quantitative comparison of QS signaling molecules in SynComs and monocultures. (d) Regulatory effects of exogenous quorum sensing signals, AHLs and AI-2, on herbicides degradation efficiencies, herbicides resistance and key metabolites concentration. (e) qRT-PCR quantification of key protein expression levels in SynCom YKB (top) and HKB (down) following supplementation with exogenous metabolic signals.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/6b46d17281b24c1057448c5c.png"},{"id":97370869,"identity":"7428aaaf-0364-496b-8197-b1f3af91bdd7","added_by":"auto","created_at":"2025-12-03 16:28:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1766530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4 Maize pot experiments on SynComs and key interference systems. (a) Maize growth performance across treatment groups at 30 days post-emergence.\u003c/strong\u003e (b) Differences in root growth indicators among different treatment groups. (c) Differences in leaf physiological indicators among different treatment groups. (d) Residual herbicide concentrations in rhizosphere black soil among different treatment groups.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/396d405d2f1479ae470cc7d2.png"},{"id":97322914,"identity":"3ebd454f-8d72-4a66-a96c-e0c5bcd71e24","added_by":"auto","created_at":"2025-12-03 08:15:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1367492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5 Annotation and enrichment of key metabolic functions among different treatment groups. \u003c/strong\u003e(a) KEGG enrichment analysis of differentially and key regulated pathways between SynComs vs control, and interference systems vs wild-type systems. (b) Metagenomic annotation of expression levels of key enzymes for herbicide degradation. (c) Gene count of quorum sensing signal molecules annotated by metagenomics in different treatment groups. The dashed line indicates the median values connecting different treatments.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/ace3823601aa1e2d60ef8826.png"},{"id":97322913,"identity":"7551c782-58e7-4d39-b152-063b7c53e527","added_by":"auto","created_at":"2025-12-03 08:15:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1279160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6 Evolutionary, abundance, and functional analysis of key MAGs.\u003c/strong\u003e (a) Evolutionary relationships at the phylum level, relative abundance in the samples, and correlation with environmental factors of 28 MAGs. Each branch in the evolutionary tree represents different MAGs, the branch length is the evolutionary distance between two species, \u003cem\u003ei.e.\u003c/em\u003e, the degree of species difference, and different colors represent different phyla. (b) Functional annotation of KEGG metabolism of the 4 key MAGs (strains Y1, K1, B1, and H1). Node size represents the \u003cem\u003ep\u003c/em\u003e-value, and color represents the number of annotations. Different colors represent different KEGG metabolic classifications.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/71eb3c4946ca179e7cf7c1d5.png"},{"id":97369878,"identity":"68123c6c-3b01-441c-8876-12089d06e9db","added_by":"auto","created_at":"2025-12-03 16:25:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3409014,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7 Quorum sensing-driven interaction mechanisms of SynComs members (a) and functional regulation of SynComs in soil ecosystems (b).\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/c1475d9e2e9aa70b5ff57b57.png"},{"id":97664606,"identity":"a59e3f5a-3a58-4cad-8fc0-0efa890e3b77","added_by":"auto","created_at":"2025-12-08 09:11:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12373790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/ff99e204-32be-4f6d-89ae-c11257c77b54.pdf"},{"id":97322917,"identity":"f31dd0e2-fdff-4351-99e5-10dfc9451e65","added_by":"auto","created_at":"2025-12-03 08:15:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43265757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended data Figure Legends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.1\u003c/strong\u003e \u003cstrong\u003eCo-degradation capacities and proteomics analysis of SynCom YKB and HKB. \u003c/strong\u003e(a) Morphological and phylogenetic analysis of the strains. (b) Degradation capabilities and first-order kinetic parameters of the SynComs under varying initial herbicide concentrations. (c) Growth status of individual members of the SynComs at a initial herbicide concentration of 100 mg/L. (d) Volcano plot of differentially expressed proteins identified by proteomics across treatment groups. Red dots denote up-regulated proteins, and blue dots denote down-regulated proteins. (e) Quantification of differentially expressed proteins in each symbiont member under the exposure of herbicides. Red bars represent up-regulated proteins, and green bars represent down-regulated proteins. (f) Subcellular localization analysis of differentially expressed proteins annotated in each symbiont member under the exposure of herbicides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.2\u003c/strong\u003e \u003cstrong\u003eCOG pathway enrichment analysis on treatment groups.\u003c/strong\u003e Significantly enriched pathways include: Sulfur metabolism (pink), Tryptophan metabolism (yellow), Glycose and energy metabolism (brown), Herbicide degradation (blue), and EPS and quorum sensing (green).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.3\u003c/strong\u003e \u003cstrong\u003eExpression levels of functional proteins involved in herbicide degradation, quorum sensing, tryptophan metabolism, sulfur metabolism, energy metabolism, and biofilm formation in different systems by qRT-PCR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.4\u003c/strong\u003e \u003cstrong\u003eResilience capacities of exogenous QS signals on key metabolites concentration in SynComs RNAi strains.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.5 Functional analysis of down-regulated metabolic pathways and metabolites in the interference system.\u003c/strong\u003e (a) KEGG topological analysis of down-regulated metabolic pathways in the interference system. Each bubble represents a KEGG pathway, with larger bubbles indicating greater biological relevance. The x-axis represents the relative importance of metabolites in the pathway (Impact value), while the y-axis represents the enrichment significance of metabolites participating in the pathway, expressed as -log10 (\u003cem\u003ep\u003c/em\u003e-value). (b) Down-regulated metabolites classification and enrichment in interference systems by Metabolite Set Enrichment Analysis (MSEA). Color indicates the enrichment rate of metabolites in down-regulated metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.6\u003c/strong\u003e \u003cstrong\u003eMetagenomic profiling of soil functional regulation with the inoculation of SynComs or interference strains.\u003c/strong\u003e (a) \u003cem\u003eα\u003c/em\u003e-diversity indices across treatments at taxonomic, KEGG, and COG levels (Chao1 and shannon). (b) PCoA analysis of \u003cem\u003eβ\u003c/em\u003e-diversity (Bray-Curtis dissimilarity) for taxonomic, KEGG, and COG levels. (c) Neutral Community Model and Stochasticity Threshold (NST) analysis of community structure among different treatment groups. The dashed line in the figure represents the threshold for distinguishing between deterministic and stochastic. D, Venn diagrams and taxonomic composition among different treatment groups. Abundance was calculated using RPKM (Reads Per Kilobase per Million mapped reads), with the top 20 most abundant genera selected for visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.7\u003c/strong\u003e \u003cstrong\u003eWGCNA of different treatment groups and environmental factors. \u003c/strong\u003eCorrelation coefficients were calculated using the Spearman method, and the top 50 nodes with the highest connectivity within each module were selected for analysis. Modules significantly positively correlated with wild-type systems and environmental factors were selected for KEGG pathway analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.8\u003c/strong\u003e \u003cstrong\u003eHeatmap of the Spearman correlation of key environmental factors in maize pot culture with taxonomic composition structure and COG functional levels in the metagenomics. \u003c/strong\u003eThe color represents the magnitude of the correlation factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig.9\u003c/strong\u003e \u003cstrong\u003eSankey plot of community abundance of MAGs at different taxonomic levels.\u003c/strong\u003e Different bars represent different taxonomic levels and groups, color bands in the bars represent species, the length of the bands represent the species abundance, and the connecting lines represent the correspondences of the species at different levels.\u003c/p\u003e","description":"","filename":"Zhangetal.SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8076029/v1/1292cc08a6bd21d44857a020.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Microbial Communication Drives Division of Labor in SynComs for Herbicides Degradation","fulltext":[{"header":"Significance","content":"\u003cp\u003eThis study presents a new framework for bioremediation that unites microbial ecology, synthetic biology, systems modeling, and sustainable agriculture. We constructed two synthetic microbial communities (SynComs) capable of simultaneously degrading multiple herbicides, while maintaining stability and promoting crop growth. Through integrated multi-omics, we reveal that distinct quorum-sensing (QS) systems (acyl-homoserine lactone (AHL)-mediated and autoinducer-2 (AI-2)-mediated signaling) coordinate metabolic cooperation, division of labor, and ecological resilience among community members. These findings identify QS-mediated communication as a governing principle for designing effective SynComs, enabling predictable and scalable microbial strategies for soil restoration. Our findings advance microbial systems design and biotechnological engineering for pollution control and provide a new conceptual and technological foundation for harnessing microbial cooperation to ensure food security and ecosystem sustainability.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe northeast black soil region of China, recognized as one of the world’s four major chernozem zones, play a fundamental role in Chinese national food security\u003csup\u003e1-5\u003c/sup\u003e. However, decades of intense agriculture have led to an accumulation of herbicide residue pollution\u003csup\u003e6, 7\u003c/sup\u003e. While the application of herbicides has effectively suppressed the growth of weeds in farmlands to a certain extent\u003csup\u003e8\u003c/sup\u003e, high frequency and high-dose applications coupled with declining utilization efficiency have led to significant herbicide residues in soils, which reduces soil biodiversity, accelerate nutrient loss, degrades environmental quality, and poses potential health risks via food chain accumulation\u003csup\u003e9-12\u003c/sup\u003e. The main herbicide residues include acetochlor, atrazine, nicosulfuron, and fomensafen, spanning multiple types such as amides, triazines, sulfonylureas and ethers, with co-contamination being most common\u003csup\u003e13-15\u003c/sup\u003e.\u0026nbsp;The primary crops cultivated in the northeast chernozem region are maize, soybean, and rice\u003csup\u003e16,\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e and herbicide types and application rates vary considerably across cropping systems and farming practices, leading to significant regional differences in herbicide residue levels\u003csup\u003e18,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e. These disparities complicate remediation and management efforts, making the development of effective strategies to eliminate co-occurring herbicide residues and restore the ecological functions of Northeast chernozems an urgent scientific and technological challenge.\u003c/p\u003e\n\u003cp\u003eMicrobial remediation represents a promising approach for mitigating herbicide pollution\u003csup\u003e20, 21\u003c/sup\u003e. However, bioremediation with a single microorganism has inherent limitations when addressing complex mixed contaminants\u003csup\u003e22-24\u003c/sup\u003e, including narrow substrate specificity, which restricts degradation capacity to targeted pollutants\u003csup\u003e25\u003c/sup\u003e, and limited environmental adaptability, which impacts the survival and remediation efficacy of the bacteria\u003csup\u003e26, 27\u003c/sup\u003e. Synthetic microbial communities (SynComs), comprising the artificial combination of two or more microorganisms with clearly defined taxonomic status and functional characteristics at predetermined ratios, overcome the limitations of a single strain strategy, so as to both enhance degradation efficiency of complex herbicide pollution and support better ecosystem adaptation and survival\u003csup\u003e28, 29\u003c/sup\u003e. SynComs are often designed to provide a parsimonious combination of functional traits to facilitate a defined outcome with limited redundancy among community members\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;However, the SynCom membership is also selected so as to facilitate metabolic coordination between members. This metabolic interaction is thought to contribute to the structural stability, adaptation to diverse environments, and the execution of complex functions by the assemblage. Recent advances in AI, synthetic microbiology, and multi-omic analyses have accelerated effective SynCom design, yet we still lack a fundamental understanding of the key biological features that facilitate metabolic interaction between members and stable ecological dynamics when introduced to a natural soil\u003csup\u003e31, 32\u003c/sup\u003e. We posit, that systems ecology theory provides a foundation on which to understand the interactions and regulatory mechanisms within SynComs, and can help decipher their functional networks, mechanisms of interindividual communication, and the resulting division of metabolic activity among constituent strains\u003csup\u003e28, 30\u003c/sup\u003e.\u0026nbsp;Recent work has provided proof-of-concept that such communities can be engineered to achieve this goal\u003csup\u003e11\u003c/sup\u003e. In a previous study, we constructed a four-member SynCom that simultaneously degraded 8 herbicides in black soils, while also enhancing microbial diversity, stabilizing colonization, and stimulating soil carbon metabolism. This demonstrated that SynComs can both remediate herbicide residues and restore critical soil functions. However, the regulatory mechanisms enabling these communities to coordinate activity and divide labor remained unclear.\u003c/p\u003e\n\u003cp\u003eMicrobial interactions within SynComs are fundamentally shaped by quorum sensing (QS) and metabolic cross-feeding, which collectively determine the community's structure and function\u003csup\u003e33, 34\u003c/sup\u003e.\u0026nbsp;The QS system enables bacteria to perceive population density through the production, release, and detection of extracellular signaling molecules\u003csup\u003e35, 36\u003c/sup\u003e. This facilitates coordination of gene expression and behaviors among community members to optimize spatiotemporal organization, functional output, ecosystem integrity, and controllability\u003csup\u003e37\u003c/sup\u003e, as well as inter-species interactions, microbial environmental functions, and host-microbe relationships\u003csup\u003e38, 39\u003c/sup\u003e. QS constitutes a complex signaling network comprising multiple pathways\u003csup\u003e40, 41\u003c/sup\u003e. Extensive research has identified and characterized numerous distinct QS signal molecules (Table S1)\u003csup\u003e42-46\u003c/sup\u003e: (1) Acyl-homoserine lactones (AHLs); (2) Autoinducers (AI-2/AI-3); (3) Autoinducing peptides (AIPs); and (4) Others, such as \u003cem\u003ePseudomonas\u003c/em\u003e quinolone signal (PQS) and diketopiperazines (DKPs). Metabolic complementarity within microbial communities is regulated by QS, enabling assemblages to circumvent metabolic bottlenecks and the accumulation of toxic intermediates within single cells\u003csup\u003e47, 48\u003c/sup\u003e. Therefore, better characterization of the QS activity among members will enable the development of more robust SynComs for applied solutions to pollution.\u003c/p\u003e\n\u003cp\u003eHere we present two new SynComs designed to degrade complex multi-herbicide pollution. Through integrated multi-omics analyes we characterized the metabolic interactions, signal transduction pathways, and functional regulation that enable each SynCom to degrade soil herbicide residues. We also developed a predictive model that explains how QS mediates SynCom ecological dynamics. In sum, we present a robust, highly adaptive, and efficient bioremediation tool for herbicide pollution.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eScreening of herbicide-degrading strains in northeast black soil and construction of SynComs\u003c/h2\u003e\u003cp\u003eWe used enrichment and laboratory domestication techniques to isolate 29 potential herbicide-degrading strains from six soil samples (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table S2). In monoculture each of the 29 strains exhibited different herbicide degradation abilities at an initial concentration of 100 mg/L, though the degradation rates were generally below 50% within 60 h (Table S3). However, \u003cem\u003ePseudomonas\u003c/em\u003e sp. B1, \u003cem\u003eRalstonia\u003c/em\u003e sp. R5, \u003cem\u003eBacillus\u003c/em\u003e sp. H1, \u003cem\u003eKlebsiella\u003c/em\u003e sp. L3, \u003cem\u003eEnterobacter\u003c/em\u003e sp. E3, \u003cem\u003eAcinetobacter\u003c/em\u003e sp. K1, and \u003cem\u003eComamonas\u003c/em\u003e sp. Y1 all demonstrated degradation rates exceeding 50% for specific herbicides. Through random combination experiments with SynComs comprising 2 to 5 strains, we identified two 3-member SynComs with the ability to perform 80% degradation efficiency for 8 herbicides (atrazine, acetochlor, butachlor, metolachlor, metribuzin, nicosulfuron, pyrazosulfuron, fomensafen; Fig. S2a). SynCom YKB comprised \u003cem\u003eComamonas\u003c/em\u003e sp. Y1, \u003cem\u003eAcinetobacter\u003c/em\u003e sp. K1, and \u003cem\u003ePseudomonas\u003c/em\u003e sp. B1; while SynCom HKB comprised \u003cem\u003eBacillus\u003c/em\u003e sp. H1, \u003cem\u003eAcinetobacter\u003c/em\u003e sp. K1, and \u003cem\u003ePseudomonas\u003c/em\u003e sp. B1. Further analysis revealed that the SynCom YKB not only exhibited enhanced degradation performance but also showed a significant increase in herbicide tolerance, with resistance levels of ~\u0026thinsp;100% (Fig. S2b). Conversely, SynCom HKB showed no significant improvement in resistance to herbicides compared to the monoculture strains (Fig. S2b).\u003c/p\u003e\u003cp\u003eThe ability of each SynComs to degrade a combination of 8 herbicides at varying concentrations were investigated. Both SynComs could efficiently degrade the herbicide mix, following a first-order kinetic model (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). Notably, both SynComs had optimal observed performance when the initial concentration of the 8-herbicide mixture was 100 mg/L. Therefore, this condition was selected as the standard treatment for subsequent studies. Throughout the degradation process, all members of each SynCom maintained consistent growth trends (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), providing a solid foundation for stable microbial interactions and functional performance. Furthermore, the overall biomass in both the SynComs and monoculture systems tended to be consistent, indicating that the enhanced herbicides degradation capacity in SynComs was not achieved through a simple increase in cell numbers (Fig. S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eProteomics reveals metabolic functions and division of labor among SynComs members\u003c/h2\u003e\u003cp\u003eTo elucidate the interaction patterns within the SynComs and the mechanisms underlying herbicide degradation, we conducted a comparative proteomic analysis under the following conditions: (1) monocultures versus SynComs, and (2) presence or absence of 100 mg/L of the 8-herbicide mix. Compared to monocultures, the SynComs demonstrated significant up-regulation of numerous proteins in the absence of herbicide pollution. SynCom YKB showed upregulation of 1155, 877, and 577 proteins, and downregulation of 81, 36, and 213 proteins compared to monocultures of Y1, K1, and B1, respectively (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Similarly, SynCom HKB had upregulation of 812, 189, and 319 proteins, and downregulation of 33, 282, and 12 proteins, compared to the monocultured strains H1, K1, and B1, respectively.\u003c/p\u003e\u003cp\u003eIn the presence of the 8 herbicides, SynComs YKB and HKB showed up-regulation of 185 and 79 proteins and down-regulation of 69 and 76 proteins, respectively, compared to the no herbicide control. Analysis of differentially expressed proteins (DEPs) annotated to each member revealed that in SynCom YKB, strain K1 contained the most DEPs, with 63 up-regulated and 19 down-regulated proteins (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). In SynCom HKB, strain B1 showed the highest number of DEPs, including 48 up-regulated and 31 down-regulated proteins. Simultaneously, subcellular localization analysis of the DEPs indicated that a majority were localized in the cytoplasm. These findings may suggest distinct metabolic partitioning among members within different SynComs, with different members showing different levels of protein expression across each SynCom and in monoculture (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Fig. S4).\u003c/p\u003e\u003cp\u003eWe further employed GO, COG, and KEGG databases to compare the DEPs and functional metabolic pathways enriched among members of the SynComs. In the GO enrichment analysis of SynCom YKB, compared to monoculture, strain K1 exhibited greater enrichment of proteins involved in cellular biosynthetic process, cellular metabolic process, and macromolecule biosynthetic process. Strain B1 showed significant upregulation in catalytic activity, nucleotide binding, and metabolic process. Strain Y1 predominantly contributed to transporter complex, localization, and amino acid/organic acid metabolic processes (Fig. S5). Notably, strain Y1 demonstrated greater metabolic functional divergence compared to the other two members. In SynCom HKB, GO analysis revealed that strain K1 was mainly enriched in chemotaxis, signaling receptor activity, and nucleotide binding. Strain B1 upregulated functions associated with localization, transporter complex, and oxidoreductase activity, and Strain H1 dominated DNA catalysis, DNA-binding transcription factor activity, and sulfur biosynthetic process (Fig. S6). Compared to SynCom YKB, the members in SynCom HKB system showed lower overlap in GO enrichment, indicating more distinct metabolic partitioning among strains.\u003c/p\u003e\u003cp\u003eThe COG and KEGG enrichment analyses also elucidated the key functional roles undertaken by different members within the SynComs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Upon herbicide induction, SynCom YKB significantly up-regulated pathways related to herbicide degradation, quorum sensing (QS), tryptophan metabolism, and carbohydrate metabolism, while SynCom HKB primarily enhanced herbicide degradation, QS, sulfur metabolism, and carbohydrate metabolism. Compared to monocultures, each strain also exhibited distinct metabolic functional enrichment when in the SynCom. In SynCom YKB, strain Y1 was mainly enriched in pathways related to carbohydrate and energy metabolism, strain K1 significantly upregulated herbicide degradation and QS, while strain B1 contributed to tryptophan metabolism and carbohydrate metabolism. Similarly, in SynCom HKB, strain H1 was primarily responsible for DNA repair and QS, strain K1 was enriched in key pathways including tryptophan metabolism, carbohydrate metabolism, and herbicide degradation; and strain B1 significantly up-regulated herbicide degradation and carbohydrate metabolism. These results suggest metabolic division of labor and synergistic interactions between the SynComs members (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Table S4\u0026ndash;S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo elucidate metabolic interactions we constructed protein-protein interaction (PPI) networks based on the enriched proteins of each strain. In SynCom YKB, strain Y1, K1, and B1 exhibited core functional modules centered around carbohydrate metabolism, QS, and tryptophan metabolism, respectively (Fig. S7a). Similarly, in SynCom HKB, the core modules of strains H1, K1, and B1 were primarily associated with QS, sulfur metabolism, and herbicide degradation, respectively (Fig. S7a). Network centrality analyses (degree, closeness, and betweenness) showed that strains Y1 and H1 were the most central nodes in their respective SynComs, indicating that they played the largest role in shaping overall network complexity. (Fig. S7b).\u003c/p\u003e\u003cp\u003eIn the two SynComs, we further analyzed the enriched protein sets associated with strains K1 and B1. Strain K1 showed strong overlap in differentially enriched proteins across both systems, while strain B1 displayed marked divergence, indicating functional plasticity and context-dependent roles of B1 in different symbiotic environments. Additionally, we employed weighted gene co-expression network analysis (WGCNA) to identify key modules significantly correlated with herbicide degradation rates (Fig. S8). In SynCom YKB, three key modules were detected, among them, the MEblue module was significantly enriched with proteins involved in QS, herbicide degradation, and tryptophan metabolism (Fig. S8). In SynCom HKB, four key modules were identified: MEblue was enriched in proteins related to QS and herbicide degradation, while MEbrown was predominantly associated with sulfur metabolism (Fig. S8). Finally, RT-qPCR was employed to validate the expression levels of genes encoding key proteins under different treatment conditions. The results demonstrated that the expression levels were significantly greater in the SynComs than in monocultures, which was consistent with the proteomics analysis (Extended Data Fig.\u0026nbsp;3).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQS-mediated regulation of microbial interactions and metabolic functions\u003c/h3\u003e\n\u003cp\u003eTo further elucidate the interaction patterns and regulatory mechanisms within each SynCom, we quantified 6 key metabolites under different treatments: biofilm (extracellular polysaccharides, EPS), energy molecules (ATP and NADH), succinate (SA), tryptophan (trp), and a sulfur cycle metabolite (taurine, tau). Compared to monocultures, the SynComs exhibited significantly greater accumulation of all 6 metabolites during herbicide degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), but the SynComs showed differential accumulation patterns, e.g. SynCom YKB accumulated more biofilm, SA, and trp, whereas SynCom HKB accumulated more tau.\u003c/p\u003e\u003cp\u003eFurthermore, we quantified the levels of QS signal molecules across different systems. The results revealed that, compared to the monocultures or herbicides-free control, N-hexanoyl-L-homoserine lactone (C6-HSL) consistently accumulated at elevated levels in SynCom YKB, whereas high concentrations of (S)-4,5-dihydroxy-2,3-pentanedione (DPD/AI-2) were consistently detected in SynCom HKB. Therefore, we postulate that C6-HSL and AI-2 serve as the key QS signal molecules in their respective SynComs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). To determine if QS might play a role in stimulating metabolic activity in the SynComs, we supplemented the SynComs with C6-HSL and DPD/AI-2. This exogenous supplementation significantly enhanced both herbicide degradation efficiency and tolerance in both monocultures and each SynCom (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Furthermore, the addition of C6-HSL and DPD/AI-2 promoted the accumulation of biofilm, ATP, NADH, SA, trp, and tau, indicating stimulation of overall cellular metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). To validate these findings, we used RT-qPCR to quantify the effect of adding the C6-HSL, DPD/AI-2, SA, trp and tau on the expression levels of the proteins in related metabolic pathways. The results demonstrated that C6-HSL and DPD/AI-2 markedly upregulated the expression of key proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Specifically, C6-HSL significantly enhanced the expression of key enzymes in pathways associated with herbicide degradation, AHL signaling, biofilm formation, carbohydrate metabolism, and tryptophan metabolism in SynCom YKB (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Interestingly in SynCom HKB, it was DPD/AI-2 and not C6-HSL that promoted the expression of enzymes in herbicide-degradation and carbohydrate metabolism, as well as the AI-2 signaling pathway and sulfur metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). This suggests that each SynCom has a unique QS regulatory system, with SynCom YKB primarily being induced by AHL-type signals, and SynCom HKB mainly regulated by AI-2-type signals.\u003c/p\u003e\u003cp\u003eAs expected, when we inhibited the key enzymes involved in herbicide degradation, the degradation capacity within each SynCom was significantly reduced (Fig. S9, Table S6, S7). This impact was most significant for those herbicide degradation pathways in strain K1 for SynCom YKB, and strain B1 for SynCom HKB. We conducted an in-depth analysis of the impact of inhibiting the expression of key proteins essential for target metabolic functions on both herbicide degradation and core metabolic activities within each SynCom. inhibition of key genes in the QS pathways, specifically the AHL and AI-2 pathways, generally led to significant suppression of herbicide degradation in each SynCom. In validation of the importance of different QS pathways in each SynCom, inhibition of the AHL pathway had a more substantial effect on SynCom YKB, while AI-2 inhibition had a bigger impact for SynCom HKB (Table S8). Moreover, inhibition of the QS pathways markedly suppressed downstream metabolic processes including biofilm formation, carbohydrate metabolism, energy metabolism, tryptophan, and sulfur metabolism, as measured by enzyme expression and metabolite concentration (Fig. S10, Table S8). To validate this importance, we added key metabolites (C6-HSL, DPD/AI-2, SA, trp, or tau) back to strains with inhibited pathways. The addition of C6-HSL and DPD/AI-2 significantly rescued the herbicide degradation capacity in all inhibited strains. In contrast, the addition of other small metabolites (SA, trp, tau) only restored degradation capability in their respective target RNAi strains (Fig. S11). Furthermore, the application of C6-HSL and DPD/AI-2 also enhanced the accumulation of key downstream metabolites within the RNAi systems, with as expected based on the results above, differential restorative effects of C6-HSL and AI-2 on SynCom YKB and HKB, respectively (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Collectively, these findings underscore the critical regulatory role of QS signaling molecules in the SynComs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven the inherent cytotoxicity of herbicides, we assessed the levels of cellular toxicity during the degradation process by measuring intracellular \u003cem\u003eβ\u003c/em\u003e-galactosidase activity (Fig. S12). Upon herbicide addition to the SynComs, high levels of cytotoxicity were observed within the first 12\u0026ndash;24 hours, indicating the accumulation of both the parent herbicide and its intermediate metabolites (Fig. S12). As degradation progressed, the cytotoxicity gradually decreased and returned to baseline levels, demonstrating rapid and complete herbicides breakdown (Fig. S12). In contrast to the wild-type strains, the RNAi strains consistently exhibited sustained high cytotoxicity throughout the degradation process (Fig. S12). Among them, the inhibition of AHLs showed the greatest cytotoxicity for SynCom YKB while inhibition of AI-2 and sulfur metabolism had the greatest cytotoxicity for SynCom HKB.\u003c/p\u003e\n\u003ch3\u003eMetabolomics reveals QS-regulated metabolic dynamics\u003c/h3\u003e\n\u003cp\u003eTo further elucidate the differences in interaction patterns of QS-regulation, we conducted untargeted metabolomic analyses on each SynCom with and without the inhibition of the key metabolic pathways (QS, tryptophan metabolism, and sulfur metabolism). Significant alterations in metabolites were observed across all inhibited SynComs compared to the wild-type (Fig. S13). Among them, YKB-QSR2 (\u003cem\u003elux\u003c/em\u003eI RNAi), YKB-TMR2 (\u003cem\u003ehpa\u003c/em\u003eBA RNAi), HKB-QSR1(\u003cem\u003elux\u003c/em\u003eS RNAi), and HKB-SMR2 (\u003cem\u003ecys\u003c/em\u003eD RNAi) exhibited the greatest number of significantly differentially expressed metabolites (DEMs) (Fig. S13). KEGG categorization revealed that DEMs in interfered systems were predominantly enriched in lipid metabolism, amino acid metabolism, and biosynthesis of other secondary metabolites. Specifically, QS-inhibited systems in SynCom YKB showed greater enrichment of metabolites related to membrane transport, signaling, and cellular community (which refers to genes/proteins associated with cell\u0026ndash;cell interactions, junctions, or multicellular structures; Fig. S14). Similarly, QS-inhibited systems in SynCom HKB were notably enriched in signaling molecules and interactions, as well as signal transduction (Fig. S15).\u003c/p\u003e\u003cp\u003eWe focused on significantly down-regulated differential metabolites (DDEMs) in the inhibited systems to uncover correlations underlying functional inhibition. KEGG enrichment analysis indicated that in SynCom YKB, disruption of QS pathways markedly suppressed tryptophan metabolism, purine metabolism, sphingolipid metabolism, sphingolipid signaling, and herbicide degradation pathways, whereas interference with tryptophan metabolism had minimal impact on overall metabolic activity (Fig. S16). In SynCom HKB, QS disruption significantly reduced sulfur metabolism, glycerophospholipid metabolism, amino acid metabolism, nucleotide metabolism, and benzoxazinoid biosynthesis pathways. Metabolic network analysis of DDEMs revealed higher network complexity in YKB-QSR2, HKB-QSR1 and HKB-SMR2, indicating substantial metabolic differences between SynComs upon interference with different QS signaling pathways (Fig. S17 and S18).\u003c/p\u003e\u003cp\u003eTo further evaluate the functional importance of DDEMs across treatment groups, we performed KEGG topology analysis (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In SynCom YKB, tryptophan metabolism, atrazine degradation, and amino acid metabolism consistently played central roles. Specifically, within YKB-QSR2, aminobenzoate degradation and ABC transporters emerged as key hubs among down-regulated pathways. Compared to YKB-TMRNAi system, the AHLs-interfered system had enrichment of tryptophan metabolism and higher relative importance of key DDEMs. In SynCom HKB, atrazine degradation, sulfur metabolism, taurine and hypotaurine metabolism, and carbon metabolism were consistently prominent. In HKB-QSR2, cAMP signaling, nucleotide metabolism, and amino acid metabolism also served as critical metabolic functions. Relative to HKB-SMRNAi, AI-2 interference led to higher functional importance of suppressed metabolites within sulfur metabolism. These results further support our prior data showing that inhibition of AHL signaling in SynCom YKB and AI-2 signaling in SynCom HKB significantly inhibits overall metabolic activity and regulates multidimensional functions including herbicide degradation, amino acid metabolism, carbon metabolism, and transport. We also integrated compound classifications and enrichment levels of DEMs using metabolite set enrichment analysis (MSEA). Results revealed that DEMs included abundant herbicide intermediates and secondary metabolites, such as diazines, benzofurans, benzenes, and heteroaromatic compounds, further confirming severely inhibited herbicide degradation in interfered systems (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Additionally, YKB-QSR2 and HKB-QSR1 exhibited the greatest DEM enrichment levels, indicating that interference in these metabolic functions most strongly suppresses herbicide degradation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSynCom application influences natural soil metabolism.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA maize pot experiment was conducted to validate the metabolic impact of each SynCom in field-derived soils. Compared to the control group, both SynCom YKB and SynCom HKB significantly promoted maize growth (Fig.\u0026nbsp;3a, b). Specifically, root length increased by 90.57% and 92.24%, root fresh weight by 102.69% and 66.84%, shoot length by 76.85% and 77.46%, and shoot fresh weight by 249.56% and 219.31%, respectively (Fig.\u0026nbsp;3a). The constituent strains of each SynCom exhibited stable growth in the black soil substrate, with both communities sustaining a biomass of 10⁵ colony-forming units (CFU) 21 days after inoculation even in the presence of an endogenous microbiome (Fig. S19). All inhibited systems exhibited reduced plant-growth-promoting effects, with the most pronounced suppression observed in YKB-QSR2 and HKB-QSR1, highlighting the importance of QS for plant growth promotion. Compared to SynCom YKB, YKB-QSR2 inhibited root length, root weight, shoot length, and shoot fresh weight by 24.73%, 52.28%, 35.59%, and 66.08%, respectively. Similarly, compared to SynCom HKB, HKB-QSR1 reduced these parameters by 40.14%, 48.43%, 34.70%, and 60.19%, respectively. Moreover, both SynCom YKB and SynCom HKB significantly enhanced the activity of nutrient metabolism-related enzymes in the rhizosphere soil, improved root vitality, increased the photosynthetic rate in leaves, and elevated the content of nutritional components, further confirming their pronounced beneficial effects on maize seedling growth (Fig.\u0026nbsp;3b, c, S20).\u003c/p\u003e\u003cp\u003eAdditionally, throughout the maize growth period, both SynCom YKB and SynCom HKB efficiently degraded the endogenous herbicide pollution in these field-derived soils, with a degradation rate consistently exceeding 95%. In contrast, the herbicide degradation capacity was significantly impaired in the YKB-QSR2 and HKB-QSR1 systems, which achieved only approximately 40% degradation (Fig.\u0026nbsp;3d).\u003c/p\u003e\n\u003ch3\u003eMetagenomics reveals the regulatory mechanisms of SynCom metabolism in field-derived soils\u003c/h3\u003e\n\u003cp\u003eTo elucidate the regulatory functions and metabolic division of labor of the SynComs in a field-derived soil environment, we conducted metagenomic analyses. Compared to the control group, SynCom application increased taxonomic and functional \u003cem\u003eα\u003c/em\u003e-diversity of the soil microbiome (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Although this increase was partially attenuated upon the introduction of inhibited strains, it still remained greater than that of the control. \u003cem\u003eβ\u003c/em\u003e-diversity analysis revealed clear differences in species composition and functional profiles among treatment groups, with relatively smaller differences observed between YKB and YKB-QSR2, and between HKB and HKB-QSR1 (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Meanwhile, the normalized stochasticity ratio (NST) analysis indicated that the introduction of the SynComs shifted the bacterial community assembly from stochastic processes toward deterministic processes (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), suggesting that environmental filtering and ecological selection became the dominant forces governing community structure and stability. Furthermore, as expected, due to the membership of the assemblages, the addition of SynCom YKB significantly increased the relative abundance of \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eComamonas\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e, while SynCom HKB enrichment led to an enrichment of \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e. These trends are highly consistent with the taxonomic composition of the introduced SynComs, providing further evidence for their successful colonization (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eKEGG enrichment analysis of annotated metabolic functions across treatment groups demonstrated that the application of both SynCom YKB and HKB significantly increased the relative abundance of functional genes related to herbicide degradation, QS, carbohydrate and energy metabolism, tryptophan metabolism, and sulfur metabolism compared to the control, which were consistent with earlier findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In contrast, these key pathways were significantly down-regulated in the inhibited systems compared to the wild-type SynComs, underscoring the inhibitory effect of metabolic disruption on overall regulatory function. We further evaluated the relative contributions of bacterial taxa to these key metabolic pathways. The results indicated functional partitioning among microbial members; for example, in SynCom YKB-treated soils, \u003cem\u003eComamonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e collectively played key roles in QS, herbicide degradation, and tryptophan metabolism, while \u003cem\u003eComamonas\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e together contributed prominently to carbohydrate and energy metabolism. In SynCom HKB-treated soils, \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e were crucial for QS and herbicides degradation, whereas genus of \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e jointly dominated carbohydrate and energy metabolism. All three genera contributed significantly to central carbon and energy metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). To validate these metagenomic insights, the accumulation of key metabolites in the soil were quantified, and the abundance of key herbicide degradation proteins and QS signal molecules were quantified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c, S21). The results demonstrated that both SynComs promoted the abundance of key herbicide degradation genes and QS signal molecules, while promoting soil accumulation of SA. Additionally, SynCom YKB enhanced tryptophan accumulation, while SynCom HKB increased taurine levels. In QS-interfered systems, the levels of these key metabolites and signal molecules were significantly reduced, confirming that QS-mediated communication underpins the metabolic coordination within the SynComs.\u003c/p\u003e\u003cp\u003eCorrelation analyses were employed to investigate the relationships between soil ecological changes and bacterial taxonomic or functional characteristics. First, a significant positive correlation was observed between the concentration of herbicides in the soil, growth of maize and the taxonomic and functional microbial \u003cem\u003eβ\u003c/em\u003e-diversity, indicating consistency between functional potential and phenotypic outcomes (Fig. S22 and S23). Mantel tests revealed a significant negative correlation between metagenomic profiles of the control group and maize growth phenotypes as well as soil enzyme activities, whereas the treatment groups showed the opposite trend (Fig. S24a). Furthermore, variance partitioning analysis (VPA) confirmed that the selected environmental factors explained 30.84% and 86.37% of the variance in soil species composition and KEGG functions, respectively, underscoring a stronger deterministic and explanatory influence of environmental factors on functional traits (Fig. S24b). WGCNA was also used to identify key functional modules significantly associated with treatments and environmental factors, and yielding four major modules. MEyellow was enriched for biofilm formation and tryptophan metabolism, MEblue for QS and two-component systems, MEgreen for carbon metabolism, herbicide degradation, and signal transduction, and MEred for sulfur metabolism and glycoside metabolism (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The influence of soil species and specific functions on environmental factors was further analyzed. The results indicated that the relative abundances of genus \u003cem\u003eComamonas\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e were significantly positively correlated with soil environmental factor levels, suggesting that the SynComs collaboratively modulate soil ecological functions and maize growth alongside certain indigenous microorganisms. In contrast, the relative abundance of some native taxa, such as genus \u003cem\u003eCupriavidus\u003c/em\u003e, showed a significant negative correlation with environmental factors, potentially indicating competitive or antagonistic interactions (Extended Data Fig.\u0026nbsp;8).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMetagenome-Assembled Genomes (MAGs) reflect functional partitioning and consortia stability\u003c/h3\u003e\n\u003cp\u003eMetagenomic data from the SynCom YKB and HKB treatment groups were assembled and binned, yielding 28 MAGs (Extended Data Fig.\u0026nbsp;9). KEGG annotation identified four MAGs (MAG12, MAG46, MAG57, and MAG82) with over 99% genomic similarity to the SynCom strains Y1, K1, B1, and H1, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Analysis of MAG proportions revealed that the SynCom YKB treatment group was enriched with MAG12, MAG46, and MAG57, while the HKB group was enriched with MAG46, MAG57, and MAG82, which was consistent with the taxonomic makeup of the respective SynComs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Moreover, the relative abundance of these four target MAGs was significantly positively correlated with soil environmental factors, further supporting their putative key roles in regulating soil functionality. KEGG functional annotation of the four MAGs revealed substantial involvement in signal transduction, transcription, amino acid metabolism, energy metabolism, and carbohydrate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Specifically, MAG12 was most prominently associated with signal transduction and energy production/conversion; MAG46 primarily regulated membrane biogenesis and amino acid metabolism; MAG57 was responsible for amino acid metabolism and carbohydrate transport/metabolism; while MAG82 dominated signal transduction and DNA repair, which illustrated a clear functional partitioning and cooperation among members of the different SynComs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSynComs represent a promising strategy for the comprehensive utilization of beneficial microorganisms in sustainable agriculture. Numerous studies have demonstrated the significant potential of SynComs in the bioremediation of pollutants, with different consortia exhibiting distinct functional traits and degradation mechanisms\u003csup\u003e\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. For instance, a synthetic co-culture of \u003cem\u003ePseudomonas\u003c/em\u003e sp. ADP and \u003cem\u003eArthrobacter\u003c/em\u003e sp. degraded 95% of atrazine at an initial concentration of 100 mg/L within just 7 days under laboratory conditions\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. However, most currently constructed SynComs only address the remediation of single herbicide types, overlooking the practical issue of combined herbicide contamination\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Moreover, it is essential to investigate the interactions among SynCom members and elucidate their regulatory roles in natural ecosystems to clarify communication and division of labor, thereby addressing common application challenges such as insufficient synergy or functional failure\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Our previous work demonstrated that SynComs could simultaneously degrade multiple herbicides while enhancing soil microbial diversity and carbon metabolism\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The present study builds on that foundation by uncovering the regulatory basis of this cooperative activity, showing that distinct quorum-sensing systems can orchestrate division of labor in different SynComs. This shift from functional demonstration to mechanistic understanding provides a framework for more rational design of synthetic communities for bioremediation. We constructed two SynComs, YKB and HKB, by a top-down approach. Guided by the Design-Build-Test-Learn (DBTL) framework, we employed multi-omics analyses, cross-validation, and pot experiments to reveal interaction models and regulatory mechanisms mediated by QS signaling and facilitated by downstream metabolic pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWe first uncovered the metabolic division of labor among members within SynCom YKB and SynCom HKB.\u003c/b\u003e In SynCom YKB, strain K1 serves as the primary degrader of herbicides, strain Y1 acts as an energy supplier, while strain B1 functions as a \u0026ldquo;clean-up\u0026rdquo; agent that further metabolizes intermediate products generated by strain K1 via tryptophan metabolism, ultimately yielding acetyl-CoA, which enters the TCA cycle to supply additional energy to the system. Similarly, in SynCom HKB, strain H1 mitigates cytotoxicity through DNA repair, while strains K1 and B1 collaboratively perform the main herbicide degradation tasks. Additionally, strains H1 and B1 activate their sulfur metabolism pathways, forming an electron transport chain linked to herbicides degradation that promotes and regulates redox processes while supplying energy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). \u003cb\u003eNotably, we demonstrated that the same microbial strain can perform significantly different metabolic roles across different SynComs systems.\u003c/b\u003e Herbicides degradation and resistance assays revealed that SynCom YKB exhibited simultaneous enhancement in both degradation efficiency and resistance, whereas resistance in SynCom HKB did not increase alongside its degradation capacity. This observation led us to hypothesize that distinct regulatory mechanisms might govern community interactions and degradation processes in the two systems. Proteomic analysis of differentially expressed proteins in each member supported this hypothesis: strains indeed activate different interaction patterns depending on their consortium context. While strain K1 showed minimal functional variation between the two systems, serving consistently as a primary degrader, strain B1 displayed remarkable functional divergence. In the SynCom YKB system, it acted as a helper strain, facilitating downstream degradation and tryptophan metabolism. However, in the HKB system it functioned as a key degrader, primarily mediating herbicides breakdown and sulfur metabolism. Previous studies indicate that functional adaptation of strains within SynComs is influenced by environmental factors, microbial interactions, and signaling molecules\u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Thus, we proposed that the observed functional divergence of strain B1 may be attributed to: 1. Different signaling molecules mediating inter-strain interactions in the two systems, leading to early divergence in metabolic division of labor; 2. In SynCom YKB, lower herbicides-induced cytotoxicity allows strain K1, supported energetically by strain Y1, to efficiently initiate degradation. Consequently, strain B1 only need to undertake the downstream processing within a stable partnership; 3. In SynCom HKB, higher cytotoxicity necessitates rapid joint degradation by both strain B1 and K1 to mitigate systemic damage.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWe elucidated the regulatory role of QS in mediating interactions within the two SynComs.\u003c/b\u003e Exogenous metabolite complementation demonstrated that QS signal molecules enhance both the expression of key enzymes involved in herbicides degradation and the overall degradation capability of the SynComs. Notably, QS signal molecules also up-regulate key genes in downstream metabolic pathways and modulate the accumulation of critical metabolites. Furthermore, interference with QS signaling resulted in the most pronounced suppression of both degradation function and downstream metabolic activities, underscoring the essential role of QS in establishing microbial interactions, regulating metabolic division of labor, and maintaining functional stability. QS signal molecules accumulate with increasing bacterial density and, upon reaching a threshold concentration, trigger intracellular signal transduction via ligand-receptor interactions, ultimately leading to the activation or repression of specific genes\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Numerous studies focusing on bacterial isolates have documented QS-mediated behaviors\u0026mdash;such as coordinated population activities, biofilm formation, regulation of virulence and pathogenicity, and control of growth and metabolism\u0026mdash;providing compelling evidence for the role of QS in bacterial collective behavior\u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Consistent with previous reports, SynComs in this study exhibited significant up-regulation of QS-related pathways compared to axenic cultures, directly correlating with enhanced metabolic and degradative functions. Prior research also has confirmed that QS signaling regulates microbial biodegradation of pollutants. For instance, several studies have leveraged AHLs-mediated QS systems to improve pollutant removal in wastewater treatment processes\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Hu et al. reported that the addition of 5 nM AHLs significantly enhanced biofilm activity in a sequencing biofilm batch reactor (SBBR), increasing COD removal by approximately 7%\u003csup\u003e47\u003c/sup\u003e. Valle et al. demonstrated that AHLs promote the abundance of phenol-degrading bacteria in wastewater\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and Zhou et al. found that QS facilitates bacterial degradation of the antibiotic florfenicol\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. More importantly, our study reveals distinct QS regulatory patterns between the two SynComs: in SynCom YKB, AHLs-based signaling predominates, whereas AI-2 plays a major role in regulating metabolic functions in SynCom HKB. AHLs are among the most common autoinducers (AIs), produced by over 100 species of \u003cem\u003eProteobacteria\u003c/em\u003e, including numerous Gram-negative bacteria, and are centrally governed by the LuxI/LuxR system\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. They are widely reported to regulate intraspecies communication, biofilm morphogenesis, EPS production, and bacterial activities\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Meanwhile, AI-2 operates in the QS systems of both Gram-positive and Gram-negative bacteria (over 60 species) and is considered pivotal in inter-species communication\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003elux\u003c/em\u003eS gene is recognized as the hallmark AI-2 synthase. Structural analyses of LuxS proteins across bacteria reveal a conserved homodimeric zinc-binding metalloprotein structure, with conserved residues His54, His58, and Cys126 forming the active site\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. This structural conservation suggests that AI-2 molecules may be similarly recognized across species\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Accordingly, as all three members of SynCom YKB are Gram-negative and encode intact LuxI/LuxR systems, AHL signaling dominates QS regulation in this system. While in SynCom HKB, which includes both Gram-positive (Genus of \u003cem\u003eBacillus\u003c/em\u003e) and Gram-negative strains, AI-2 is highly activated and facilitates inter-strain communication. Interestingly, this divergence in QS signals suggests distinct communication patterns: in SynCom YKB, intra-species signaling may prevail, where metabolic division of labor is maintained via feedback through secreted metabolites; whereas in SynCom HKB, robust inter-species signaling enables high-level communication that coordinates differential metabolic expression among members as cell density increases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQS not only regulates the microbial degradation of herbicides but also activates distinct downstream metabolic pathways that facilitate the further breakdown of herbicide-derived intermediates.\u003c/b\u003e Metabolomic analyses revealed that inhibition of QS signaling significantly down-regulated the activity of multiple downstream pathways, including amino acid metabolism, sulfur metabolism, carbon metabolism, and nucleotide metabolism, underscoring the central regulatory role of QS in the metabolic function of the microbial consortium. In SynCom YKB, disruption of the AHLs-mediated QS system led to a loss of activity in the tryptophan metabolic pathway, whereas supplementation with exogenous C6-HSL, a key signaling molecules of AHLs, restored the accumulation of key metabolites in this pathway, confirming the positive regulatory role of AHLs in tryptophan metabolism. Previous studies have shown that the tryptophan metabolic pathway mediates the complete degradation of organic pollutants such as herbicides\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Tryptophan metabolism involves multiple hydrolytic-oxidoreductive reactions (e.g., the iaa pathway) and decarboxylative degradation routes (e.g., the iac pathway), serving as a hub for the biodegradation of catechol, benzoate, aminobenzoate, and nicotinamide metabolism\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. This process facilitates the complete conversion of herbicide intermediates into acetyl-CoA. Consequently, tryptophan metabolism not only promotes thorough herbicide degradation but also generates additional acetyl-CoA, which feeds into glycolysis, pyruvate metabolism, and the TCA cycle to supply energy to the microbial community\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Meanwhile, in SynCom HKB, AI-2-mediated inter-species communication significantly enhanced sulfur metabolic activity. Similarly, QS interference resulted in the loss of downstream metabolic function, which was restored upon AI-2 supplementation. \u003cb\u003eNumerous studies have established a close relationship between sulfur metabolism and herbicide degradation\u003c/b\u003e, primarily manifested in the following aspects: 1. Involvement of key sulfur metabolism genes. For example, the sulfur metabolism-related gene CysJ has been demonstrated to participate in the degradation of chlorimuron-ethyl by \u003cem\u003eChenggangzhangella methylocysteaceae\u003c/em\u003e, suggesting that sulfur metabolism may supply energy or intermediate products that assist in herbicide breakdown\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. 2. Sulfate generation may alter soil chemical properties, influencing herbicide adsorption, mobility, or microbial activity, thereby indirectly promoting or inhibiting herbicide degradation\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. 3. The sulfate reduction pathway can form a complete electron transport chain with herbicide redox reactions, thereby accelerating herbicide degradation\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. In summary, within SynCom HKB, AI-2-mediated up-regulation of sulfur metabolism promotes herbicides degradation and generates substantial bioactive compounds such as acetyl-CoA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThis study also provided a deeper understanding of SynCom YKB and SynCom HKB on regulatory mechanisms within the black soil ecosystem.\u003c/b\u003e The SynComs significantly enhanced maize growth and promoted both taxonomic and functional diversity in the soil, likely attributable to reduced herbicides toxicity and improved soil ecological conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The shift in bacterial community assembly toward deterministic processes following inoculation further confirms that SynComs are key drivers of soil microbial structure and function. Notably, such beneficial effects were markedly inhibited when the key metabolic pathways described above were disrupted. In particular, interference with QS severely impaired the functionality in the black soil ecosystem, with AHLs and AI-2 playing predominant roles in SynCom YKB and SynCom HKB, respectively. QS not only regulated interactions within the SynComs but also plays a central role in mediating interactions between the inoculants and indigenous soil microorganisms. Metagenomic analyses revealed that the SynComs enhanced key soil metabolic functions, including herbicides degradation, carbohydrate and energy metabolism, and QS. \u003cb\u003eDifferent members occupied distinct ecological niches across key functions, highlighting the division of labor and communication within the constructed communities.\u003c/b\u003e Compared to single-strain applications, the SynComs exhibited greater ecological stability and adaptive capacity to changing environmental and physicochemical conditions\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Furthermore, MAGs further elucidated the functional partitioning among members in the natural soil environment. Consistent with in vitro findings, all four strains significantly activated signal transduction pathways. Strain Y1 primarily functioned as an energy producer and transporter, strains K1 and B1 were responsible for organic compound metabolism and transfer, and strain H1 dominated DNA repair and carbon metabolism activities. It is precisely the well-coordinated division of labor and functional complementarity that underpins the stable interactions and highly efficient degradation/regulatory performance of the SynComs in the natural ecosystem.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003eMedia, primers, and chemical standards\u003c/h2\u003e\n \u003cp\u003eA 1/10 LB medium was used throughout this study for bacterial isolation/screening, strain cultivation, and functional validation. The medium contained 1 g tryptone, 0.5 g yeast extract, and 1 g NaCl per liter, and adjusted to pH 7.2. For solid plates, 18 g/L agar was added. All media were sterilized by autoclaving at 121\u0026deg;C for 15 minutes. The primers used in this study are listed in Table S9. Analytical standards of atrazine (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%), acetochlor (purity\u0026thinsp;\u0026gt;\u0026thinsp;99%), butachlor (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%), metolachlor (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%), metribuzin (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%), nicosulfuron (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%), pyrazosulfuron (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%), and fomesafen (purity\u0026thinsp;\u0026gt;\u0026thinsp;98%) (Table S10) were purchased from the Pesticide Research Institute (Shanghai, China). Stock solutions of each herbicide (10 g/L) were prepared in HPLC-grade methanol. All other chemicals were of analytical grade and obtained from Scrbio Biotechnology Co., Ltd. (Shanghai, China).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil sampling and strain enrichment\u003c/h2\u003e\n \u003cp\u003eSix sampling sites were selected within the typical black soil region of Northeast China, covering Liaoning, Jilin, and Heilongjiang provinces. Sites included Changtu (CT), Kuancheng (KC), Acheng (AC), Beilin (BL), Hailun (HL), and Beian (BA) (Table S2). Soil sampling and basic climatic information for the black soil were listed in the supplementary materials.\u003c/p\u003e\n \u003cp\u003eFor strain enrichment, 10 g of soil was inoculated into 100 mL of sterile water supplemented with 50 mg/L each herbicides, and incubated at 28\u0026deg;C for 5 days. Enriched microbial communities were isolated using the dilution plate count method. Soil suspensions were allowed to settle for 15 minutes at room temperature, then serially diluted from 10⁻\u0026sup1; to 10⁻⁶ using sterile water. Aliquots (100 \u0026micro;L) of dilutions between 10⁻\u0026sup3; and 10⁻⁶ were spread onto 1/10 LB agar plates. Each dilution was plated in triplicate. Plates were incubated inverted at 30\u0026deg;C and monitored regularly for colony formation. Isolated colonies were purified by streaking onto fresh solid medium.\u003c/p\u003e\n \u003cp\u003eStrain identification was based on morphological characteristics (For details, see the supplementary materials) and 16S rRNA gene sequencing. Genomic DNA was extracted using the method described by Marmur (1961)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. The 16S rRNA gene was amplified with universal primers 27F and 1492R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e and sequenced by Tsingke Biotechnology (Beijing, China). Sequences were analyzed using the GenBank BLAST program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/BLAST\u003c/span\u003e\u003c/span\u003e). A neighbor-joining (NJ) phylogenetic tree was constructed, and branch stability was assessed using bootstrap analysis with 1,000 replicates\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. The pure culture strains were stored frozen at -80\u0026deg;C with glycerol (25%, w/v) as preservative.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation of strain resistance and degradation capacity\u003c/h2\u003e\n \u003cp\u003eTo assess herbicides resistance, various concentrations of herbicides were added to 1/10 LB medium, mixed thoroughly, and poured into plates. A 10 \u0026micro;L aliquot of bacterial suspension was spotted onto the plates, which were then incubated inverted at 28\u0026deg;C for 2 days. Bacterial growth was observed and recorded, and the maximum herbicides concentration allowing normal growth was determined as the minimum inhibitory concentration (MIC)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. For SynComs, strains were mixed in equal volumes, and 10 \u0026micro;L of the mixture was used for resistance testing.\u003c/p\u003e\n \u003cp\u003eHPLC-MS was employed to determine the content of herbicides, with specific operational details provided in the supplementary materials\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSingle herbicide degradation assay\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStrains were pre-cultured overnight in 5 mL of 1/10 LB medium at 28\u0026deg;C with shaking at 150 rpm. Then, 1 mL of the culture (OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.8) was inoculated into 100 mL of 1/10 LB medium containing 100 mg/L of individual herbicides and incubated for 60 h. For SynComs, a 1% inoculum (v/v) consisting of equal proportions of each strain was used.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMixed herbicide degradation assay\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA mixture of eight herbicides was prepared at final concentrations of 50, 100, 150, and 200 mg/L each in 100 mL of 1/10 LB medium. Then, 1 mL of pre-cultured seed culture (OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.8) was inoculated into the mixed herbicide medium and incubated at 28\u0026deg;C with shaking at 150 rpm for 60 hours. Samples were taken every 12 hours to monitor bacterial growth and residual herbicide content. The total residual herbicide concentration was defined as the sum of the concentrations of all eight herbicides. Degradation kinetics were modeled using a first-order reaction model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eProteomic processing and analysis\u003c/h2\u003e\n \u003cp\u003eStrains Y1, K1, B1, and H1 were cultured individually and in co-culture in 1/10 LB medium containing 100 mg/L of mixed herbicides for 36 hours. Cells were harvested by centrifugation (12,000 rpm, 4\u0026deg;C), and the pellets were lyophilized. Protein extraction was performed using the Majorbio Microprotein Kit (details in the supplementary materials). Protein concentration was determined by the Bradford method. Equal amounts of peptides were dissolved in mass spectrometry loading buffer and analyzed by data-independent acquisition (DIA). Raw DIA data were processed using Spectronaut\u0026trade; 19 for database searching. The specific parameters are compiled in the supplementary materials. Separate and combined protein databases of the four strains were used in this study.\u003c/p\u003e\n \u003cp\u003eFunctional annotation of differentially expressed proteins was conducted on the Gene Ontology (GO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003c/span\u003e) across three categories: biological process, cellular component, and molecular function. Metabolic pathway analysis was performed using the KEGG (Kyoto Encyclopedia of Genes and Genomes, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003c/span\u003e) and COG (Cluster of Orthologous Groups) databases. PPI networks were constructed on STRING version 11.5.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eReal-time quantitative PCR and RNA interference technology\u003c/h2\u003e\n \u003cp\u003eReal-time quantitative PCR (RT-qPCR) was used to examine the expression levels of key genes associated with herbicides degradation, QS, biofilm synthesis, energy metabolism, tryptophan metabolism, and sulfur metabolism\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Strains were inoculated at a total initial concentration of 1% into 1/10 LB medium and cultured at 28\u0026deg;C with shaking at 150 rpm for 36 h. Cell pellets were collected for subsequent RNA extraction and RT-qPCR analysis. Total RNA was extracted using TRIzol reagent (Invitrogen) (details in the supplementary materials). The cDNA was diluted 10-fold and subjected to RT-qPCR analysis by SYBR Green real-time PCR master mix (Toyobo). Amplification was carried out on an iQ5 real-time PCR detection system (Bio-Rad, USA), and data were analyzed using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;CT\u003c/sup\u003e method. Quantitative RT-qPCR was performed on an ABI ViiA7 instrument using 0.1 mL fast optical 96-well reaction plates (Applied Biosystems).\u003c/p\u003e\n \u003cp\u003eDue to the broad antibiotic resistance profiles of strains Y1, K1, B1, and H1, RNA interference (RNAi) was employed for targeted gene silencing\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. A strong Tac promoter\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e and gene-specific silencing fragments were cloned into the plasmid pUC19, which carries a red fluorescent protein (RFP) marker (Fig. S25)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. RNAi targets were designed against specific genes of interest in each strain; primers used for amplifying target genes are listed in Table S9. The target fragments were inserted into the \u003cem\u003elacZ\u003c/em\u003e site of the plasmid via Hind III and Sac I restriction sites. Recombinant plasmids were then introduced into the respective strains via electroporation. Wild-type strains without plasmids formed white colonies with no red fluorescence on LB plates. Strains carrying the empty vector exhibited red fluorescence and blue colonies, while RNAi strains with inserted target fragments showed white colonies with red fluorescence. The strains were iteratively cultured for five generations, and RT-qPCR confirmed stable silencing of the target genes in the RNAi strains. Phenotypic validation of RNAi strains and stability testing of gene interference are presented in Fig. S26. Detailed information on all constructed interference strains is summarized in Table S6.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eDetection of key metabolites and reporter gene assays\u003c/h2\u003e\n \u003cp\u003eBiofilm content was determined by the method of crystal violet staining, with absorbance measured at a wavelength of 595 nm (details in the supplementary materials)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. ATP and NADH were analyzed using a high-performance liquid chromatography system (HPLC, 2690 series, Waters, USA) with a mobile phase consisting of 90% 50 mM phosphate buffer, 10% acetonitrile, and 3.22 g/L tetrabutylammonium bromide (pH 6.8), with a flow rate of 1 .0 mL/min\u003csup\u003e86\u003c/sup\u003e. SA, trp, and tau were also measured via HPLC under the following conditions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e: 1. SA: Mobile phase\u0026mdash;acetonitrile : deionized water : acetic acid (25:75:5, v/v); flow rate: 1.0 mL/min; detection wavelength: 303 nm. 2. Tryptophan: Mobile phase\u0026mdash;acetonitrile : phosphate buffer (87:13); flow rate: 1.0 mL/min; detection wavelength: 280 nm. 3. Taurine: Mobile phase\u0026mdash;0.05 mol/L potassium dihydrogen phosphate solution (pH adjusted with phosphoric acid) : methanol (1:1); flow rate: 1.0 mL/min; detection wavelength: 240 nm.\u003c/p\u003e\n \u003cp\u003eReporter gene construction and \u003cem\u003e\u0026beta;\u003c/em\u003e-Galactosidase assay: The promoter-recA sequence was amplified by PCR and cloned into the EcoRI-BamHI site of the plasmid pLSP-kt2lacZ, resulting in the recombinant plasmid pLSP-promoter-recA. The construct was verified by PCR and sequencing (Fig. S27)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. The plasmid was then transformed into the target strains, which were cultured in 1/10 LB medium. \u003cem\u003e\u0026beta;\u003c/em\u003e-Galactosidase activity was measured every 12 hours across different experimental systems.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eMetabolomic analysis\u003c/h2\u003e\n \u003cp\u003eWild-type and RNAi strains of Y1, K1, B1, and H1 were co-cultured in 1/10 LB for 48 h. Then, 100 \u0026micro;L of culture was transferred to a 1.5 mL centrifuge tube, mixed with 400 \u0026micro;L of extraction solvent (acetonitrile:methanol\u0026thinsp;=\u0026thinsp;1:1, v/v) containing four internal standards (including L-2-chlorophenylalanine at 0.02 mg/mL), and vortexed for 30 seconds. Metabolomics preprocessing and specific parameters are compiled in the supplementary materials\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eRaw data were processed using the ropls package (v1.6.2) for multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). A 7-round cross-validation was applied to evaluate model robustness. Significantly altered metabolites were identified based on a variable importance in projection (VIP) value\u0026thinsp;\u0026gt;\u0026thinsp;1 from the OPLS-DA model and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from Student\u0026rsquo;s t-test. Metabolic pathway annotation of differential metabolites was performed using the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/kegg/pathway.html\u003c/span\u003e\u003c/span\u003e). Pathway enrichment analysis was conducted using the scipy.stats package in Python, and the most relevant biological pathways were identified via Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eMaize pot experiment\u003c/h2\u003e\n \u003cp\u003eTo distinguish the target strains from indigenous microorganisms, recombinant strains labeled with red fluorescent protein (RFP) were constructed. The plasmid pTn7-RFP was kindly provided by Dr. Tim Tolker-Nielsen (Fig. S28). Transformants of Y1::pTn7RFP, K1::pTn7RFP, B1::pTn7RFP, and H1::pTn7RFP were selected using LB agar containing 50 \u0026micro;g/mL ampicillin\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. After incubation at 28\u0026deg;C for 2 days, fluorescent colonies were visualized using a ChemiDoc MP imaging system.\u003c/p\u003e\n \u003cp\u003eEach target strain was inoculated at 1% (v/v) into 1/10 LB liquid medium and cultured at 28\u0026deg;C for 36 h. Cells were harvested by centrifugation (5,000 rpm, 20 minutes) and resuspended in sterile water to an OD\u003csub\u003e600=\u003c/sub\u003e0.5. Maize seeds were surface-sterilized by soaking in sterile water for 20 h, treated with 75% ethanol for 30 seconds, followed by 1% sodium hypochlorite for 30 min, and rinsed 4\u0026ndash;5 times with distilled water. Seeds were germinated in vermiculite, and after 7 days, uniformly sized seedlings were selected and transplanted into black soil\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe pot experiment used air-dried and sieved black soil collected from the Modern Agricultural Demonstration Base in Minzhu Township, Harbin. Basic physicochemical properties of the soil are provided in Table S11. Each plastic pot (11 cm in diameter) was filled with 1.0 kg of soil and watered to maintain 60% of the field water holding capacity. After equilibration for one day, one maize seedling was transplanted per pot. Plants were grown under controlled conditions: 25\u0026deg;C, with a photoperiod of 14 h of light and 10 h of darkness. The light intensity during the photoperiod was 600 \u0026micro;mol photons\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;\u003csup\u003e89\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe experimental treatments were as follows: control (CK, receiving sterile water only), synthetic consortium groups (SynCom YKB and SynCom HKB), and interference strain groups (YKB-QSR1, YKB-QSR2, YKB-TMR1, YKB-TMR2, HKB-QSR1, HKB-QSR2, HKB-SMR1, HKB-SMR2). Each treatment included four replicates. A total of 15 mL of bacterial suspension was applied per pot (5 mL per strain in consortia treatments). Plants were irrigated with 250 mL of water every 5 days. Sampling was conducted 21 days after inoculation. The specific testing methods for maize growth-related indicators and soil enzymes were compiled in the supplementary materials.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eMetagenomic Analysis\u003c/h2\u003e\n \u003cp\u003eTotal DNA from soil samples was subjected to whole-genome sequencing with three biological replicates per condition (details in the supplementary materials). Raw sequencing reads were quality-controlled and assembled using FASTp (v0.24.2)\u003csup\u003e90\u003c/sup\u003e and MEGAHIT (v1.1.2)\u003csup\u003e91\u003c/sup\u003e, respectively. Gene prediction was performed with MetaGene, and sequence clustering and alignment were carried out using CD-HIT (v4.6.1)\u003csup\u003e92\u003c/sup\u003e and SOAPaligner (v2.21)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Contigs derived from metagenomic assembly were binned on a per-sample basis. Binning, consolidation, and refinement were conducted using MetaBAT2, MaxBin2, and CONCOCT, followed by bin refinement, quality assessment, taxonomic annotation, and selection of medium-quality MAGs. The amino acid sequences of the non-redundant gene catalog were aligned against the NR database using BLASTP implemented in DIAMOND (v2.0.13; e-value\u0026thinsp;\u0026le;\u0026thinsp;1e⁻⁵). Taxonomic annotation was assigned based on the corresponding classification information from the NR database, and species abundance was estimated from the summed abundance of genes assigned to each taxon. Non-redundant gene sequences were also aligned against the KEGG, COG, and Quorum Sensing databases to annotate functional traits, KEGG orthologous pathways, EC numbers, and functional modules. Functional abundance profiles were derived from these annotations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll bacterial 16S and genome sequencing data reported in this paper were deposited in GitHub\u0026nbsp;https://github.com/ZhangY306/raw-data-for-QS. All raw data analyzed in this studu were listed in the\u0026nbsp;Supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2022YFD1500202), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA28020202), the National Natural Science Foundation of China (42407176), the Jiangsu Youth Foundation (BK20241701), the Self-Deployment Program of Nanjing Soil Research Institute of Chinese Academy of Sciences (ISSAS2402), the Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB437), and the China Postdoctoral Science Foundation (2024M753333).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.C. and Y.Z. conceptualized and designed the research. Y.Z., L.N., M.Y.Z, P.H.,W.L., X.X., R.S., performed the research. Y.Z., X.L. and H.C analyzed the data. H.C. supervised the study. Y.Z., J.A.G., and H.C. wrote the paper. H.C., J.A.G., K.S., H.X.X., N.Z. and J.Z\u0026nbsp;reviewed the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXuan F et al (2023) Mapping crop type in Northeast China during 2013\u0026ndash;2021 using automatic sampling and tile-based image classification. Int J Appl Earth ObS 117\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang HY, Sun LY, Qi DL, Cai QG (2012) Using 137Cs technique to quantify soil erosion and deposition rates in an agricultural catchment in the black soil region, Northeast China. Geomorphology 169\u0026ndash;170:142\u0026ndash;150\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu XZ, Xu Y, Chen SC, Xu SG, Zhang HW (2010) Soil loss and conservation in the black soil region of Northeast China: a retrospective study. Environ Sci Policy 13(8):793\u0026ndash;800\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y et al (2014) Interaction between microbes DNA and atrazine in black soil analyzed by spectroscopy. CLEAN Soil Air Water 43(6):867\u0026ndash;871\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y et al (2017) Characterization of spectral responses of dissolved organic matter (DOM) for atrazine binding during the sorption process onto\u0026ensp;black\u0026ensp;soil. Chemosphere 180:531\u0026ndash;539\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRotich HK, Zhang ZY, Zhao YS, Li JC (2004) The adsorption behavior of three organophosphorus pesticides in peat and soil samples and their degradation in aqueous solutions at different temperatures and pH values. Int J Environm Anal Chem 84:289\u0026ndash;301\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRama Krishna K, Philip L (2008) Adsorption and desorption characteristics of lindane, carbofuran and methyl parathion on various Indian soils. J Hazard Mater 160:559\u0026ndash;567\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Z et al (2023) Improved method to characterize leaf surfaces, guide adjuvant selection, and improve glyphosate efficacy. J Agric Food Chem 71(3):1348\u0026ndash;1359\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichardson JR, Fitsanakis V, Westerink RHS, Kanthasamy AG (2019) Neurotoxicity of pesticides. Acta Neuropathol 138(3):343\u0026ndash;362\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBast A, Semen KO, Drent M (2021) Pulmonary toxicity associated with occupational and environmental exposure to pesticides and herbicides. Curr Opin Pulm Med 27(4):278\u0026ndash;283\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y et al (2025) SynCom-mediated herbicide degradation activates microbial carbon metabolism in soils. \u003cem\u003eiMeta\u003c/em\u003e e70058\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRabelo JS, Santos EAD, Melo EI, Marcal G, Vieira Vaz M, Mendes GO (2023) Tolerance of microorganisms to residual herbicides found in eucalyptus plantations. Chemosphere 329:138630\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoly P, Bonnemoy F, Charvy GC, Bohatier J, Mallet C (2013) Toxicity assessment of the maize herbicides S-metolachlor, benoxacor, mesotrione and nicosulfuron, and their corresponding commercial formulations, alone and in mixtures, using the Microtoxtest. Chemosphere 93:2444\u0026ndash;2450\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraga RR et al (2016) Effect of growing Brachiria brizantha on phytoremediation of picloram under different pH environments. Ecol Eng 94:102\u0026ndash;106\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagner M, Mikola J, Saloniemi I, Saikkonen K, Helander M (2019) Effects of a glyphosate-based herbicide on soil animal trophic groups and associated ecosystem functioning in a northern agricultural field. Sci Rep 9(1):8540\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGfrerer M et al (2002) Occurrence of triazines in surface and drinking water of Liaoning Province in eastern China. J Biochem Biophys Methods 53(1\u0026ndash;3):217\u0026ndash;228\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu XF et al (2018) Herbicide accumulations in the Xingkai lake area and the use of restored wetland for agricultural drainage treatment. Eco Eng 120:260\u0026ndash;265\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi RG, Lv JG, Feng JM (2011) Assessment of pesticide pollution in suburban soil in south Shenyang, China. Bull Environ Contam Toxicol 87(5):567\u0026ndash;573\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang XC, Liu QL (2020) Spatial and temporal distribution characteristics of triazine herbicides in typical agricultural regions of Liaoning, China. Bull Environ Contam Toxicol 105(6):899\u0026ndash;905\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Fu L, Jin C, Meng Z, Yang N (2019) Study on the isolation of two atrazine-degrading bacteria and the development of a microbial agent. Microorganisms 7:80\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWahlaa AQ, Anwara S, Muellerc JA, Arslanc M, Iqbal S (2020) Immobilization of metribuzin degrading bacterial consortium MB3R on biochar enhances bioremediation of potato vegetated soil and restores bacterial community structure. J Hazard Mater 390:121493\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J et al (2022) Construction and comparison of synthetic microbial consortium system (SMCs) by non-living or living materials immobilization and application in acetochlor degradation. J Hazard Mater 438:129460\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaron E, Lafrance P, Auclair JC (2010) Impact of grass and grass with poplar buffer strips on atrazine and metolachlor losses in surface runoff and subsurface infiltration from agricultural plots. J Environ Qual 39:617\u0026ndash;629\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzewczyk R, R\u0026oacute;żalska S, Mironenka J, Bernat P (2020) Atrazine biodegradation by mycoinsecticide \u003cem\u003eMetarhizium robertsii\u003c/em\u003e: Insights into its amino acids and lipids profile. J Environ Manage 262:11030\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang J et al (2022) Toxicological effects, environmental behaviors and remediation technologies of herbicide atrazine in soil and sediment: a comprehensive review. Chemosphere 307(Pt 3):136006\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaceres-Jensen L et al (2020) Nicosulfuron sorption kinetics and sorption/desorption on volcanic ash-derived soils: Proposal of sorption and transport mechanisms. J Hazard Mater 385:121576\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong J (2023) Current insights into the microbial degradation of nicosulfuron: Strains, metabolic pathways, and molecular mechanisms. Chemosphere 326:138390\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDu R et al (2021) Construction of a synthetic microbial community for the biosynthesis of volatile sulfur compound by multi-module division of labor. Food Chem 347:129036\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohns NI, Blazejewski T, Gomes AL, Wang HH (2016) Principles for designing synthetic microbial communities. Curr Opin Microbiol 31:146\u0026ndash;153\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJing J, Garbeva P, Raaijmakers JM, Medema MH (2024) Strategies for tailoring functional microbial synthetic communities. ISME J 18(1):wrae049\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrosskopf T, Soyer OS (2014) Synthetic microbial communities. Curr Opin Microbiol 18(100):72\u0026ndash;77\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScott SR et al (2017) A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis. Nat Microbiol 2:17083\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD'Souza G et al (2018) Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep 35(5):455\u0026ndash;488\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoyte KZ, Schluter J, Foster KR (2015) The ecology of the microbiome: Networks, competition, and stability. Science 350(6261):663\u0026ndash;666\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGould AL et al (2018) Microbiome interactions shape host fitness. \u003cem\u003eProc. Natl. Acad. Sci. USA\u003c/em\u003e 115(51), E11951-E11960\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMallon CA et al (2015) Resource pulses can alleviate the biodiversity-invasion relationship in soil microbial communities. Ecology 96(4):915\u0026ndash;926\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNadell CD, Drescher K, Foster KR (2016) Spatial structure, cooperation and competition in biofilms. Nat Rev Microbiol 14:589e600\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVillamizar S, Ferro JC, Caicedo JA, Alves L (2020) Bactericidal effect of entomopathogenic bacterium \u003cem\u003ePseudomonas entomophila\u003c/em\u003e against \u003cem\u003eXanthomonas citri\u003c/em\u003e reduces citrus canker disease severity. Front Microbiol 11:1431\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFuqua WC, Winans SC, Greenberg EP (1994) Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators. J Bacteriol 176(2):269\u0026ndash;275\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValle A, Bailey MJ, Whiteley AS, Manefield M (2004) N-acyl-l-homoserine lactones (AHLs) affect microbial community composition and function in activated sludge. Environ Microbiol 6:424e433\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChong G, Kimyon O, Rice SA, Kjelleberg S, Manefield M (2012) The presence and role of bacterial quorum sensing in activated sludge. Microb Biotechnol 5:621e633\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan CH et al (2014) The role of quorum sensing signalling in EPS production and the assembly of a sludge community into aerobic granules. ISME J 8:1186e1197\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTarighi S, Taheri P (2011) Different aspects of bacterial communication signals. World J Microbiol Biotechnol 27:1267e1280\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSturme MHJ et al (2002) Cell to cell communication by autoinducing peptides in grampositive bacteria. Anton Leeuw 81:233e243\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiggle SP, Cornelis P, Williams P, Camara M (2006) 4-Quinolone signalling in \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e: old molecules, new perspectives. Int J Med Microbiol 296:83e91\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee VT et al (2007) A cyclic-di-GMP receptor required for bacterial exopolysaccharide production. Mol Microbiol 65:1474\u0026ndash;1484\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu H et al (2016) Biofilm activity and sludge characteristics affected by exogenous N-acyl homoserine lactones in biofilm reactors. Bioresour Technol 211:339\u0026ndash;347\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Q et al (2021) Impact of acyl-homoserine lactones on the response of nitrogen cycling in sediment to florfenicol stress. Sci Total Environ 785:147294\u0026ndash;147304\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X et al (2020) Immobilization of Cd using mixed \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eComamonas\u003c/em\u003e bacterial reagents in pot experiments with \u003cem\u003eBrassica rapa L\u003c/em\u003e. Environ Sci Technol 54(24):15731\u0026ndash;15741\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePurnomo AS, Sariwati A, Kamei I (2020) Synergistic interaction of a consortium of the brown-rot fungus \u003cem\u003eFomitopsis pinicola\u003c/em\u003e and the bacterium \u003cem\u003eRalstonia pickettii\u003c/em\u003e for DDT biodegradation. Heliyon 6(6):e04027\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia X, He Y, Jiang D, Liu C, Lu W (2019) Construction and analysis of an engineered \u003cem\u003eEscherichia coli\u003c/em\u003e-\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e co-culture consortium for phenanthrene bioremoval. Biochem Eng J 148:214\u0026ndash;223\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Lin M, Zhuang D (2022) Wastewater treatment and emerging contaminants: Bibliometric analysis. Chemosphere 297:133932\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGovantes R et al (2010) Regulation of the atrazine-degradative genes in \u003cem\u003ePseudomonas\u003c/em\u003e sp. Strain ADP. FEMS Microbiol Lett 310:1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuri M, Gandhi K, Kumar MS (2023) Emerging environmental contaminants: A global perspective on policies and regulations. J Environ Manage 332:117344\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan S, Naushad M, Govarthanan M, Iqbal J, Alfadul SM (2022) Emerging contaminants of high concern for the environment: Current trends and future research. Environ Res 207:112609\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L et al (2022) Engineering consortia by polymeric microbial swarmbots. Nat Commun 13(1):3879\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026ntilde;a L et al (2021) Obligate cross-feeding expands the metabolic niche of bacteria. Nat Ecol Evol 5(9):1224\u0026ndash;1232\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDolinšek J, Goldschmidt F, Johnson DR (2016) Synthetic microbial ecology and the dynamic interplay between microbial genotypes. FEMS Microbiol Rev 40(6):961\u0026ndash;979\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou X et al (2022) Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease. Nat Commun 13(1):7890\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo X et al (2015) Distal modulation of bacterial cell\u0026ndash;cell signalling in a synthetic ecosystem using partitioned microfluidics. Lab Chip 15:1842\u0026ndash;1851\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlgburi A et al (2021) Black cardamom essential oil prevents \u003cem\u003eEscherichia coli\u003c/em\u003e O157: H7 and \u003cem\u003eSalmonella Typhimurium\u003c/em\u003e JSG 1748 biofflm formation through inhibition of quorum sensing. J Food Sci Technol 58(8):3183\u0026ndash;3191\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang J et al (2016) Acyl-homoserine lactone-based quorum sensing and quorum quenching hold promise to determine the performance of biological wastewater treatments: an overview. Chemosphere 157:137\u0026ndash;151\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhiteley M, Diggle S, Greenberg E (2017) Progress in and promise of bacterial quorum sensing research. Nature 551:313\u0026ndash;320\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTripathi S, Sharma P, Chandra R (2021) Distillery wastewater detoxiffcation and management through phytoremediation employing \u003cem\u003eRicinus communis L\u003c/em\u003e. Bioresour Technol p. 125192\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTripathi S, Sharma P, Chandra R (2021) Degradation of organometallic pollutants of Distillery wastewater by bacterial community in biostimulation and bioaugmentation process. Bioresour Technol p. 125518\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z et al (2019) The regulation of N-acyl-homoserine lactones (AHLs)-based quorum sensing on EPS secretion via ATP synthetic for the stability of aerobic granular sludge. Sci Total Environ 673:83\u0026ndash;91\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z et al (2020) N-acyl-homoserine lactones (AHLs) in intertidal marsh: Diversity and potential role in nitrogen cycling. Plant Soil 454:103\u0026ndash;119\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkus V et al (2023) Conversations in the gut: The role of quorum sensing in normobiosis. Int J Mol Sci 24:3722\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayer C et al (2023) Quorum sensing architecture network in \u003cem\u003eEscherichia coli\u003c/em\u003e virulence and pathogenesis. FEMS Microbiol Rev 47:fuad031\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewis H et al (2001) A structural genomics approach to the study of quorum sensing crystal structures of three LuxS orthologs. Structure 9:527\u0026ndash;537\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Xu Q, Wang G, Shi K (2022) Indole-acetic acid promotes ammonia removal through heterotrophic nitriffcation, aerobic denitriffcation with mixed \u003cem\u003eEnterobacter\u003c/em\u003e sp. Z1 and \u003cem\u003eKlebsiella\u003c/em\u003e sp. Z2. Front Microbiol 13:929036\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai Y et al (2025) Biodegradation of acetochlor by microbial consortium AT1: microcosm centric microbiomic-metabolomics mechanisms and environmental remediation feasibility. J Environ Manage 387:125892\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi L et al (2024) Interplay of humic acid and Cr(Ⅵ) on green microalgae: metabolic responses and chromium enrichment. J Hazard Mater 480:135885\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell PGC, Twiss MR, Wilkinson KJ (1997) Accumulation of natural organic matter on the surfaces of living cells: implications for the interaction of toxic solutes with aquatic biota. Can J Fisheries Aquat Sci 54(11):2543\u0026ndash;2554\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu Z et al (2022) A novel pathway of Chlorimuron-Ethyl biodegradation by \u003cem\u003eChenggangzhangella methanolivorans\u003c/em\u003e strain CHL1 and its molecular mechanisms. Int J Mol Sci 23(17):9890\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa Q et al (2021) Substrate control of sulphur utilisation and microbial stoichiometry in soil: Results of 13C, 15N, 14C, and 35S quad labelling. ISME J 15(11):3148\u0026ndash;3158\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdulinam D et al (2020) ATP sulfurylase activity of sulfate-reducing bacteria from various ecotopes.\u003cem\u003e3 Biotech.\u003c/em\u003e10(2)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpencer RS et al (2017) A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum‐ regulated lysis. Nat Microbiol 2:17083\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrevor GJ et al (2020) Compartmentalized microbes and co-cultures in hydrogels for on‐demand bioproduction and preservation. Nat Commun 11:563\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarmur J, Schildkraut CL, Doty P (1961) The reversible denaturation of DNA and its use in studies of nucleic acid homologies and the biological relatedness of microorganisms. J Chim Phys Physicochim Biol 58:945\u0026ndash;955\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen Y, Yang L, Liang X (2014) The characteristics of a novel heterotrophic nitrifying and aerobic denitrifying bacterium, \u003cem\u003eAcinetobacter junii\u003c/em\u003e YB. Bioresour Technol 171:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) Mega 6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30(4):2725\u0026ndash;2729\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Xu Z, Chen Z, Wang G (2020) Simultaneous degradation of triazophos, methamidophos and carbofuran pesticides in wastewater using an \u003cem\u003eEnterobacter\u003c/em\u003e bacterial bioreactor and analysis of toxicity and biosafety. Chemosphere 261:128054\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKagiya G et al (2005) Generation of a strong promoter for \u003cem\u003eEscherichia coli\u003c/em\u003e from eukaryotic genome DNA. J Biotechnol 115:239\u0026ndash;248\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan H (2018) RNA interference to knock down gene expression. Methods Mol Biol 1706:293\u0026ndash;302\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y et al (2019) Cooperation between two strains of \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e in the simultaneous nitrogen removal and phosphate accumulation processes. Bioresour Technol 291:121854\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Xu Q, Wang G, Shi K (2023) Mixed \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e bacteria enhance soybean biological nitrogen fixation ability when combined with rhizobia inoculation. Soil Biol Biochem 184:109100\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y et al (2022) \u003cem\u003eSuaeda salsa\u003c/em\u003e root-associated microorganisms could effectively improve maize growth and resistance under salt stress. Microbiol Spectr 10(4):e0134922\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArbona V, L\u0026oacute;pez-Climent MF, P\u0026eacute;rez-Clemente RM (2009) G\u0026oacute;mez-Cadenas, A. Maintenance of a high photosynthetic performance is linked to flooding tolerance in citrus. Environ Exp Bot 66:135\u0026ndash;142\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen S Ultrafast one-pass FASTQ Data preprocessing, quality control, and deduplication using Fastp. \u003cem\u003eiMeta\u003c/em\u003e 2, e107\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi D et al (2015) An ultra-fast single‐node solution for large and complex metagenomics assembly via succinct de bruijn graph. Bioinformatics 31:1674\u0026ndash;1676\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBj\u0026oslash;rn NH et al (2014) Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol 32:822\u0026ndash;828\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang D et al (2019) MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SynComs, co-herbicides degradation, multi-omics analyses, quorum sensing, division of labor","lastPublishedDoi":"10.21203/rs.3.rs-8076029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8076029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe persistence of multiple herbicide residues poses a major challenge to agricultural productivity and soil health in the Northeast Black Soil region. To address this issue, we employed a “top-down” strategy to construct two synthetic microbial communities (SynComs YKB and HKB) that significantly enhanced the co-degradation of multiple herbicides under both laboratory and soil conditions. Multi-omics analyses demonstrated that each SynCom maintained a distinct metabolic potential, facilitated a metabolic division of labor among its constituent members, and utilized quorum sensing (QS) as the key regulatory mechanism governing this division of labor. Specifically, Acyl-homoserine lactones (AHLs) governed the functional dynamics of SynCom YKB and autoinducer-2 (AI-2) primarily regulated SynCom HKB. QS molecules also regulated and enhanced downstream metabolic functions, further promoting stable microbial interactions and the herbicide degradation process. Maize pot experiments combined with metagenomic profiling demonstrated that the SynComs provided many benefits for the native black soil ecosystem, and confirmed the critical role of QS-mediated interactions between the SynComs and indigenous microorganisms. This study presents an efficient bioaugmentation strategy to alleviate herbicide residues in black soil ecosystems, while simultaneously offering new insights into the communication mechanisms, interaction models, and design principles of SynComs for environmental bioremediation.\u003c/p\u003e","manuscriptTitle":"Microbial Communication Drives Division of Labor in SynComs for Herbicides Degradation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 08:15:06","doi":"10.21203/rs.3.rs-8076029/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9aaeda4a-0dc9-4a2c-9678-e06c7a4f0da0","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58511598,"name":"Biological sciences/Microbiology/Microbial communities/Symbiosis"},{"id":58511599,"name":"Biological sciences/Microbiology/Environmental microbiology/Soil microbiology"},{"id":58511600,"name":"Biological sciences/Microbiology/Bacteria/Bacterial synthetic biology"},{"id":58511601,"name":"Biological sciences/Microbiology/Microbial communities/Microbiome"}],"tags":[],"updatedAt":"2025-12-03T08:15:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 08:15:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8076029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8076029","identity":"rs-8076029","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}