Stochastic and deterministic evolutionary processes in microecosystem of dye-degrading functional microbiomes

Stochastic and deterministic evolutionary processes in microecosystem of dye-degrading functional microbiomes | 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 Stochastic and deterministic evolutionary processes in microecosystem of dye-degrading functional microbiomes XIN Liu, Yiting Qin, Jiajie Liu, Wanting Li, Xinyi Guo, Na Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9048998/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Dye wastewater seriously damages the water ecosystem and threatens human health. Functional microbiome showed significant advantages in dye wastewater treatment with their excellent degradation ability. The evolution of the microbiome has important implications for its functionality. The study provided insights into the evolutionary mechanisms of functional microbiome in dye wastewater treatment, based on research data spanning from 2015 to 2024. It was found that the changes of environmental factors had a great influence on the structure of microbiome, which was dominated by stochastic evolution in the short term. However, long-term follow-up monitoring showed that the functional microbiome was dominated by deterministic evolution, displaying good stability and adaptability. In addition, it was worth noting that interannual evolution of the microbiome was mainly influenced by biological interactions, whereas the intra-annual evolutionary process was mainly dominated by environmental selection. These results provide more efficient, stable and reliable biological solutions to environmental problems such as wastewater treatment. Biological sciences/Computational biology and bioinformatics Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Functional microbiome Microbiome evolution Activator Electron donor Degradation decolorization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Globally, the printing and dyeing industry has consistently been an important manufacturing sector. Dye wastewater therefrom has a high chroma and contains a considerable amount of organic pollutants, which not only significantly affects the appearance of the receiving water body but also undermines the aquatic ecological environment [ 1 , 2 ] . The demand of people for a beautiful ecological environment urges the effective treatment of dye wastewater to be more urgent [ 3 ] . Biological methods are often used in the treatment of dye wastewater because of their low consumption, environmental protection and high efficiency [ 4 ] . The functional microbiome, with its exceptional biodegradability and environmental adaptability, demonstrates considerable advantages in the biological treatment of dye wastewater [ 5 , 6 ] . Functional microbiome is a complex microecosystem containing several different communities that work together to fulfil specific functions. Therefore, an in-depth understanding of microbiology in process of dye decolorization is crucial. Artificially domesticated functional microbiome usually undergoes some degree of selection and guidance, and is therefore characterised by a degree of deterministic evolution [ 7 ] . In adapting to a particular environment or fulfilling a particular function, functional microbiome may undergo a series of deterministic evolutionary processes to adapt to the ecological niche in which they are found and to fulfil a particular function [ 8 ] 。Human intervention can be used to induce certain microbiome to gain an advantage and multiply in response to human intervention by selecting specific microorganisms, providing specific growth conditions, and so on, in order to fulfil specific functions [ 9 ] . The process of such interventions is usually purposeful and planned, and therefore to some extent deterministic. However, there can also be a degree of randomness in the process of human intervention [ 7 ] . For example, environmental factors, competitive pressures, mutations and other factors may affect the process of evolution and development of the microbiome, leading to some stochastic changes [ 10 ] . Thus, although artificially domesticated functional microbiome is characterised by deterministic evolution, a degree of stochasticity may still be present in the evolutionary process. Determinism and stochasticity are intertwined in the evolution of functional microbiome. Together, they shape the microbiome's diversity and adaptability, ultimately influencing its functionality. Currently, there is a lack of studies on deterministic as well as stochastic evolution of functional microbiome. Functional microbiome had shown great potential in treating environmental problems such as dye wastewater. However, to make full use of these microbiome, an in-depth understanding of their evolutionary mechanisms and modes of regulation is required. In terms of deterministic evolution, a large number of studies have clarified the optimal growth environments for many functional microbiome, as well as their reproductive metabolic processes [ 11 , 12 ] 。However, the multiple dimensions of the regulatory network within the microbiome and the interactions between the colonies require more research to reveal the underlying mechanisms. At the same time, there is insufficient understanding of how these factors affect the evolution of functional microbiome and how they can be controlled and utilised [ 13 , 14 ] . In order to better understand the microbial interactions of functional communities during dye wastewater treatment and to reveal the mechanism of natural microbiome construction, this study collected a large amount of functional microbiome data during a total of ten years of research experiments from 2015 to 2024. The effects of anthropogenic disturbances such as stress domestication, addition of exotic substances, etc. were assessed on the properties of functional microbial interactions networks associated with the degradation of dye wastewater. The study is based on zero modelling with phylogenetic data to access community structure assembly processes and uses an analytical framework to quantify the relative roles of deterministic and stochastic processes. Based on Spearman's correlation coefficient, a correlation network was constructed to explore the co-existing relationships among microorganisms. Two important node characteristics, intra-module connectivity and inter-module connectivity, are derived on the basis of network modules, and node attributes can be classified into four types based on the topological characteristics of the nodes to find key species in the microbial association network. These results help to understand how complex functional microbiome respond to and recover from long-term anthropogenic disturbances, assess their relationship with dye wastewater treatment, and further reveal the mechanisms of evolutionary succession of microbiome during dye wastewater treatment. Through more in-depth research, we are expected to better understand the evolutionary laws of functional bacterial microbiome and provide more efficient, stable and reliable biological solutions to environmental problems such as wastewater treatment. 1. Materials and methods 1.1. Chemicals, culture media and microbiome Remazol Brilliant Blue R (RBBR/RB19, CAS No.2580-78-1, MW 626.54), Reactive black 5 (RB5, CAS No. 17095-24-8, MW 991.82), Acid Orange 7 (AO7, CAS No.633-96-5, MW 350.32), Chorazol Black E (CBE, CAS No.1937-37-7, MW 781.73) and Malachite Green (MG CAS No.2437-29-8, MW 463.5) were purchased from Sigma-Aldrich (USA). All inorganic salts were purchased from Sinopharm Chemical Reagent (Shanghai) Co., Ltd. Yeast extract was purchased from Sangon Biotech (Shanghai) Co., Ltd. The basic medium contained (g L − 1 ): NH 4 Cl 0.2, Na 2 SO 4 0.5, KH 2 PO 4 2.66 and yeast extract 3. The complex activator comprises epigallocatechin gallate at a mass concentration of 2.5 g/L, theanine at a mass concentration of 2.5 g/L, ascorbic acid at a mass concentration of 6.5 g/L, H 3 BO 3 at a mass concentration of 6.5 g/L, FeCl 3 mass concentration of 4.5 g/L, and MgCl 2 mass concentration of 0.05 g/L. All the above culture media were adjusted to pH 6.0 and sterilized with a high-pressure sterilizer at 121℃ and 0.10 MPa for 20 minutes. In this study, the original microbial source used for screening the functional microbiome was activated sludge from a simulated hydrolysis acidification tank in the laboratory. The activated sludge came from the reflux sludge of the second sedimentation tank of Songdong Wastewater Treatment Plant in Songjiang District, Shanghai. The initial sludge concentration was 4560 mg/L, and the sedimentation performance was good. 1.2. Decolorization Took 10 mL of DDMZI/DDMY1 that had been cultured for 48 hours with a 10% (V/V) inoculation amount and added it to a conical flask containing 90 mL of fresh YE medium. Added RB5/RBBR to achieve a final concentration of 100 mg/L. Incubated at a constant temperature of 37℃. After a certain period of cultivation, took 2 mL of decolorization solution and fully contacted it with oxygen. Centrifuged at 6200×g for 10 minutes and observed the color changes before and after contact with oxygen. Using YE culture medium as a blank, measured the absorbance value of the supernatant at the characteristic wavelength of 597 nm. Calculated the decolorization ratio according to formula (1), and repeated the process 3 times. $${A}_{d}=\frac{\left({A}_{0}{-A}_{t}\right)}{{A}_{0}}\times100\text{\%}$$ 1 In the formula, A d is the decolorization ratio at the measured time; A 0 is the characteristic peak value of the dye solution at time 0; A t is the characteristic peak measured at time t. 1.3. Domestication of functional microbiome Took 10 mL of fresh activated sludge from the anoxic tank of the well that was running a simulated dye wastewater hydrolysis acidification tank in that experiment and added it to a 250 mL conical flask containing 90 mL of basic culture medium. Incubated it in a constant temperature incubator at 37 ℃ for 48 h. At the same time, the microbial density in the culture medium was measured using a blood cell counting plate. When the microbial density measured by the blood cell counting plate was greater than 10 8 cells/mL and the OD600 was around 1.5, transfer the microbial solution at a rate of 10% (V/V) into domestication medium (domestication medium: anhydrous sodium sulfate 0.5 g/L, ammonium chloride 0.2 g/L, potassium dihydrogen phosphate 2.66 g/L, yeast extract 3 g/L, RBBR ranging from 10 to 100 mg/L). When domesticating, the dye concentration started from 10 mg/L of Reactive Brilliant Blue 19 and was incubated at a constant temperature of 37 ℃ for 48 h to determine the decolorization ratio. When the decolorization ratio reached 80% or more, it was transferred to a domestication medium with a higher dye concentration. When the decolorization ratio of the mixed microbial community on 100 mg/L of active brilliant blue 19 reached over 80% after 48 hours, domestication was completed, and it was considered that a functional microbiome with high efficiency in degrading high concentrations of active brilliant blue 19 had been screened and named DDMY1. DDMY2 was co-cultured and domesticated using the domestication medium containing tea leaf residue at a mass concentration of 3 g/L. In the later stage, to study the removal effect of functional microbiome on azo dyes, RB5 was used to domesticate the functional microbiome DDMY1. The domestication medium contained (g L − 1 ): NH 4 Cl 0.2, Na 2 SO 4 0.5, KH 2 PO 4 2.66, yeast extract 3 and RB5 ranging from 10 to 100 mg/L. Domestication had been completed, and it was believed that a functional microbiome with high efficiency in degrading high concentrations of RB5 had been screened and named DDMZ1. 1.4. Effects of different electron donors and acceptors on the community structure of functional microbiome In this study, different electron donors and electron acceptors were selected to investigate the changes in the community structure of DDMZ1 and its keystone species. Four groups were selected as different electron donors: inorganic salts (MN), fructose (FRU), yeast extract (YE), and yeast extract + fructose (YE + FRU). Five different structural dyes were selected as different types of electron acceptors: AO7, RB5, CBE, RBBR, and MG. AO7 is a monoazo dye, RB5 is a diazo dye, CBE is a triazo dye, MG is a triphenylmethane dye, and RBBR is an anthraquinone dye. In addition, four different concentrations of azo dye RB5 (0 mg/L, 100 mg/L, 200 mg/L, and 400 mg/L) were selected as different concentrations of dye electron acceptors. 1.5. DNA extraction, PCR amplification and sequencing with Illumina MiSeq The detailed experimental operation was in accordance with manufacturer’s instructions. Total DNA of all samples were extracted by E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer’s protocols. Using extracted DNA as a template, primers 515F (5 'GTGCCAGCMGCGG-3') and 907R (5'-CCGTCAATTCMTTTRAGTTT-3') were used to amplify the V4 and V5 regions of 16S rDNA. The Illumina MiSeq sequencing method was employed to do this investigation. After sequencing, raw fastq files were demultiplexed and quality-filtered using Quantitative Insights into Microbial Ecology (QIIME). In order to further improve the quality of analysis results, before conducting bioinformatics analysis, the raw data obtained must be filtered and processed to obtain an optimized sequence. After sequencing, in order to reduce the inaccuracy of sequencing results caused by different sequences, it is necessary to ensure that the same number of sequences are used when comparing microbial communities between different samples. Using UPARSE (version 7.1 http://drive5.com/uparse/)Assig n these sequences as classification units OTUs (97% similarity). 1.6. Statistical analysis Based on the OTU clustering analysis results, the Mothur software package was used to analyze the diversity indices and dilution curves of Chao, Ace, Simpson, Coverage, and Shannon.Based on taxonomic information, using RDP software package ( http://rdp.cme.msu.edu/)Perfor m phylogenetic analysis on sequences at the taxonomic level. Based on the different microbial community compositions of different samples, hierarchical aggregation, principal co-ordinates analysis (PCoA), fold change, Abundance Circos plot and Nonmetric Multidimensional Scalin (NMDS), were performed on each sample using the R software package. Besides, to examine relationship among microbial community, the co-occurrence network was constructed as the previous study of Peng et al [ 15 ] . In order to identify key species in the microbial association network, ZIPI analysis was performed on the samples. Node attributes can be categorized into four types based on the topological characteristics of the node, including: Module hubs (module centers, nodes with high connectivity within the module, Zi > 2.5 and Pi < 0.62), Connectors (connection nodes, nodes with high connectivity between the two modules, Zi 0.62), Network hubs (network centers, nodes with high connectivity throughout the network, Zi > 2.5 and Pi > 0.62), and Peripherals (peripheral nodes, nodes that do not have high connectivity within and between modules, Zi < 2.5 and Pi < 0.62). The remaining 3 types of nodes other than Peripherals are usually categorized as critical nodes [ 16 ] . An analytical framework was used to quantify the relative roles of deterministic and stochastic processes based on the null model method and phylogenetic data.βNTI and RCbray can be classified into different regions, including Heterogeneous Selection(βNTI > 2), Homogeneous Selection(βNTI < -2), Dispersal Limitation(|β-NTI| 0.95), Homogenizing Dispersal(|β-NTI| < 2 and RCbray < − 0.95), Undominated(|β- NTI| < 2 and |RCbray| < 0.95). Meanwhile, the R package of Tax4Fun2 was utilized to rapidly predict the functional profiles and functional redundancy of prokaryotes based on 16S rRNA gene sequences, and to analyze the functional changes of microbiome over the decade. 2. Results and discussion 2.1. Decoloration ratio The functional microbiome DDMY1, which was capable of efficiently decolorizing Reactive Brilliant Blue 19, was finally obtained after being screened and domesticated through 10 anthraquinone dye concentration gradients over a period of 1 year and approximately 150 generations, utilizing gradient concentration pressure domestication. As shown in Fig. 1 (a), the 48 h decolorization ratio of 100 mg/L RBBR by functional microbiome DDMY1 was able to reach more than 98% in 2015. Domestication of DDMY1 with azo dyes using RB5 in March 2016 resulted in DDMZ1, a functional microbiome capable of efficiently degrading azo dyes. After one year of domestication DDMZ1 was able to achieve 95% decolorization ratio of 100 mg/L azo dye RB5(Fig. 1 (b)). Overall, the two functional microbiomes maintained a better decolorization ratio effect in the long term. It is presumed that the long-term stable culture environment contributed to the stability of the microbiome structure. According to Fig. 1 (c), it was found (this part of the data has been published [ 17 ] ) that the decolorization of DDMY2 was better than that of DDMY1 when treating 200 mg/L of RBBR. DDMY2 differed from DDMY1 in that the former had been domesticated together with the addition of tea dregs during the long-term domestication process, so it was presumed that the difference in decolorization ratios was due to the activation of tea dregs. It was also seen that the complex activator had a significant enhancement on the decolorization ability of the functional microbiome. Figure 1 (d) demonstrated the changes in decolorization ratio of functional microbiome DDMZ1 treating different structural types of dyes. It was seen that in azo dyes, the decolorization ability of DDMZ1 was enhanced with the reduction of azo groups. DDMZ1 showed strong decolorization ability in the late stage of decolorization of triphenylmethane dyes, while the decolorization ability of anthraquinone dyes was slightly lower than that of the other two types of dyes. Overall, the functional microbiome DDMZ1 showed good degradation of different types of dyes. Figure 1 (e) showed the ability of functional microbiome to decolorize the azo dye RB5 by treating different concentrations. It could be seen that the functional microbiome had a high decolorization ratio of 72.1% at 72 h in the presence of a high concentration of dye at 400 mg/L, demonstrating a high decolorization capacity. At the same time, it was obvious that the decolorization ratio decreased with increasing dye concentration. Figure 1 (f) showed the effect of the presence of different electron donors on the decolorization ability of functional microbiome. According to the decolorization ratio, it was seen that YE + FRU>YE > FRU>MN, and the YE + FRU group was the most effective, with a decolorization ratio of up to 98.5%. In summary, the three groups of autonomously domesticated natural microbiomes exhibited significant functionality in the biological treatment of dye wastewater. Additionally, variations in environmental factors during the domestication and application processes had a crucial impact on the expression of the microbiomes' functionality. 2.2. Studies on the evolution of functional microbiome during the domestication stage Figure 2 (a) showed a hierarchical clustering tree diagram of the microbiome of DDMY1 during domestication and use, indicating the evolutionary relationship of functional microbiome during domestication. Sequences of samples belonging to the same branching class had a similar evolutionary relationship, which indicated that the functional microbiome evolved over time as domestication and application progressed. During the one-year period of functional microbiome application following domestication, the microbiome underwent significant evolution and had already diverged into a distinct branch from its original domestication stage. Figure 2 (b) showed the analysis of βNTI/RCbray community structure of DDMY1 during domestication and application. Firstly, the two-year process of domestication and application of DDMY1 was dominated by stochastic evolution with genetic drift. Some of the quantitative data had RCbray values greater than 0.95, suggesting that the functional microbiome also underwent diffusion-restricted stochastic evolution without homogeneous diffusion, and presumably involved fewer microbial community interactions within the functional microbiome. Functional microbiome underwent a small amount of deterministic evolution (201501–201503, 201503–201506), suggesting that changes in its community structure were not randomly expected, but rather that physico-chemical conditions during domestication had a driving influence on the community composition. Alpha diversity is a reflection of the abundance and diversity of microbiome. As can be seen in Table 1 , the coverage data indicated that the results were generally indicative of the true state of the microorganisms in the samples. In the pre-domestication period of 0–6 months, Shannon's index was greater with longer domestication time, and Simpson's index, indicating that functional microbiome increased in microbial diversity in the pre-domestication period with increasing domestication time. The data based on the the Ace and Chao indices showed that the species abundance of functional microbiome increased in the pre-domestication period with the increase in the duration of domestication. In the later stages of domestication, the diversity of functional microbiome declined and the abundance increased with increasing domestication time. Overall, the diversity of the microbiome decreased in the late stage of domestication compared to the early stage, and the abundance showed a trend of increasing and then decreasing with the time of domestication. Maximum abundance was reached at 3 months of domestication. During the application phase of the functional microbiome, an increase in microbial diversity was observed, accompanied by a decrease in the abundance of microbial. It is hypothesized that anthropogenic activities, such as domestication and application, significantly influenced microbiome evolution. Table 1 Alpha diversity during DDMY1 domestication and application Sample Phase Sobs Shannon Simpson Ace Chao Coverage 0M Pre-domestication 9 0.670559 0.573068 9.373779 9 0.99997 3M 15 0.866241 0.481677 15 15 1 6M 20 0.888257 0.469446 22.722886 21 0.999918 10M Late stage of domestication 10 0.885563 0.472989 0 9.333333 0.99993 11M 9 0.872402 0.565624 12.886969 11 0.999942 20M Application phase 18 1.102678 0.499872 18.474966 18 0.999971 24M 13 1.457159 0.252125 14.680891 13.5 0.999934 The results of the modularity analysis showed that the DDMY1 association network was mainly composed of two association modes such as intra-module and inter-module interactions. Lachnoclostridium (OTU14), Lachnoclostridium_5 (OTU15) and Tyzzerella (OTU16) constituted Module 1. Other microorganisms constituted one Module 2. According to the Network correlation network analysis diagram in Fig. 3 , it could be seen that the DDMY1 community structure was predominantly intra-module 2 interactions. Clostridium_sensu_stricto_12 had the highest value in the center of all nodes and was the most important in the network structure. Clostridium_sensu_stricto_12 was recognized as a key microorganism for acetic acid production [ 18 ] , which played an important function in the treatment of DDMY1 dye wastewater. It was worth noting that microorganisms were clearly positively correlated with each other in the community structure, with only Burkholderia (OTU10) showing negative correlation with both unclassified_o_Pseudomonadales (OTU12) and Enterococcus (OTU13). According to Abilaji et al. [ 19 ] macro genomic results showed that Enterococcus (OTU13) was involved in the biodegradation of textile wastewater. The study of Wang et al. [ 20 ] found that Enterococcus (OTU13) was able to achieve 81.95% decolorization of 50 mg/L RB5. Zhang et al. [ 21 ] also found that Burkholderia (OTU10) had a good decolorization ratio for azo dyes, and the decolorization ratio for RB5 at 200 mg/L could reach 76%. The functionality of unclassified_o_Pseudomonadales (OTU12) for dye degradation had been reported relatively little. Both Burkholderia (OTU10) and Enterococcus (OTU13) possess dye degradation functions in DDMY1, and it was hypothesized that the negative correlation presented by the two inhibits the functionality of DDMY1. During the evolution of microbial communities, while continually enhancing functionality, there emerged community relationships that inhibited the expression of that functionality. 2.3. Effects of complex activators on the evolution of functional microbiome PcoA analyses, NMDS analyses, and correlation analyses are able to assess the effects of different domestication processes on the evolution of functional microbiome communities. Network correlation network analysis enables the study of the co-existence of community species in the presence of different domestication processes. According to the PcoA analysis depicted in Fig. 4 (a), it could be observed that DDMY1, which had been domesticated exclusively by RBBR, exhibited a certain degree of similarity in the evolution of its microbiological population after 6 months of domestication, yet it remained within the same quadrant. However, after the addition of the complex activator, the relative coordinate points of the two groups of samples (D1A vs. 6D1A) were already in different quadrants compared to D1 and 6D1. And D1A was not in the same quadrant as its sample 6D1A obtained after 6 months of domestication, and the evolution of the microbiome was obvious. While for the microbiome DDMY2, which had been co-domesticated by RBBR and tea residue, the effect produced by the composite activator was less pronounced, remaining largely within the same quadrant, the evolution of the microbiome was not evident. This could also be clearly discerned through the NMDS analysis (Fig. 4 (b)). For DDMY2, the effects produced by prolonged domestication were likely to be greater than the effects of the complex activator.According to the correlation clustering analysis in Fig. 4 (c), it could be seen that sample group D1 (D1A and 6D1A (X6D1A)) with the addition of the composite activator belonged to the same cluster as group D2 domesticated by tea residue. In summary, it was hypothesized that because the composite activator better simulated the activation function of tea dregs for the microbiome, both of them provided the same culture environment to promote the deterministic evolution of functional microbiome. According to the network correlation analysis of DDMY1 in Fig. 4 (d), it could be seen that the microbiome could be categorized into three plates, and both intra- and inter-plate interactions played very important roles. Plate I and Plate II were in a many-to-many mutualistic relationship, and norank_f__PHOS-HE36 (OTU16) connected the two plates. Norank_f__PHOS-HE36 (OTU16) was also the most highly correlated microorganism in the microbiome and was found to be mostly used in metal metabolism as well as in reaction systems such as denitrification [ 22 , 23 ] . In Plate I, Enterococcus (OTU17) showed negative correlation with all microorganisms in Plate I. Enterococcus (OTU17) had been documented by many to possess dye-degrading functions [ 24 – 26 ] , and was hypothesized to play an important role in the functional microbiome DDMY1. Li et al. [ 27 ] found that the relative abundance of norank_o__JG30-KF-CM45 (OTU14) was negatively correlated with NH 3 . The reduction of azo dyes by Enterococcus (OTU17) under parthenogenetic anaerobic conditions might have produced a certain amount of ammonia, leading to a negative correlation. PcoA analysis, NMDS analysis, and correlation analysis revealed that the effect of complex activators on the deterministic evolution of functional microbiome was more pronounced compared to long-term domestication. Therefore, the study used Fold change to reflect species characterization data with up- or down-regulation of abundance (Fig. 4 (e)) as a way to further analyze the effect of complex activators on the evolution of functional microbiome. First for DDMZ1, the addition of the complex activator significantly increased the abundance of Pseudomonas and decreased the abundance of Lachnoclostridium . Pseudomonas had a better degradation effect [ 28 ] , which is also corroborated by the fact that the addition of activators contributed to the functional microbiome decolorization ratio. Huang et al. [ 29 ] also showed that the addition of tea polyphenols contained in the complex activator resulted in a decrease in Lachnoclostridium abundance. Zeng et al [ 30 ] . believe that Burkholderia-Paraburkholderia was negatively correlated with epigallocatechin gallate. The results of the decrease in the abundance of Burkholderia-Paraburkholderia , which were observed due to the addition of activators, were consistent with their previous findings. For DDMY2, the effect of activators was smaller compared to DDMY1, with the largest increase in the abundance of Azoarcus and a significant decrease in the abundance of norank_f__Veillonellaceae . Polysaccharides from Fuzhuan brick tea (FBTPS), one of most important bioactive components in tea. Chen et al. [ 31 ] found that the addition of FBTPS induced a significant increase in the abundance of norank_f__Veillonellaceae . DDMY2 had been domesticated with tea leaf residue, which contains FBTPS but not in the added complex activator, so it was hypothesized that the absence of this substance had led to a decrease in the abundance of norank_f__Veillonellaceae . DDMY1 and DDMY2 were subjected to activator addition and six-month domestication to study their evolutionary process, and the results of βNTI/RCbray community structure analysis were shown in Fig. 4 (f). As can be seen, all of the evolution of the two groups of colonies was stochastic. Genetic drift was dominant, with a small amount of diffusion limitation. The functional microbiome DDMY1 after 6 months of domestication showed a clear diffusion limitation between the other three groups. It was hypothesized that diffusion limitation occurred mainly due to spatial as well as temporal separation and the inability of microorganisms to interact between samples, resulting in ecological drift over time. 2.4. Effects of changing environmental factors on the evolution of functional microbiome 2.4.1. Different structural types of dye electron acceptors Functional microbiome DDMZ1 had a better effect on the treatment of dyeing wastewater, and different structural types of dyes affected the community structure and the evolution of the microbiome. Figure 5 (I) represented the graph of structural changes of functional microbiome under the influence of different structural types of dye electron acceptors. The effect of different types of dyes on the structure of microbiome was evident, with significant differences in community structure in the presence of azo dyes, triphenylmethane and anthraquinone dyes. DDMZ1 was domesticated from the azo dye RB5. The community structure of the double azo dye (RB5) and triple azo dye (CBE) groups was similar to that of the functional microbiome DDMZ1 in the absence of the dye, and differed somewhat from that in the presence of the single azo dye (AO7). The larger proportion (37.62%) of Enterococcus (OTU12) was the main reason for the difference in community structure between those affected by single azo dyes and those affected by other azo dyes. A number of studies had shown that Enterococcus (OTU12) had been applied to dye biodegradation because of its better functionality [ 24 , 25 ] . Enterococcus (OTU12) was hypothesized to be capable of degrading dyes with relatively simple structures and to had relatively low tolerance for more complex and toxic azo dyes. Pseudomonas (OTU19) showed a large percentage (87.58%) in anthraquinone dyes, which was hypothesized to have a better removal ability for anthraquinone dyes, which was in agreement with the findings of Wang et al. [ 32 ] . Stenotrophomonas (OTU11, 26.80%), Escherichia-Shigella (OTU18, 38.36%) and Pseudomonas (OTU19, 21.51%) were more predominant in the MG group, which differed considerably from the community structure under the influence of the azo dye group. The structure of the community affected by triphenylmethane dyes was clearly more complex compared to azo dyes as well as anthraquinone dyes. 2.4.2. Different concentrations of dye electron acceptors The effects of different concentrations of azo dye RB5 on the community structure of functional microbiome were shown in Fig. 5 (II). The composition of the microbiome was more complex in the absence of dye presence, while the community structure was gradually simplified as the dye concentration increased. Stenotrophomonas (OTU5) had a large occupancy at low concentrations, but no significant occupancy was seen at the high concentration of 400 mg/LRB5, hence it was hypothesized that the high toxicity of the dye at its high concentration had an effect on its activity. The abundance of Burkholderia-Paraburkholderia (OTU6) was higher at 400 mg/LRB5 (58.37%) than 200 mg/LRB5 (28.79%). It was hypothesized that Burkholderia-Paraburkholderia (OTU6) played an important role in the decolorization of dyes at high concentrations by functional microbiome. 2.4.3. Different types of electron donor According to Fig. 5 (III-a), it could be seen that the results of functional microbiome changed significantly in the presence of different electron donors. The microbiome of the YE and MN groups were more similar. It was hypothesized that the community structure had stabilized because the domestication medium for the functional microbiome contains YE, and that the microbiome changes less when YE was not added. Lactococcus (OTU13) had the largest percentage in YE (58.42%) vs. MN (71.72%), which was not a major functional microorganism as presumed based on the decolorization ratios of different electron donors. When the electron donor was FRU group, the microbiome was more complex, in which Escherichia-Shigella (OTU18) dominated. Tacas et al. [ 33 ] found that Escherichia-Shigella (OTU18) had a better extracellular electron transfer capacity, which favors dye degradation. When YE was compounded with FRU as an electron donor, the community structure was similar to that of the YE group, and it was hypothesized that YE group had a greater effect on the microbiome compared to FRU group. Figure 5 (III-b) showed the ZIPI analysis for the effect of different donors on microbiome evolution, and the remaining three types of nodes except Peripherals were categorized as key nodes. Chroococcidiopsis_SAG_2023 (OTU1, (0.6667, 0.866)) and Burkholderia-Caballeronia-Paraburkholderia (OTU3, (0.625, 1.1547) were all of the Connectors type, nodes with a high degree of connectivity between the two modules, and could be fully categorized as critical nodes. Combined with the Circos plot and decolorization ratio, Burkholderia-Caballeronia-Paraburkholderia (OTU3) had the largest percentage in the YE + FRU group, which had the highest decolorization ratio, and thus it was hypothesized that Burkholderia-Caballeronia- Paraburkholderia (OTU3) played an important functional role in the microbiome. In summary, environmental factors had a significant impact on community structure, and changes in community structure also affected the evolution of the microbiome. YE group electron donors had the greatest influence on the microbiome. Burkholderia-Caballeronia-Paraburkholderia was the key functional microorganisms within the microbiome. The conclusions drawn can be leveraged to strategically guide the evolution of the microbiome. 2.5. A decade long application of functional microbiome evolutionary studies 2.5.1. Changes in microbiome diversity Table 2 showed the Alpha diversity data during the decade of DDMZ1 domestication and application. The Coverages data indicate that all measurements were reliable. Simpson's index could be used to characterize the concentration of community composition, with higher values indicating higher concentration and lower diversity. According to Table 2 , it could be seen that Shannon index gradually increased and Simpson index gradually decreased over the five years from 2015–2019, and the diversity of functional microbiome gradually increased. The diversity of functional microbiome gradually decreased over the four years from 2021–2024. The overall trend of functional microbiome diversity over the decade was first increasing and then decreasing. It was hypothesized that in the initial stage, the increase in diversity might be due to the gradual diversification of the microbiome through adaptive evolution to new habitats under different environmental conditions. However, over time, long periods of unchanging culture environments might lead to the dominance of certain strains, which might result in a decrease in diversity. Table 2 Alpha diversity during a decade of DDMZ1 domestication and application Sample Sobs Shannon Simpson Ace Chao Coverage 2015 13 0.797284 0.484319 13.460219 13 0.999973 2016 9 1.276632 0.326386 9 9 1 2018 22 1.603299 0.330105 22 22 1 2019 13 1.897976 0.17909 13 13 1 2021 14 1.769325 0.223585 14 14 0.99998 2022 11 1.380235 0.39922 11 11 1 2024 7 0.917385 0.448764 7.424427 7 0.999982 2.5.2. Evolution of functional microbiome Figure 6 (a) showed the community structure composition and changes of functional microbiome DDMZ1 during the ten-year domestication and application process. It was obvious that the microbes in the functional microbiome mainly belong to two phyla, Firmicutes and Proteobacteria . Burkholderia and unclassified_f__Enterobacteriaceae dominated the initial microbiome, but the abundance of both gradually decreased during microbiome domestication and prolonged application. Klebsiella , Escherichia-Shigella and Stenotrophomonas gradually dominated. Many studies had also shown that the three microorganisms mentioned above could play an important role in dye degradation [ 33 – 35 ] . Figure 6 (b) demonstrated the results of calculating the stochastic and deterministic evolution of functional microbiome based on βNTI/RCbray, and it was clearly visible that the evolution of functional microbiome over the decade had shown significant deterministic evolution. The quantified interannual variation was mainly distributed in the βNTI > 2 range, with dispersed genetic distances for OTUs, showing that the interannual variation of the microbiome was mainly dominated by biological interactions. Whereas annual variation was mainly in the βNTI<-2 range, the genetic distance of OTUs converged and was dominated by environmental selection. Deterministic evolution helps to maintain and optimize the functional microbiome [ 36 ] . Functional microbiome was screened for microbial species that were adapted to specific environments through a deterministic evolutionary process under specific environmental conditions. These microorganisms worked together to perform specific functions through interaction and coordination. The microbiome had maintained deterministic evolution, presumably due to the long-term consistency of the culture environment of the microbiome over a ten-year period. Figure 6 (c) illustrated the key species in the microbial association network of functional microbiome over the decade. Burkholderia (0.625, 1.1547) with Pi > 0.62, the study concluded that nodes with high connectivity between the two modules were keystone species for functional microbiome. However, changes in community structure over the decade showed that the key species, Burkholderia , decreased as a percentage of the microbiome with microbiome application. Several studies had demonstrated the strong degradation capacity of the key species Burkholderia in dyeing wastewater [ 37 , 38 ] . Therefore, in order to improve the dye degradation ability of the microbiome, subsequent artificial regulation of their abundance is considered necessary. 2.6. Prediction of functional microbiome Rapid prediction of functional profiles of microbiome from 16SrRNA gene sequences contributes to a deeper understanding of the functional properties of microbiome in specific environments or conditions. From Fig. 7 , it could be seen that the main functions of DDMZ1 were Carbohydrate metabolism and Lipid metabolism. Both are basic and important functions in microbial communities. They provide energy and building materials for microorganisms. This function of Xenobiotics biodegradation and metabolism is important for the maintenance of ecosystems. Overall, the expression of Xenobiotics biodegradation and metabolism function gradually increased during the decade, which showed that the functional microbiome DDMZ1 was able to show stronger functionality after long-term domestication and application. It was also evident that the functional expression of DDMZ1 in the microbiome remained essentially stable over the ten-year period, which corroborates the deterministic evolution derived from 2.5.2 Evolution of functional microbiome. 3. Conclusions The functional microbiome had good decolorization ability for dye wastewater. The changes of environmental factors had a great influence on the community structure of functional microbiome. Under the influence of short-term environmental factors, the microbiome was dominated by stochastic evolution. However, long-term tracking and monitoring of the functional microbiome revealed that the functional microbiome was basically dominated by deterministic evolution during 10 years of domestication and application. This indicates that the functional microbiome had better stability as well as adaptability when the culture conditions were kept constant, which was conducive to maintaining and optimizing the function of the microbiome. And the interannual evolution of microbiome was mainly influenced by biological interactions, while the intra-annual evolutionary process was mainly dominated by environmental selection. At the same time the functional microbiome expression remained stable and the function of Xenobiotics biodegradation and metabolism expression gradually increased. Declarations Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xin Liu：Writing – original draft, Formal analysis, Conceptualization, Investigation Yiting Qin: Writing – original draft, Formal analysis, Software, Methodology, Visualization Jiajie Liu: Resources, Project administration Wanting Li: Resources, Software, Supervision Xinyi Guo: Software, Validation, Visualization Na Liu: Project administration, Funding acquisition Qingyun Zhang: Project administration, Funding acquisition Xuehui Xie: Writing – review and editing, Validation, Project administration, Funding acquisition Ye Chen: Writing – review and editing, Methodology, Resources, Funding acquisition Author Contribution Xin Liu：Writing – original draft, Formal analysis, Conceptualization, InvestigationYiting Qin: Writing – original draft, Formal analysis, Software, Methodology, VisualizationJiajie Liu: Resources, Project administrationWanting Li: Resources, Software, SupervisionXinyi Guo: Software, Validation, VisualizationNa Liu: Project administration, Funding acquisitionQingyun Zhang: Project administration, Funding acquisitionXuehui Xie: Writing – review and editing, Validation, Project administration, Funding acquisitionYe Chen: Writing – review and editing, Methodology, Resources, Funding acquisition Acknowledgement The authors acknowledge the Fundamental Research Funds for the Central Universities, China (grant numbers 2232024A-02，2232022G-01). The Innovation Team for Reducing Pollution and Carbon Emissions in the Agricultural Ecological Environment of Northern Anhui (grant number 2024TD02). The scientific research program of Anhui Provincial Education Department (grant number 2025AHGXZK60086), the Innovation and Entrepreneurship Training Program for College Students (grant number 202110363068). Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Barbelli-Lopez, M.S., M.P. Peralta, L. Levin, et al., Effect of co-cultivation of white and brown rot species on basidiome production, lignocelluloytic enzyme activity and dye decolourisation . Bioresource Technology, 2024. 395: p. 130397. Zhao, H.-Q., N. Hou, Y.-R. Wang, et al., Carbon nanotubes mediated chemical and biological decolorization of azo dye: Understanding the structure-activity relationship . Environmental Research, 2022. 210: p. 112897. Song, Y., L. Wang, X. 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Shi, et al., Efficient Decolorization of Water and Oil-Soluble Azo Dyes by Enterococcus avium Treated with HP-β-CD . Pakistan Journal of Zoology, 2019. 51(2): p. 675–680. Nazari, N. and F.J. Kashi, A novel combination of immobilized Enterococcus casseliflavus sp. nov. with silver nanoparticles into a reusable matrix of Ca-Alg beads as a new strategy for biotreatment of Disperse Blue 183: Insights into metabolic characterization, biotoxicity, and mutagenic properties . Journal of Environmental Management, 2023. 325: p. 116578. Rathod, J., S. Pandey, K. Mahadik, et al., Homologous overexpression of azoreductase (azoA) in Enterococcus sp. L2 moderated growth and azo dye decolorization while gaining an oxidative and heavy metal stress resistance: A trade-off . Environmental Technology & Innovation, 2022. 27: p. 102531. Li, C., G. Pan, X. Wang, et al., The effects of non-metallic organic tanning agents on the microbial community structure in wastewater . 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Xu, Response Surface Optimization of Vat Blue 4 Degradation Process Using Pseudomonas aeruginosa WYT . Polish Journal of Environmental Studies, 2023. 32(1). Tacas, A.C.J., P.-W. Tsai, L.L. Tayo, et al., Degradation and biotoxicity of azo dyes using indigenous bacteria-acclimated microbial fuel cells (MFCs) . Process Biochemistry, 2021. 102: p. 59–71. Chantarasiri, A., Klebsiella and Enterobacter isolated from mangrove wetland soils in Thailand and their application in biological decolorization of textile reactive dyes . International Journal of Environmental Research and Public Health, 2020. 17(20): p. 7531. Galai, S., A. Perez de los Rios, F.J. Hernández-Fernández, et al., Microbial fuel cell application for azoic dye decolorization with simultaneous bioenergy production using Stenotrophomonas sp . Chemical Engineering & Technology, 2015. 38(9): p. 1511–1518. Wang, J., J. Shen, Y. Wu, et al., Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes . The ISME journal, 2013. 7(7): p. 1310–1321. Akita, H., S. Fujimoto, K. Wada, et al., Performance of Burkholderia multivorans CCA53 for ethyl red degradation . The Journal of General and Applied Microbiology, 2020. 66(4): p. 220–227. Gan, L., F. Zhou, G. Owens, et al., Burkholderia cepacia immobilized on eucalyptus leaves used to simultaneously remove malachite green (MG) and Cr (VI) . Colloids and Surfaces B: Biointerfaces, 2018. 172: p. 526–531. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 09 Mar, 2026 Submission checks completed at journal 08 Mar, 2026 First submitted to journal 06 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9048998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638051366,"identity":"b6ce6b02-b646-45a5-9cac-c9fd2435b209","order_by":0,"name":"XIN Liu","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"XIN","middleName":"","lastName":"Liu","suffix":""},{"id":638051367,"identity":"b154cb65-26f5-4732-a081-15073d590f09","order_by":1,"name":"Yiting Qin","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Qin","suffix":""},{"id":638051368,"identity":"15ef8ff5-bd4d-4322-8e98-7c77b9b8d7b4","order_by":2,"name":"Jiajie Liu","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Liu","suffix":""},{"id":638051369,"identity":"71a2e215-30d5-424a-904f-dfd61f00e22a","order_by":3,"name":"Wanting Li","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Wanting","middleName":"","lastName":"Li","suffix":""},{"id":638051370,"identity":"1572893d-d4d6-4727-a229-e610817c3a3a","order_by":4,"name":"Xinyi Guo","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Guo","suffix":""},{"id":638051371,"identity":"9a853c73-25c4-47e8-9a55-eeb91e797c80","order_by":5,"name":"Na Liu","email":"","orcid":"","institution":"Suzhou University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Liu","suffix":""},{"id":638051372,"identity":"1ecf20a5-67e8-44c7-b5bc-cb635c06846f","order_by":6,"name":"Qingyun Zhang","email":"","orcid":"","institution":"Anhui Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Qingyun","middleName":"","lastName":"Zhang","suffix":""},{"id":638051373,"identity":"5da8148e-1653-47e8-90e7-fe9bf527149d","order_by":7,"name":"Xuehui Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZjB5gIGNvcEMxGJsIF4LzwFitTBAtTBIJBCpxeA48zFpnoo7iX2Sj7c95mGwkd1wgPnZA3xaJJvZko15zjxLbJNOKzfmYUgz3nCAzdwAnxZ+Zh7Dx7xth4FacsykeRgOJ244wMMmgU8LGzP/h8O8/4BaJM+AtPwnrAVoC+Nj3gagFgkekJYDhLUA/WJsOOfYYeM2nrQyyTkGycYzD7OZ4dVicP7wM4k3NYdl57cf3ibxpsJOtu948zO8WtBNYIBF7igYBaNgFIwCSgAA0IJC6qQnW6IAAAAASUVORK5CYII=","orcid":"","institution":"Donghua University","correspondingAuthor":true,"prefix":"","firstName":"Xuehui","middleName":"","lastName":"Xie","suffix":""},{"id":638051374,"identity":"adeb82a1-5e67-45c1-8d77-0424c70389c6","order_by":8,"name":"Ye Chen","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-06 09:54:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9048998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9048998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109088594,"identity":"838584e6-c138-429c-bfa1-90337b52aca2","added_by":"auto","created_at":"2026-05-12 13:22:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174666,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in decolorization ratio of functional microbiome during prolonged incubation. (a) Decolorization ratio change during DDMY1 domestication and application period, (b) Decolorization ratio change during DDMZ1 domestication and application period, (c) Activator addition to DDMY1 and DDMY2 decolorization ratio change over time, (d) Decolorization ratio time change of functional microbiome DDMZ1 under different types of dye conditions, (e) Decolorization ratio time change of functional microbiome DDMZ1 under different concentrations of dye conditions, (f) Decolorization ratio time change of functional microbiome DDMZ1 under different electron donor conditions.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/c1627a5d8ff21b3a59557cb2.png"},{"id":109088403,"identity":"988e602e-426c-4dfd-999d-76ee65118da8","added_by":"auto","created_at":"2026-05-12 13:21:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84282,"visible":true,"origin":"","legend":"\u003cp\u003eEvolutionary map of the microbiome during DDMY1 domestication. (a) Hierarchical clustering tree diagram of the microbiome, (b) βNTI/RCbray community structure analysis. (Different subgroups in the legend represent two-by-two comparisons between samples within different groups, others represent comparisons between samples within different subgroups. c1: initial microbiome vs. 3 months, c2: 3 months vs. 6 months, c3: 6 months vs. 10 months, p1: 11 months vs. 20 months, and P2: 20 months vs. 24 months).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/e9d02e2f6ead77204bcee135.png"},{"id":109088459,"identity":"5923a565-de53-4a96-801b-137fec6043a3","added_by":"auto","created_at":"2026-05-12 13:21:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211089,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analysis of functional microbiome DDMY1 network associations during domestication and application.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/b11cc546955dbd359fccbb10.png"},{"id":109088404,"identity":"daeefbb2-628b-4416-ace9-e8a86b3d2c8f","added_by":"auto","created_at":"2026-05-12 13:21:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439368,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of activators on the community structure of functional microbiome. (a) PcoA analysis, (b) NMDS, (c) correlation analysis, (d) Network correlation network analysis, (e) Fold change analysis, (f) βNTI/RCbray community structure analysis. (In (a)(b)(c): D1: DDMY1, 6D1(X6D1): DDMY1 after 6 months of RBBR domestication, D1A:DDMY1+ activator, 6D1A(X6D1A): DDMY1+ activator for 6 months of domestication, D2: DDMY2, 6D2(X6D2): DDMY2 after 6 months of co-domestication of RBBR and tea residues, D2A: DDMY2+ activator, 6D2A(X6D2A): DDMY2+ activator for 6 months of domestication.) (In (f): D1: DDMY1 after 6 months of RBBR, DA1: DDMY1+ activator and DDMY1+ activator for 6 months of domestication, D2: DDMY2 after 6 months of co-domestication of RBBR and tea residues, DA2: DDMY2+ activator and DDMY2+ activator for 6 months of domestication).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/83e6c84a62c6ced3000eef1f.png"},{"id":109088400,"identity":"3df6c8ce-bf4a-409c-b74b-cfa818e678f1","added_by":"auto","created_at":"2026-05-12 13:21:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":894838,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of environmental factors on the community structure of functional microbiome. (I. Effects of different structural types of dye electron acceptors on the community structure of functional microbiome. (a) Circos plot, (b) stacked bar graph. (R1: DDMZ1, R2:DDMZ1+AO7, R3:DDMZ1+RB5, R4:DDMZ1+CBE, R5:DDMZ1+RBBR, R6:DDMZ1+MG)), (II. Effect of different concentrations of dyes on the community structure of functional microbiome. (a) Circos plot, (b) Stacked bar graph. (N1: DDMZ1, N2: DDMZ1+100 mg/L RB5, N3:DDMZ1+200 mg/L RB5, N4:DDMZ1+400 mg/L RB5)), (III. Effects of different electron donors on the community structure of functional microbiome. (a) Circos plot, (b) ZIPI analysis. (F1：YE, F2：MN, F3:FRU, F4: YE+FRU)).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/5f2da4c84934983e73451791.png"},{"id":109088915,"identity":"f99abcfa-fdd1-4ccb-8bd7-eb7ca417351f","added_by":"auto","created_at":"2026-05-12 13:24:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":201868,"visible":true,"origin":"","legend":"\u003cp\u003eEvolutionary studies of functional microbiome over a decade. (a) Bubble plot of community abundance, (b) βNTI/RCbray community structure analysis. (Different groupings in the legend represent two-by-two comparisons between samples within different groups, and others represent comparisons between samples within different groupings), (c) ZIPI analysis.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/19b835e75d30dce8f21ba588.png"},{"id":109088401,"identity":"5f7845c9-9a05-472a-8109-961c926f9abb","added_by":"auto","created_at":"2026-05-12 13:21:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":313202,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of decadal variation study of predictive function of microbial community (Metabolism level).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/518ec0426d2cf3c58af5a91f.png"},{"id":109204655,"identity":"c701600f-3e01-4899-82ad-b02042bb4bbc","added_by":"auto","created_at":"2026-05-13 15:01:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2550655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9048998/v1/ae7566e7-c610-4bc4-9f60-2a7eea407e05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stochastic and deterministic evolutionary processes in microecosystem of dye-degrading functional microbiomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, the printing and dyeing industry has consistently been an important manufacturing sector. Dye wastewater therefrom has a high chroma and contains a considerable amount of organic pollutants, which not only significantly affects the appearance of the receiving water body but also undermines the aquatic ecological environment\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The demand of people for a beautiful ecological environment urges the effective treatment of dye wastewater to be more urgent\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Biological methods are often used in the treatment of dye wastewater because of their low consumption, environmental protection and high efficiency\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The functional microbiome, with its exceptional biodegradability and environmental adaptability, demonstrates considerable advantages in the biological treatment of dye wastewater\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Functional microbiome is a complex microecosystem containing several different communities that work together to fulfil specific functions. Therefore, an in-depth understanding of microbiology in process of dye decolorization is crucial.\u003c/p\u003e \u003cp\u003eArtificially domesticated functional microbiome usually undergoes some degree of selection and guidance, and is therefore characterised by a degree of deterministic evolution\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In adapting to a particular environment or fulfilling a particular function, functional microbiome may undergo a series of deterministic evolutionary processes to adapt to the ecological niche in which they are found and to fulfil a particular function\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e。Human intervention can be used to induce certain microbiome to gain an advantage and multiply in response to human intervention by selecting specific microorganisms, providing specific growth conditions, and so on, in order to fulfil specific functions\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The process of such interventions is usually purposeful and planned, and therefore to some extent deterministic. However, there can also be a degree of randomness in the process of human intervention\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. For example, environmental factors, competitive pressures, mutations and other factors may affect the process of evolution and development of the microbiome, leading to some stochastic changes\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Thus, although artificially domesticated functional microbiome is characterised by deterministic evolution, a degree of stochasticity may still be present in the evolutionary process. Determinism and stochasticity are intertwined in the evolution of functional microbiome. Together, they shape the microbiome's diversity and adaptability, ultimately influencing its functionality.\u003c/p\u003e \u003cp\u003eCurrently, there is a lack of studies on deterministic as well as stochastic evolution of functional microbiome. Functional microbiome had shown great potential in treating environmental problems such as dye wastewater. However, to make full use of these microbiome, an in-depth understanding of their evolutionary mechanisms and modes of regulation is required. In terms of deterministic evolution, a large number of studies have clarified the optimal growth environments for many functional microbiome, as well as their reproductive metabolic processes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e。However, the multiple dimensions of the regulatory network within the microbiome and the interactions between the colonies require more research to reveal the underlying mechanisms. At the same time, there is insufficient understanding of how these factors affect the evolution of functional microbiome and how they can be controlled and utilised\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn order to better understand the microbial interactions of functional communities during dye wastewater treatment and to reveal the mechanism of natural microbiome construction, this study collected a large amount of functional microbiome data during a total of ten years of research experiments from 2015 to 2024. The effects of anthropogenic disturbances such as stress domestication, addition of exotic substances, etc. were assessed on the properties of functional microbial interactions networks associated with the degradation of dye wastewater. The study is based on zero modelling with phylogenetic data to access community structure assembly processes and uses an analytical framework to quantify the relative roles of deterministic and stochastic processes. Based on Spearman's correlation coefficient, a correlation network was constructed to explore the co-existing relationships among microorganisms. Two important node characteristics, intra-module connectivity and inter-module connectivity, are derived on the basis of network modules, and node attributes can be classified into four types based on the topological characteristics of the nodes to find key species in the microbial association network. These results help to understand how complex functional microbiome respond to and recover from long-term anthropogenic disturbances, assess their relationship with dye wastewater treatment, and further reveal the mechanisms of evolutionary succession of microbiome during dye wastewater treatment. Through more in-depth research, we are expected to better understand the evolutionary laws of functional bacterial microbiome and provide more efficient, stable and reliable biological solutions to environmental problems such as wastewater treatment.\u003c/p\u003e"},{"header":"1. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Chemicals, culture media and microbiome\u003c/h2\u003e \u003cp\u003eRemazol Brilliant Blue R (RBBR/RB19, CAS No.2580-78-1, MW 626.54), Reactive black 5 (RB5, CAS No. 17095-24-8, MW 991.82), Acid Orange 7 (AO7, CAS No.633-96-5, MW 350.32), Chorazol Black E (CBE, CAS No.1937-37-7, MW 781.73) and Malachite Green (MG CAS No.2437-29-8, MW 463.5) were purchased from Sigma-Aldrich (USA). All inorganic salts were purchased from Sinopharm Chemical Reagent (Shanghai) Co., Ltd. Yeast extract was purchased from Sangon Biotech (Shanghai) Co., Ltd. The basic medium contained (g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e): NH\u003csub\u003e4\u003c/sub\u003eCl 0.2, Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e 0.5, KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e 2.66 and yeast extract 3. The complex activator comprises epigallocatechin gallate at a mass concentration of 2.5 g/L, theanine at a mass concentration of 2.5 g/L, ascorbic acid at a mass concentration of 6.5 g/L, H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e at a mass concentration of 6.5 g/L, FeCl\u003csub\u003e3\u003c/sub\u003e mass concentration of 4.5 g/L, and MgCl\u003csub\u003e2\u003c/sub\u003e mass concentration of 0.05 g/L.\u003c/p\u003e \u003cp\u003eAll the above culture media were adjusted to pH 6.0 and sterilized with a high-pressure sterilizer at 121℃ and 0.10 MPa for 20 minutes.\u003c/p\u003e \u003cp\u003eIn this study, the original microbial source used for screening the functional microbiome was activated sludge from a simulated hydrolysis acidification tank in the laboratory. The activated sludge came from the reflux sludge of the second sedimentation tank of Songdong Wastewater Treatment Plant in Songjiang District, Shanghai. The initial sludge concentration was 4560 mg/L, and the sedimentation performance was good.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Decolorization\u003c/h2\u003e \u003cp\u003eTook 10 mL of DDMZI/DDMY1 that had been cultured for 48 hours with a 10% (V/V) inoculation amount and added it to a conical flask containing 90 mL of fresh YE medium. Added RB5/RBBR to achieve a final concentration of 100 mg/L. Incubated at a constant temperature of 37℃. After a certain period of cultivation, took 2 mL of decolorization solution and fully contacted it with oxygen. Centrifuged at 6200\u0026times;g for 10 minutes and observed the color changes before and after contact with oxygen. Using YE culture medium as a blank, measured the absorbance value of the supernatant at the characteristic wavelength of 597 nm. Calculated the decolorization ratio according to formula (1), and repeated the process 3 times.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${A}_{d}=\\frac{\\left({A}_{0}{-A}_{t}\\right)}{{A}_{0}}\\times100\\text{\\%}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, A\u003csub\u003ed\u003c/sub\u003e is the decolorization ratio at the measured time; A\u003csub\u003e0\u003c/sub\u003e is the characteristic peak value of the dye solution at time 0; A\u003csub\u003et\u003c/sub\u003e is the characteristic peak measured at time t.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Domestication of functional microbiome\u003c/h2\u003e \u003cp\u003eTook 10 mL of fresh activated sludge from the anoxic tank of the well that was running a simulated dye wastewater hydrolysis acidification tank in that experiment and added it to a 250 mL conical flask containing 90 mL of basic culture medium. Incubated it in a constant temperature incubator at 37 ℃ for 48 h. At the same time, the microbial density in the culture medium was measured using a blood cell counting plate. When the microbial density measured by the blood cell counting plate was greater than 10\u003csup\u003e8\u003c/sup\u003e cells/mL and the OD600 was around 1.5, transfer the microbial solution at a rate of 10% (V/V) into domestication medium (domestication medium: anhydrous sodium sulfate 0.5 g/L, ammonium chloride 0.2 g/L, potassium dihydrogen phosphate 2.66 g/L, yeast extract 3 g/L, RBBR ranging from 10 to 100 mg/L). When domesticating, the dye concentration started from 10 mg/L of Reactive Brilliant Blue 19 and was incubated at a constant temperature of 37 ℃ for 48 h to determine the decolorization ratio. When the decolorization ratio reached 80% or more, it was transferred to a domestication medium with a higher dye concentration. When the decolorization ratio of the mixed microbial community on 100 mg/L of active brilliant blue 19 reached over 80% after 48 hours, domestication was completed, and it was considered that a functional microbiome with high efficiency in degrading high concentrations of active brilliant blue 19 had been screened and named DDMY1. DDMY2 was co-cultured and domesticated using the domestication medium containing tea leaf residue at a mass concentration of 3 g/L.\u003c/p\u003e \u003cp\u003eIn the later stage, to study the removal effect of functional microbiome on azo dyes, RB5 was used to domesticate the functional microbiome DDMY1. The domestication medium contained (g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e): NH\u003csub\u003e4\u003c/sub\u003eCl 0.2, Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e 0.5, KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e 2.66, yeast extract 3 and RB5 ranging from 10 to 100 mg/L. Domestication had been completed, and it was believed that a functional microbiome with high efficiency in degrading high concentrations of RB5 had been screened and named DDMZ1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Effects of different electron donors and acceptors on the community structure of functional microbiome\u003c/h2\u003e \u003cp\u003eIn this study, different electron donors and electron acceptors were selected to investigate the changes in the community structure of DDMZ1 and its keystone species. Four groups were selected as different electron donors: inorganic salts (MN), fructose (FRU), yeast extract (YE), and yeast extract\u0026thinsp;+\u0026thinsp;fructose (YE\u0026thinsp;+\u0026thinsp;FRU). Five different structural dyes were selected as different types of electron acceptors: AO7, RB5, CBE, RBBR, and MG. AO7 is a monoazo dye, RB5 is a diazo dye, CBE is a triazo dye, MG is a triphenylmethane dye, and RBBR is an anthraquinone dye. In addition, four different concentrations of azo dye RB5 (0 mg/L, 100 mg/L, 200 mg/L, and 400 mg/L) were selected as different concentrations of dye electron acceptors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5. DNA extraction, PCR amplification and sequencing with Illumina MiSeq\u003c/h2\u003e \u003cp\u003eThe detailed experimental operation was in accordance with manufacturer\u0026rsquo;s instructions. Total DNA of all samples were extracted by E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer\u0026rsquo;s protocols. Using extracted DNA as a template, primers 515F (5 'GTGCCAGCMGCGG-3') and 907R (5'-CCGTCAATTCMTTTRAGTTT-3') were used to amplify the V4 and V5 regions of 16S rDNA. The Illumina MiSeq sequencing method was employed to do this investigation. After sequencing, raw fastq files were demultiplexed and quality-filtered using Quantitative Insights into Microbial Ecology (QIIME). In order to further improve the quality of analysis results, before conducting bioinformatics analysis, the raw data obtained must be filtered and processed to obtain an optimized sequence. After sequencing, in order to reduce the inaccuracy of sequencing results caused by different sequences, it is necessary to ensure that the same number of sequences are used when comparing microbial communities between different samples. Using UPARSE (version 7.1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/)Assig\u003c/span\u003e\u003cspan address=\"http://drive5.com/uparse/)Assig\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003en these sequences as classification units OTUs (97% similarity).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eBased on the OTU clustering analysis results, the Mothur software package was used to analyze the diversity indices and dilution curves of Chao, Ace, Simpson, Coverage, and Shannon.Based on taxonomic information, using RDP software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/)Perfor\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/)Perfor\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003em phylogenetic analysis on sequences at the taxonomic level. Based on the different microbial community compositions of different samples, hierarchical aggregation, principal co-ordinates analysis (PCoA), fold change, Abundance Circos plot and Nonmetric Multidimensional Scalin (NMDS), were performed on each sample using the R software package. Besides, to examine relationship among microbial community, the co-occurrence network was constructed as the previous study of Peng et al\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In order to identify key species in the microbial association network, ZIPI analysis was performed on the samples. Node attributes can be categorized into four types based on the topological characteristics of the node, including: Module hubs (module centers, nodes with high connectivity within the module, Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5 and Pi\u0026thinsp;\u0026lt;\u0026thinsp;0.62), Connectors (connection nodes, nodes with high connectivity between the two modules, Zi\u0026thinsp;\u0026lt;\u0026thinsp;2.5 and Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62), Network hubs (network centers, nodes with high connectivity throughout the network, Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5 and Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62), and Peripherals (peripheral nodes, nodes that do not have high connectivity within and between modules, Zi\u0026thinsp;\u0026lt;\u0026thinsp;2.5 and Pi\u0026thinsp;\u0026lt;\u0026thinsp;0.62). The remaining 3 types of nodes other than Peripherals are usually categorized as critical nodes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. An analytical framework was used to quantify the relative roles of deterministic and stochastic processes based on the null model method and phylogenetic data.βNTI and RCbray can be classified into different regions, including Heterogeneous Selection(βNTI\u0026thinsp;\u0026gt;\u0026thinsp;2), Homogeneous Selection(βNTI \u0026lt; -2), Dispersal Limitation(|β-NTI| \u0026lt; 2 and RCbray\u0026thinsp;\u0026gt;\u0026thinsp;0.95), Homogenizing Dispersal(|β-NTI| \u0026lt; 2 and RCbray\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.95), Undominated(|β- NTI| \u0026lt; 2 and |RCbray| \u0026lt; 0.95). Meanwhile, the R package of Tax4Fun2 was utilized to rapidly predict the functional profiles and functional redundancy of prokaryotes based on 16S rRNA gene sequences, and to analyze the functional changes of microbiome over the decade.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Decoloration ratio\u003c/h2\u003e \u003cp\u003eThe functional microbiome DDMY1, which was capable of efficiently decolorizing Reactive Brilliant Blue 19, was finally obtained after being screened and domesticated through 10 anthraquinone dye concentration gradients over a period of 1 year and approximately 150 generations, utilizing gradient concentration pressure domestication. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (a), the 48 h decolorization ratio of 100 mg/L RBBR by functional microbiome DDMY1 was able to reach more than 98% in 2015. Domestication of DDMY1 with azo dyes using RB5 in March 2016 resulted in DDMZ1, a functional microbiome capable of efficiently degrading azo dyes. After one year of domestication DDMZ1 was able to achieve 95% decolorization ratio of 100 mg/L azo dye RB5(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (b)). Overall, the two functional microbiomes maintained a better decolorization ratio effect in the long term. It is presumed that the long-term stable culture environment contributed to the stability of the microbiome structure.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(c), it was found (this part of the data has been published \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e) that the decolorization of DDMY2 was better than that of DDMY1 when treating 200 mg/L of RBBR. DDMY2 differed from DDMY1 in that the former had been domesticated together with the addition of tea dregs during the long-term domestication process, so it was presumed that the difference in decolorization ratios was due to the activation of tea dregs. It was also seen that the complex activator had a significant enhancement on the decolorization ability of the functional microbiome. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(d) demonstrated the changes in decolorization ratio of functional microbiome DDMZ1 treating different structural types of dyes. It was seen that in azo dyes, the decolorization ability of DDMZ1 was enhanced with the reduction of azo groups. DDMZ1 showed strong decolorization ability in the late stage of decolorization of triphenylmethane dyes, while the decolorization ability of anthraquinone dyes was slightly lower than that of the other two types of dyes. Overall, the functional microbiome DDMZ1 showed good degradation of different types of dyes. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(e) showed the ability of functional microbiome to decolorize the azo dye RB5 by treating different concentrations. It could be seen that the functional microbiome had a high decolorization ratio of 72.1% at 72 h in the presence of a high concentration of dye at 400 mg/L, demonstrating a high decolorization capacity. At the same time, it was obvious that the decolorization ratio decreased with increasing dye concentration. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(f) showed the effect of the presence of different electron donors on the decolorization ability of functional microbiome. According to the decolorization ratio, it was seen that YE\u0026thinsp;+\u0026thinsp;FRU\u0026gt;YE\u0026thinsp;\u0026gt;\u0026thinsp;FRU\u0026gt;MN, and the YE\u0026thinsp;+\u0026thinsp;FRU group was the most effective, with a decolorization ratio of up to 98.5%.\u003c/p\u003e \u003cp\u003eIn summary, the three groups of autonomously domesticated natural microbiomes exhibited significant functionality in the biological treatment of dye wastewater. Additionally, variations in environmental factors during the domestication and application processes had a crucial impact on the expression of the microbiomes' functionality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Studies on the evolution of functional microbiome during the domestication stage\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a) showed a hierarchical clustering tree diagram of the microbiome of DDMY1 during domestication and use, indicating the evolutionary relationship of functional microbiome during domestication. Sequences of samples belonging to the same branching class had a similar evolutionary relationship, which indicated that the functional microbiome evolved over time as domestication and application progressed. During the one-year period of functional microbiome application following domestication, the microbiome underwent significant evolution and had already diverged into a distinct branch from its original domestication stage. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b) showed the analysis of βNTI/RCbray community structure of DDMY1 during domestication and application. Firstly, the two-year process of domestication and application of DDMY1 was dominated by stochastic evolution with genetic drift. Some of the quantitative data had RCbray values greater than 0.95, suggesting that the functional microbiome also underwent diffusion-restricted stochastic evolution without homogeneous diffusion, and presumably involved fewer microbial community interactions within the functional microbiome. Functional microbiome underwent a small amount of deterministic evolution (201501\u0026ndash;201503, 201503\u0026ndash;201506), suggesting that changes in its community structure were not randomly expected, but rather that physico-chemical conditions during domestication had a driving influence on the community composition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlpha diversity is a reflection of the abundance and diversity of microbiome. As can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the coverage data indicated that the results were generally indicative of the true state of the microorganisms in the samples. In the pre-domestication period of 0\u0026ndash;6 months, Shannon's index was greater with longer domestication time, and Simpson's index, indicating that functional microbiome increased in microbial diversity in the pre-domestication period with increasing domestication time. The data based on the the Ace and Chao indices showed that the species abundance of functional microbiome increased in the pre-domestication period with the increase in the duration of domestication. In the later stages of domestication, the diversity of functional microbiome declined and the abundance increased with increasing domestication time. Overall, the diversity of the microbiome decreased in the late stage of domestication compared to the early stage, and the abundance showed a trend of increasing and then decreasing with the time of domestication. Maximum abundance was reached at 3 months of domestication. During the application phase of the functional microbiome, an increase in microbial diversity was observed, accompanied by a decrease in the abundance of microbial. It is hypothesized that anthropogenic activities, such as domestication and application, significantly influenced microbiome evolution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlpha diversity during DDMY1 domestication and application\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSobs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAce\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChao\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePre-domestication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.670559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.573068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.373779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.866241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.481677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.469446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.722886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLate stage of domestication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.472989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.872402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.886969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eApplication phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.102678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.499872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.474966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.457159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.680891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the modularity analysis showed that the DDMY1 association network was mainly composed of two association modes such as intra-module and inter-module interactions. \u003cem\u003eLachnoclostridium\u003c/em\u003e (OTU14), \u003cem\u003eLachnoclostridium_5\u003c/em\u003e (OTU15) and \u003cem\u003eTyzzerella\u003c/em\u003e (OTU16) constituted Module 1. Other microorganisms constituted one Module 2. According to the Network correlation network analysis diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it could be seen that the DDMY1 community structure was predominantly intra-module 2 interactions. \u003cem\u003eClostridium_sensu_stricto_12\u003c/em\u003e had the highest value in the center of all nodes and was the most important in the network structure. \u003cem\u003eClostridium_sensu_stricto_12\u003c/em\u003e was recognized as a key microorganism for acetic acid production \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, which played an important function in the treatment of DDMY1 dye wastewater. It was worth noting that microorganisms were clearly positively correlated with each other in the community structure, with only \u003cem\u003eBurkholderia\u003c/em\u003e (OTU10) showing negative correlation with both \u003cem\u003eunclassified_o_Pseudomonadales\u003c/em\u003e (OTU12) and \u003cem\u003eEnterococcus\u003c/em\u003e (OTU13). According to Abilaji et al.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e macro genomic results showed that \u003cem\u003eEnterococcus\u003c/em\u003e (OTU13) was involved in the biodegradation of textile wastewater. The study of Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e found that \u003cem\u003eEnterococcus\u003c/em\u003e (OTU13) was able to achieve 81.95% decolorization of 50 mg/L RB5. Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e also found that \u003cem\u003eBurkholderia\u003c/em\u003e (OTU10) had a good decolorization ratio for azo dyes, and the decolorization ratio for RB5 at 200 mg/L could reach 76%. The functionality of \u003cem\u003eunclassified_o_Pseudomonadales\u003c/em\u003e (OTU12) for dye degradation had been reported relatively little. Both \u003cem\u003eBurkholderia\u003c/em\u003e (OTU10) and \u003cem\u003eEnterococcus\u003c/em\u003e (OTU13) possess dye degradation functions in DDMY1, and it was hypothesized that the negative correlation presented by the two inhibits the functionality of DDMY1. During the evolution of microbial communities, while continually enhancing functionality, there emerged community relationships that inhibited the expression of that functionality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Effects of complex activators on the evolution of functional microbiome\u003c/h2\u003e \u003cp\u003ePcoA analyses, NMDS analyses, and correlation analyses are able to assess the effects of different domestication processes on the evolution of functional microbiome communities. Network correlation network analysis enables the study of the co-existence of community species in the presence of different domestication processes. According to the PcoA analysis depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a), it could be observed that DDMY1, which had been domesticated exclusively by RBBR, exhibited a certain degree of similarity in the evolution of its microbiological population after 6 months of domestication, yet it remained within the same quadrant. However, after the addition of the complex activator, the relative coordinate points of the two groups of samples (D1A vs. 6D1A) were already in different quadrants compared to D1 and 6D1. And D1A was not in the same quadrant as its sample 6D1A obtained after 6 months of domestication, and the evolution of the microbiome was obvious. While for the microbiome DDMY2, which had been co-domesticated by RBBR and tea residue, the effect produced by the composite activator was less pronounced, remaining largely within the same quadrant, the evolution of the microbiome was not evident. This could also be clearly discerned through the NMDS analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b)). For DDMY2, the effects produced by prolonged domestication were likely to be greater than the effects of the complex activator.According to the correlation clustering analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (c), it could be seen that sample group D1 (D1A and 6D1A (X6D1A)) with the addition of the composite activator belonged to the same cluster as group D2 domesticated by tea residue. In summary, it was hypothesized that because the composite activator better simulated the activation function of tea dregs for the microbiome, both of them provided the same culture environment to promote the deterministic evolution of functional microbiome.\u003c/p\u003e \u003cp\u003eAccording to the network correlation analysis of DDMY1 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (d), it could be seen that the microbiome could be categorized into three plates, and both intra- and inter-plate interactions played very important roles. Plate I and Plate II were in a many-to-many mutualistic relationship, and \u003cem\u003enorank_f__PHOS-HE36\u003c/em\u003e (OTU16) connected the two plates. \u003cem\u003eNorank_f__PHOS-HE36\u003c/em\u003e (OTU16) was also the most highly correlated microorganism in the microbiome and was found to be mostly used in metal metabolism as well as in reaction systems such as denitrification \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In Plate I, \u003cem\u003eEnterococcus\u003c/em\u003e (OTU17) showed negative correlation with all microorganisms in Plate I. \u003cem\u003eEnterococcus\u003c/em\u003e (OTU17) had been documented by many to possess dye-degrading functions\u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, and was hypothesized to play an important role in the functional microbiome DDMY1. Li et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e found that the relative abundance of \u003cem\u003enorank_o__JG30-KF-CM45\u003c/em\u003e (OTU14) was negatively correlated with NH\u003csub\u003e3\u003c/sub\u003e. The reduction of azo dyes by \u003cem\u003eEnterococcus\u003c/em\u003e (OTU17) under parthenogenetic anaerobic conditions might have produced a certain amount of ammonia, leading to a negative correlation.\u003c/p\u003e \u003cp\u003ePcoA analysis, NMDS analysis, and correlation analysis revealed that the effect of complex activators on the deterministic evolution of functional microbiome was more pronounced compared to long-term domestication.\u003c/p\u003e \u003cp\u003eTherefore, the study used Fold change to reflect species characterization data with up- or down-regulation of abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(e)) as a way to further analyze the effect of complex activators on the evolution of functional microbiome. First for DDMZ1, the addition of the complex activator significantly increased the abundance of \u003cem\u003ePseudomonas\u003c/em\u003e and decreased the abundance of \u003cem\u003eLachnoclostridium\u003c/em\u003e. \u003cem\u003ePseudomonas\u003c/em\u003e had a better degradation effect \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, which is also corroborated by the fact that the addition of activators contributed to the functional microbiome decolorization ratio. Huang et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e also showed that the addition of tea polyphenols contained in the complex activator resulted in a decrease in \u003cem\u003eLachnoclostridium\u003c/em\u003e abundance. Zeng et al\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. believe that \u003cem\u003eBurkholderia-Paraburkholderia\u003c/em\u003e was negatively correlated with epigallocatechin gallate. The results of the decrease in the abundance of \u003cem\u003eBurkholderia-Paraburkholderia\u003c/em\u003e, which were observed due to the addition of activators, were consistent with their previous findings. For DDMY2, the effect of activators was smaller compared to DDMY1, with the largest increase in the abundance of \u003cem\u003eAzoarcus\u003c/em\u003e and a significant decrease in the abundance of \u003cem\u003enorank_f__Veillonellaceae\u003c/em\u003e. Polysaccharides from Fuzhuan brick tea (FBTPS), one of most important bioactive components in tea. Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e found that the addition of FBTPS induced a significant increase in the abundance of \u003cem\u003enorank_f__Veillonellaceae\u003c/em\u003e. DDMY2 had been domesticated with tea leaf residue, which contains FBTPS but not in the added complex activator, so it was hypothesized that the absence of this substance had led to a decrease in the abundance of \u003cem\u003enorank_f__Veillonellaceae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eDDMY1 and DDMY2 were subjected to activator addition and six-month domestication to study their evolutionary process, and the results of βNTI/RCbray community structure analysis were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (f). As can be seen, all of the evolution of the two groups of colonies was stochastic. Genetic drift was dominant, with a small amount of diffusion limitation. The functional microbiome DDMY1 after 6 months of domestication showed a clear diffusion limitation between the other three groups. It was hypothesized that diffusion limitation occurred mainly due to spatial as well as temporal separation and the inability of microorganisms to interact between samples, resulting in ecological drift over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Effects of changing environmental factors on the evolution of functional microbiome\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Different structural types of dye electron acceptors\u003c/h2\u003e \u003cp\u003eFunctional microbiome DDMZ1 had a better effect on the treatment of dyeing wastewater, and different structural types of dyes affected the community structure and the evolution of the microbiome. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(I) represented the graph of structural changes of functional microbiome under the influence of different structural types of dye electron acceptors. The effect of different types of dyes on the structure of microbiome was evident, with significant differences in community structure in the presence of azo dyes, triphenylmethane and anthraquinone dyes. DDMZ1 was domesticated from the azo dye RB5. The community structure of the double azo dye (RB5) and triple azo dye (CBE) groups was similar to that of the functional microbiome DDMZ1 in the absence of the dye, and differed somewhat from that in the presence of the single azo dye (AO7). The larger proportion (37.62%) of \u003cem\u003eEnterococcus\u003c/em\u003e (OTU12) was the main reason for the difference in community structure between those affected by single azo dyes and those affected by other azo dyes. A number of studies had shown that \u003cem\u003eEnterococcus\u003c/em\u003e (OTU12) had been applied to dye biodegradation because of its better functionality\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eEnterococcus\u003c/em\u003e (OTU12) was hypothesized to be capable of degrading dyes with relatively simple structures and to had relatively low tolerance for more complex and toxic azo dyes. \u003cem\u003ePseudomonas\u003c/em\u003e (OTU19) showed a large percentage (87.58%) in anthraquinone dyes, which was hypothesized to have a better removal ability for anthraquinone dyes, which was in agreement with the findings of Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eStenotrophomonas\u003c/em\u003e (OTU11, 26.80%), \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (OTU18, 38.36%) and \u003cem\u003ePseudomonas\u003c/em\u003e (OTU19, 21.51%) were more predominant in the MG group, which differed considerably from the community structure under the influence of the azo dye group. The structure of the community affected by triphenylmethane dyes was clearly more complex compared to azo dyes as well as anthraquinone dyes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Different concentrations of dye electron acceptors\u003c/h2\u003e \u003cp\u003eThe effects of different concentrations of azo dye RB5 on the community structure of functional microbiome were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(II). The composition of the microbiome was more complex in the absence of dye presence, while the community structure was gradually simplified as the dye concentration increased. \u003cem\u003eStenotrophomonas\u003c/em\u003e (OTU5) had a large occupancy at low concentrations, but no significant occupancy was seen at the high concentration of 400 mg/LRB5, hence it was hypothesized that the high toxicity of the dye at its high concentration had an effect on its activity. The abundance of \u003cem\u003eBurkholderia-Paraburkholderia\u003c/em\u003e (OTU6) was higher at 400 mg/LRB5 (58.37%) than 200 mg/LRB5 (28.79%). It was hypothesized that \u003cem\u003eBurkholderia-Paraburkholderia\u003c/em\u003e (OTU6) played an important role in the decolorization of dyes at high concentrations by functional microbiome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Different types of electron donor\u003c/h2\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(III-a), it could be seen that the results of functional microbiome changed significantly in the presence of different electron donors. The microbiome of the YE and MN groups were more similar. It was hypothesized that the community structure had stabilized because the domestication medium for the functional microbiome contains YE, and that the microbiome changes less when YE was not added. \u003cem\u003eLactococcus\u003c/em\u003e (OTU13) had the largest percentage in YE (58.42%) vs. MN (71.72%), which was not a major functional microorganism as presumed based on the decolorization ratios of different electron donors. When the electron donor was FRU group, the microbiome was more complex, in which \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (OTU18) dominated. Tacas et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e found that \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (OTU18) had a better extracellular electron transfer capacity, which favors dye degradation. When YE was compounded with FRU as an electron donor, the community structure was similar to that of the YE group, and it was hypothesized that YE group had a greater effect on the microbiome compared to FRU group.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(III-b) showed the ZIPI analysis for the effect of different donors on microbiome evolution, and the remaining three types of nodes except Peripherals were categorized as key nodes. \u003cem\u003eChroococcidiopsis_SAG_2023\u003c/em\u003e (OTU1, (0.6667, 0.866)) and \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e (OTU3, (0.625, 1.1547) were all of the Connectors type, nodes with a high degree of connectivity between the two modules, and could be fully categorized as critical nodes. Combined with the Circos plot and decolorization ratio, \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e (OTU3) had the largest percentage in the YE\u0026thinsp;+\u0026thinsp;FRU group, which had the highest decolorization ratio, and thus it was hypothesized that \u003cem\u003eBurkholderia-Caballeronia- Paraburkholderia\u003c/em\u003e (OTU3) played an important functional role in the microbiome.\u003c/p\u003e \u003cp\u003eIn summary, environmental factors had a significant impact on community structure, and changes in community structure also affected the evolution of the microbiome. YE group electron donors had the greatest influence on the microbiome. Burkholderia-Caballeronia-Paraburkholderia was the key functional microorganisms within the microbiome. The conclusions drawn can be leveraged to strategically guide the evolution of the microbiome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.5. A decade long application of functional microbiome evolutionary studies\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Changes in microbiome diversity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the Alpha diversity data during the decade of DDMZ1 domestication and application. The Coverages data indicate that all measurements were reliable. Simpson's index could be used to characterize the concentration of community composition, with higher values indicating higher concentration and lower diversity. According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it could be seen that Shannon index gradually increased and Simpson index gradually decreased over the five years from 2015\u0026ndash;2019, and the diversity of functional microbiome gradually increased. The diversity of functional microbiome gradually decreased over the four years from 2021\u0026ndash;2024. The overall trend of functional microbiome diversity over the decade was first increasing and then decreasing. It was hypothesized that in the initial stage, the increase in diversity might be due to the gradual diversification of the microbiome through adaptive evolution to new habitats under different environmental conditions. However, over time, long periods of unchanging culture environments might lead to the dominance of certain strains, which might result in a decrease in diversity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlpha diversity during a decade of DDMZ1 domestication and application\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSobs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAce\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChao\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.484319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.460219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.276632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.326386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.603299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.330105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.897976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.769325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.380235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.917385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.424427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Evolution of functional microbiome\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a) showed the community structure composition and changes of functional microbiome DDMZ1 during the ten-year domestication and application process. It was obvious that the microbes in the functional microbiome mainly belong to two phyla, \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e. \u003cem\u003eBurkholderia\u003c/em\u003e and \u003cem\u003eunclassified_f__Enterobacteriaceae\u003c/em\u003e dominated the initial microbiome, but the abundance of both gradually decreased during microbiome domestication and prolonged application. \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eStenotrophomonas\u003c/em\u003e gradually dominated. Many studies had also shown that the three microorganisms mentioned above could play an important role in dye degradation\u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b) demonstrated the results of calculating the stochastic and deterministic evolution of functional microbiome based on βNTI/RCbray, and it was clearly visible that the evolution of functional microbiome over the decade had shown significant deterministic evolution. The quantified interannual variation was mainly distributed in the βNTI\u0026thinsp;\u0026gt;\u0026thinsp;2 range, with dispersed genetic distances for OTUs, showing that the interannual variation of the microbiome was mainly dominated by biological interactions. Whereas annual variation was mainly in the βNTI\u0026lt;-2 range, the genetic distance of OTUs converged and was dominated by environmental selection. Deterministic evolution helps to maintain and optimize the functional microbiome\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Functional microbiome was screened for microbial species that were adapted to specific environments through a deterministic evolutionary process under specific environmental conditions. These microorganisms worked together to perform specific functions through interaction and coordination. The microbiome had maintained deterministic evolution, presumably due to the long-term consistency of the culture environment of the microbiome over a ten-year period.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c) illustrated the key species in the microbial association network of functional microbiome over the decade. \u003cem\u003eBurkholderia\u003c/em\u003e (0.625, 1.1547) with Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62, the study concluded that nodes with high connectivity between the two modules were keystone species for functional microbiome. However, changes in community structure over the decade showed that the key species, \u003cem\u003eBurkholderia\u003c/em\u003e, decreased as a percentage of the microbiome with microbiome application. Several studies had demonstrated the strong degradation capacity of the key species \u003cem\u003eBurkholderia\u003c/em\u003e in dyeing wastewater\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Therefore, in order to improve the dye degradation ability of the microbiome, subsequent artificial regulation of their abundance is considered necessary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Prediction of functional microbiome\u003c/h2\u003e \u003cp\u003eRapid prediction of functional profiles of microbiome from 16SrRNA gene sequences contributes to a deeper understanding of the functional properties of microbiome in specific environments or conditions. From Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, it could be seen that the main functions of DDMZ1 were Carbohydrate metabolism and Lipid metabolism. Both are basic and important functions in microbial communities. They provide energy and building materials for microorganisms. This function of Xenobiotics biodegradation and metabolism is important for the maintenance of ecosystems. Overall, the expression of Xenobiotics biodegradation and metabolism function gradually increased during the decade, which showed that the functional microbiome DDMZ1 was able to show stronger functionality after long-term domestication and application. It was also evident that the functional expression of DDMZ1 in the microbiome remained essentially stable over the ten-year period, which corroborates the deterministic evolution derived from 2.5.2 Evolution of functional microbiome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Conclusions","content":"\u003cp\u003eThe functional microbiome had good decolorization ability for dye wastewater. The changes of environmental factors had a great influence on the community structure of functional microbiome. Under the influence of short-term environmental factors, the microbiome was dominated by stochastic evolution. However, long-term tracking and monitoring of the functional microbiome revealed that the functional microbiome was basically dominated by deterministic evolution during 10 years of domestication and application. This indicates that the functional microbiome had better stability as well as adaptability when the culture conditions were kept constant, which was conducive to maintaining and optimizing the function of the microbiome. And the interannual evolution of microbiome was mainly influenced by biological interactions, while the intra-annual evolutionary process was mainly dominated by environmental selection. At the same time the functional microbiome expression remained stable and the function of Xenobiotics biodegradation and metabolism expression gradually increased.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cbr\u003e☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cbr\u003e☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:\u003c/p\u003e\n\u003cp\u003eXin Liu：Writing – original draft, Formal analysis, Conceptualization, Investigation\u003c/p\u003e\n\u003cp\u003eYiting Qin: Writing – original draft, Formal analysis, Software, Methodology, Visualization\u003c/p\u003e\n\u003cp\u003eJiajie Liu: Resources, Project administration\u003c/p\u003e\n\u003cp\u003eWanting Li: Resources, Software, Supervision\u003c/p\u003e\n\u003cp\u003eXinyi Guo: Software, Validation, Visualization\u003c/p\u003e\n\u003cp\u003eNa Liu: Project administration, Funding acquisition\u003c/p\u003e\n\u003cp\u003eQingyun Zhang: Project administration, Funding acquisition\u003c/p\u003e\n\u003cp\u003eXuehui Xie: Writing – review and editing, Validation, Project administration, Funding acquisition\u003c/p\u003e\n\u003cp\u003eYe Chen: Writing – review and editing, Methodology, Resources, Funding acquisition\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXin Liu：Writing \u0026ndash; original draft, Formal analysis, Conceptualization, InvestigationYiting Qin: Writing \u0026ndash; original draft, Formal analysis, Software, Methodology, VisualizationJiajie Liu: Resources, Project administrationWanting Li: Resources, Software, SupervisionXinyi Guo: Software, Validation, VisualizationNa Liu: Project administration, Funding acquisitionQingyun Zhang: Project administration, Funding acquisitionXuehui Xie: Writing \u0026ndash; review and editing, Validation, Project administration, Funding acquisitionYe Chen: Writing \u0026ndash; review and editing, Methodology, Resources, Funding acquisition\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the Fundamental Research Funds for the Central Universities, China (grant numbers 2232024A-02，2232022G-01). The Innovation Team for Reducing Pollution and Carbon Emissions in the Agricultural Ecological Environment of Northern Anhui (grant number 2024TD02). The scientific research program of Anhui Provincial Education Department (grant number 2025AHGXZK60086), the Innovation and Entrepreneurship Training Program for College Students (grant number 202110363068).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarbelli-Lopez, M.S., M.P. Peralta, L. Levin, et al., \u003cem\u003eEffect of co-cultivation of white and brown rot species on basidiome production, lignocelluloytic enzyme activity and dye decolourisation\u003c/em\u003e. Bioresource Technology, 2024. 395: p. 130397.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, H.-Q., N. Hou, Y.-R. 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Colloids and Surfaces B: Biointerfaces, 2018. 172: p. 526\u0026ndash;531.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"npj-biofilms-and-microbiomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjbiofilms","sideBox":"Learn more about [npj Biofilms and Microbiomes](http://www.nature.com/npjbiofilms/)","snPcode":"41522","submissionUrl":"https://submission.springernature.com/new-submission/41522/3","title":"npj Biofilms and Microbiomes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Functional microbiome, Microbiome evolution, Activator, Electron donor, Degradation decolorization","lastPublishedDoi":"10.21203/rs.3.rs-9048998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9048998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDye wastewater seriously damages the water ecosystem and threatens human health. Functional microbiome showed significant advantages in dye wastewater treatment with their excellent degradation ability. The evolution of the microbiome has important implications for its functionality. The study provided insights into the evolutionary mechanisms of functional microbiome in dye wastewater treatment, based on research data spanning from 2015 to 2024. It was found that the changes of environmental factors had a great influence on the structure of microbiome, which was dominated by stochastic evolution in the short term. However, long-term follow-up monitoring showed that the functional microbiome was dominated by deterministic evolution, displaying good stability and adaptability. In addition, it was worth noting that interannual evolution of the microbiome was mainly influenced by biological interactions, whereas the intra-annual evolutionary process was mainly dominated by environmental selection. These results provide more efficient, stable and reliable biological solutions to environmental problems such as wastewater treatment.\u003c/p\u003e","manuscriptTitle":"Stochastic and deterministic evolutionary processes in microecosystem of dye-degrading functional microbiomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 12:52:04","doi":"10.21203/rs.3.rs-9048998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-19T13:29:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191319000338876762746413664588621198196","date":"2026-05-18T10:28:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28509973418936852957226037588925764949","date":"2026-05-11T08:18:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T14:48:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T15:50:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T01:18:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Biofilms and Microbiomes","date":"2026-03-06T09:37:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-biofilms-and-microbiomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjbiofilms","sideBox":"Learn more about [npj Biofilms and Microbiomes](http://www.nature.com/npjbiofilms/)","snPcode":"41522","submissionUrl":"https://submission.springernature.com/new-submission/41522/3","title":"npj Biofilms and Microbiomes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"073de510-b79f-495d-8500-d4694445850c","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T13:29:56+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"191319000338876762746413664588621198196","date":"2026-05-18T10:28:25+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"28509973418936852957226037588925764949","date":"2026-05-11T08:18:20+00:00","index":18,"fulltext":""},{"type":"reviewersInvited","content":"19","date":"2026-05-04T14:48:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67908653,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":67908654,"name":"Biological sciences/Ecology"},{"id":67908655,"name":"Earth and environmental sciences/Ecology"},{"id":67908656,"name":"Earth and environmental sciences/Environmental sciences"},{"id":67908657,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-12T12:52:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 12:52:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9048998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9048998","identity":"rs-9048998","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}