Introduction

In recent years, the ubiquitous and frequent detection of trace organic chemicals (TOrCs) in aquatic environments is of increasing concern [1]. Despite their low concentrations of occurrence ranging from a few ng/L to several μg/L, they pose serious adverse impacts on water security and ecosystem health [2]. Wastewater treatment plants (WWTPs) serve as a crucial barrier preventing these contaminants from entering aquatic systems. Although conventional activated sludge and membrane bioreactor technologies were originally designed to remove organic carbon, nitrogen, phosphorus and pathogens, TOrCs are also to some extent removed or transformed [3]. However, more complete TOrC removal requires additional treatment processes such as biodegradation, adsorption, oxidation and ozonation [4,5,6]. In particular, biodegradation has proven to be a promising approach due to its high removal efficiencies and low energy demand, which is achieved by the microbial communities via metabolism or co-metabolism. For example, TOrCs were degraded more efficiently in the wastewater treatment processes with nitrification, which was related to the activity of ammonia-oxidizing bacteria [7]. Microalgae-bacteria consortium also exhibit advantages in the energy, economy, and environment with great potential in removing various TOrCs [8]. To apply the biodegradation technology in full-scale in the long term and to improve the removal efficiencies, a better understanding of TOrC biotransformation mechanisms is desired. However, the degradation mechanisms remain elusive due to the complexity of microbial interactions occurring in the whole community.

In the last decades, cultivation-independent methodology using next generation sequencing has been developed to explore whole microbial systems [9,10,5). In the non-adaptation group, most model communities were dominated by one or two families (e.g., non-adapted sediment deep communities were dominated by 91% Pseudomonadaceae in average). In the pre-adapted group, species distributed more evenly (e.g., Bradyrhizobiaceae, Nocardiaceae, Pseudomonadaceae and Comamonadaceae composed the communities from pre-adapted sediment deep by 34%, 28%, 25%, and 11% on average, respectively) while maintaining some of the taxa that were also dominant in the model communities from the non-adapted inocula (e.g., Pseudomonadaceae, Nocardiaceae, Comamonadaceae). Some rare taxa e.g., Caulobacteraceae and Micrococcaceae occupying less than 2% in all inocula were abundant in the pre- and non-adapted model communities (18% and 73% of Caulobacteraceae in two model communities from adapted sand; 14–100% Micrococcaceae in all model communities from non-adapted soil).

Fig. 5
figure 5

Comparison of microbial structure of 95 potential model communities derived from pre- and non-adapted inocula at the family level

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We hypothesized that exposing whole communities to TOrCs allows the microbial community to go through a succession. In this succession, naturally abundant but vulnerable to TOrCs microorganisms reduce in numbers while allowing rarer microorganisms to slowly increase in numbers accompanied by members of the community that could act as cornerstones of resilience (i.e., the key taxa that maintain the stability and recover the functions of a community). Hence, pre-adaptation resulted in an overall reduced richness of the whole community while having a positive effect on the species that can grow on or survive TOrCs and could then thrive in the model communities.

TOrC removal performance by model communities

Overall, the thirty selected model communities in stage 3 were able to transform 17 of the 27 TOrCs. Ten residual TOrCs remained unchanged within the microbial model communities. Most of the communities could effectively degrade three to six TOrCs simultaneously (range: 1–9; 20% cutoff of removal rate), but to different degrees (Additional file 2: Fig. S6). Moreover, only model communities from pre-adapted communities exhibited transformation for more than six compounds. Specifically, the average removal of 17 TOrCs by 15 communities from the pre-adaptation group was 30.1%, and the percentage in the non-adaptation group was 22.4% (t-test, p = 0.16). There were more TOrCs degraded after pre-adaptation (n = 17), including some persistent compounds such as carbamazepine (46.5%) and gabapentin (25.2%), which were removed below 20% in the communities from the non-adaptation group (n = 10) (Fig. 6). Comparing the removal pattern of pre-adapted consortia and the subsequent model communities, we could observe that some model communities had similar removal on for example hydrochlorothiazide, ibuprofen and caffeine, some had higher removal than the whole community on sulfamethozaxole, gemfibrozil, climbazole and atenolol. There were even unchanged compounds by pre-adapted inocula that exhibited degradation by model communities (i.e., carbamazepine, diclofenac, gabapentin, citalopram, 4/5-methylbenzotriazole). The reduction of these 17 TOrCs was attributed to biodegradation as there was almost no abiotic degradation indicating by controls (< 3.5%).

Fig. 6
figure 6

TOrC removal by 15 model communities derived from pre-adapted inoculum (left), 15 model communities from non-adapted inoculum (middle), and 5 pre-adapted inocula (right). TOrCs with below 20% removal in all communities were excluded

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Relationship between TOrC removal performance and microbial traits

The relationships between 17 TOrC removal performance and potential microbial traits were investigated by principal coordinate analysis (PCoA) across microbial communities in the pre- and non-adaptation groups. The two main axes explained 41.4% and 16.8% of the variance, respectively (Fig. 7). A weak separation was observed between pre- and non-adaptation groups (Adonis, R2 = 0.095, p = 0.019) regarding their TOrC removal patterns. We hypothesized that phylogenetic diversity of model communities will influence TOrC removal rates, which could explain the dissimilarity between groups. However, the envfit analysis showed that the variation was not correlated with phylogenetic diversity (Adonis, R2 = 0.089, p = 0.3). We also tested the correlation with other variables, i.e., Shannon diversity, estimated growth rate, final cell counts (cell counts at d21), 27 TOrCs average removal rate and removal diversity (number of TOrCs above 20% simultaneous removal). We found that only average removal rate (Adonis, R2 = 0.82, p = 0.001) and estimated growth rate (Adonis, R2 = 0.20, p = 0.046) exhibited significant correlation with the variance, indicating the positive effect of model communities’ growth rates on the overall TOrC removal performance. Although phylogenetic diversity was not related to TOrC removal rates, it was found to have a positive correlation with removed TOrC numbers (cor.test, R2 = 0.39, p = 0.03).

Fig. 7
figure 7

Principal coordinate analysis (PCoA) of TOrCs removal by thirty model communities based on Euclidean dissimilarities. The environmental variables determined using envfit function in vegan were displayed as vectors, with a length proportional to the correlation between the variable and the PCoA ordination

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Discussion

In the absence of mechanisms of TOrC biotransformation by microbiome in the aquatic systems, it is often difficult to develop high-efficiency TOrC-specific biological treatment technologies in the engineering field. Deciphering the complexity of microbial functions can be achieved by starting with simplified systems, which relies on controllable bottom-up experiments with a few species [46]. In this study, by growing serially diluted tap water, sediment, sand, and soil under oligotrophic condition with TOrCs as the sole carbon source to generate self-assembled model communities, we investigated how the pre-adaptation of inoculum could impact the diversity of model communities and their removal on TOrCs. We found that while pre-adaptation process reduced the overall richness and diversity of inoculum, it resulted in greater diversity of model communities that can survive or degrade TOrCs. Pre-adaptation also enhanced TOrC removal performance in terms of overall removal rates and degradable TOrC numbers. Our hypothesis of phylogentic diversity’s influences on TOrC removal rates was rejected, as no significant correlation was identified. However, higher phylogenetic diversity in terms of e.g. phyla of model communities will require further investigations and could lead to more removed TOrCs.

Advantages of the model community establishment workflow

There have been a variety of microbial model communities developed for different purposes by different approaches. They can be mutant-based communities, the multispecies synthetic communities and the (semi-) natural communities as Bengtsson-Palme [14] suggested. Compared with those reported model communities [47,48,49], our workflow (Fig. 1) has the strong advantages of, firstly, high-throughput cultivation under highly controlled and well-understood conditions which allows large numbers of varying diversity communities providing a more reliable reflection of natural microbial ecosystems. We started with 4707 diluted communities in stage 2 and 2658 of them showed successful growth. In addition to our selection of 95 communities and resulting 30 taxonomically non-redundant ones, it is possible to enlarge the scale with changeable inoculum and synthetic media for different purposes [18, 20, 50]. Secondly, addressing the defects of conventional isolation and artificial assembly, our method has the potential to study uncultured microorganisms or strains that cannot survive individually, as well as the rare species which usually account for less than 5% of the community but can contribute disproportionately to the microbial functions [51]. For example, Micrococcaceae dominant in model communities were diluted from the non-adapted soil inoculum (Fig. 5), whereas it is only present as 0.3% in the initial soil communities.

Standardization effect of pre-adaptation on the microbial diversity

Pre-adaptation has been proven to be a key step in our method. It serves as a selective enrichment and a standardization process reducing and normalizing microbial diversity of varying inocula and further facilitating the species distribution in model communities. It is widely accepted that assessing the diversity of different microbes requires standardization [52], similarly, to establish model communities from various natural microbial systems via a common workflow, standardized initial samples are necessary. The pre-adaptation step offered an opportunity to scale down the species richness of varying communities to similar ranges and of the same dilution thresholds (e.g., 10 cells/mL), which made the subsequent model communities more comparable and adaptable for many different approaches (Fig. 2). In our study, the species richness and phylogenetic diversity of pre-adapted consortia decreased significantly, indicating an initial filtering effect of pre-adaptation resulted in microbial structure shifts and biodiversity loss in response to environmental stress [53]. The most noticeable change was the 59% to 8% reduction on rare taxa (≤ 2% abundance) (Additional file 2: Fig. S4), which appeared to be more sensitive to environmental pressure (in our case is increasing concentrations of TOrCs) than abundant species. The sensitivity of rare taxa is also supported by other studies [54,55,56], for example, Yi et al. [57] found that abundant microbes established cooperative interactions and competed for resources and ecological niches with rare species under the stress of benzo[a]pyrene. Different explanations have been proposed that rare taxa have the ability to become dominant in the community and with the increased abundance, they could have higher functional importance than the other abundant species, the so called “insurance effect” help microbial systems maintain their functions under environmental changes [54, 58]. This phenomenon was also observed in our experiments that Nocardiaceae developed from rarity to dominance after pre-adaptation (Additional file 2: Fig. S5). Interestingly, the loss of biodiversity in pre-adapted inocula did not lead to low diversity of model communities, in contrast, the species distribution evenness and phylogenetic diversity were notably higher in model communities derived from pre-adaptation than that from non-adaptation (Fig. 4). One explanation could be the TOrC stress acting as an environmental filter during the pre-adaptation process induced stable TOrC-degrader communities (specialists), while the non-adapted inoculum contained mostly taxa adapted to other environmental niches or generalists. Therefore, when we diluted the inoculum, with the reduction of microbial populations specialists had higher chances to co-exist due to their cooperation effect and maintain the functional stability [59]. However, the non-adapted microbes could compete for resources and niches, thus leading to diversity loss in model communities when facing sudden environmental fluctuations (similar with the non-adaptation to pre-adaptation trend). The microbial responses to TOrCs could be further studied at higher temporal resolution within the pre-adaptation period in terms of compositional and functional changes.

Phylogenetic diversity and microbial growth rates facilitated TOrC removal

Pre-adaptation also has positive effects on TOrC removal performance in terms of removal rates and degradable TOrC number. The necessity of adaptation ranging from several months to years of microbial communities for removing trace pollutants has been suggested previously [34, 60,61,62]. In these studies, some reported microbial adaptation resulted in the enhancement of TOrC degradation, whereas opposite results were found that pre-adaptation did not affect their attenuation. Our findings supported the former, in general, the overall 17 TOrCs removal rates were increased by pre-adapted model communities (30.1% vs. 22.4%). In addition, there were more TOrCs that could be transformed by adapted model communities (n = 17) than non-adapted model communities (n = 10). The other ten unchanged TOrCs in our experiments i.e., amisulpride, antipyrine, candesartan, fluconazole, primidone, sotalol, tramadol, trimethoprim, venlafaxin, 4-formylaminoantipyrine, have been reported as persistent compounds with very low removal in biological treatment (Additional file 1: Table S1). Interestingly, the TOrC removal diversity rather than the removal rates was found to be positively related to phylogenetic diversity of model communities.

Microbial diversity is considered to be essential for facilitating ecosystem functions via niche partitioning effects and interaction effects [63, 64]. Although we did not identify the diversity enhancement on overall TOrC removal rates, which is still in accordance with other studies [65, 66], the benefits from phylogenetic diversity were revealed by more degradable TOrCs. A possible explanation of this could be that the niche space overlap of more distantly related species is expected to be less than closer species, thus potentially favoring niche expansion to utilize more resources [26]. This niche expansion is even stronger when partners are metabolic specialists rather than generalists, and it allows the pairing of auxotrophic taxa with metabolic dependencies that could add additional functional genes [67]. For example, one of our model communities harbored a Phenylobacterium (Additional file 2: Fig. S4), which is the single described species lacks the vitamin B12 pathway and requires a B12 producer in its community, but potentially adds pathways related to chloridazon, antipyrin and pyramidon degradation [68]. As we discussed above, pre-adaptation could facilitate specialists for TOrC degradation, therefore, model communities have more possibilities to transform a wider range of compounds. In our case, pre-adapted model communities growing in the medium containing 27 mixed TOrCs can only remove nine compounds above 20%. This maximum number may be limited by their diauxic growth pattern (i.e., TOrCs are consumed sequentially or selectively) rather than co-utilization when faced with multiple carbon sources, especially when the carbons sources are toxic and refractory chemicals [69, 70]. The variance of TOrC removal rates between pre- and non-adaptation groups was found to be related to the estimated growth rate, indicating that the faster growth of model communities could predict the better removal of TOrCs. This could also be supported by the diauxic growth as the order of substrates consumption is determined by the biomass and growth rate when the same compounds are served as sole carbon sources [71]. More researches can be done by monitoring the TOrC biotransformation kinetics (in stage 3) which better indicates the relationship between cell growth and TOrC removal.

To our best knowledge, this is the first study that investigated a wide range of TOrC biotransformation based on different model communities derived from varying natural microbial systems. Although there have been previous attempts addressing TOrC biotransformation mechanisms [18, 49], they have limitations that either the systems themselves are too complex (e.g., soil and sludge) to characterize the key players and microbial functions, or too simplified (e.g., isolates degrading specific compound) to be applicable under environment conditions. Our method with reduced richness model communities serves as a compromise, which scales down the complex microbial interactions but their diverse combinations are still reflective of the actual environmental communities. This robust and standardized protocol can also provide a basis for studies interested in specific or diverse TOrCs (or other pollutants) biotransformation, as the inoculum and the identity of the chemical filters (in our case, the 27 TOrCs) are exchangeable. In future research, we can use these model communities to identify key driving agents of biotransformation (i.e., relevant microbes and their interactions, metabolic pathways down to the enzyme/gene level, and roles of co-substrates and cofactors) at a well-defined and standardized community level.

Conclusion

Complete understanding of TOrC biotransformation is essential for the development of biological treatment methods in aquatic environment. This study set up a robust and standardized workflow for establishing low complexity model communities to investigate TOrC biotransformation mechanisms driven by interacting taxa. Our experimental results demonstrated that the pre-adaptation of natural communities to TOrC environment reduced and standardized the diversity of varying inocula. In contrast, the pre-adaptation step improved the diversity of resulting model communities in terms of species distribution evenness and phylogenetic diversity as well as the average TOrC removal rates. The phylogenetic diversity was further found to be positively related to number of TOrCs that can be biodegraded simultaneously. However, the average TOrC removal was not well correlated to the observed changes in phylogenetic diversity but to the growth rates of model communities.