Jiandong Jiang

Microbial communities play a vital role in biogeochemical cycles, allowing the biodegradation of a wide range of pollutants. The composition of the community and the interactions between its members affect degradation rate and determine the identity of the final products. Here, we demonstrate the application of sequencing technologies and metabolic modeling approaches towards enhancing biodegradation of atrazine-a herbicide causing environmental pollution. Treatment of agriculture soil with atrazine is shown to induce significant changes in community structure and functional performances. Genome-scale metabolic models were constructed for Arthrobacter, the atrazine degrader, and four other non-atrazine degrading species whose relative abundance in soil was changed following exposure to the herbicide. By modeling community function we show that consortia including the direct degrader and non-degrader differentially abundant species perform better than Arthrobacter alone. Simulations predict that growth/degradation enhancement is derived by metabolic exchanges between community members. Based on simulations we designed endogenous consortia optimized for enhanced degradation whose performances were validated in vitro and biostimulation strategies that were tested in pot experiments. Overall, our analysis demonstrates that understanding community function in its wider context, beyond the single direct degrader perspective, promotes the design of biostimulation strategies.

Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Microbial communities are believed to outperform monocultures in the complete catabolism of organic pollutants via reduced metabolic burden and increased robustness to environmental challenges; however, the interaction mechanism in functional microbiomes remains poorly understood. Here, three functionally differentiated activated sludge microbiomes (S1: complete catabolism of sulfamethoxazole (SMX); S2: complete catabolism of the phenyl part of SMX ([phenyl]-SMX) with stable accumulation of its heterocyclic product 3-amino-5-methylisoxazole (3A5MI); A: complete catabolism of 3A5MI rather than [phenyl]-SMX) were enriched. Combining time-series cultivation-independent microbial community analysis, DNA-stable isotope probing, molecular ecological network analysis, and cultivation-dependent function verification, we identified key players involved in the SMX degradation process. Paenarthrobacter and Nocardioides were primary degraders for the initial cleavage of the sulfonamide functional group (−C–S–N– bond) and 3A5MI degradation, respectively. Complete catabolism of SMX was achieved by their cross-feeding. The co-culture of Nocardioides, Acidovorax, and Sphingobium demonstrated that the nondegraders Acidovorax and Sphingobium were involved in the enhancement of 3A5MI degradation. Moreover, we unraveled the internal labor division patterns and connections among the active members centered on the two primary degraders. Overall, the proposed methodology is promisingly applicable and would help generate mechanistic, predictive, and operational understanding of the collaborative biodegradation of various contaminants. This study provides useful information for synthetic activated sludge microbiomes with optimized environmental functions.

Arbuscular mycorrhizal fungi (AMF) affect multiple ecosystem functions and processes, the assemblages of which vary across ecosystems. However, the influences of environmental factors on AMF communities which may shape these communities are still largely unknown. In this study, AMF communities from roots and rhizosphere soils of Chenopodium ambrosioides in different natural soils were investigated. The root habitat showed significantly smaller numbers of OTUs and lower community richness compared to the rhizosphere soil habitat. Most OTUs in the root habitat were shared by the soil habitat from the same sampling site, indicating that rhizosphere soils represent a pool of AMF species, a fraction of which is recruited by plants. Most of the AMF in root habitats were Glomeraceae, suggesting recruitment preferences of AMF by plants. The relative contributions of environmental factors to explain variations in AMF community composition and phylogenetic structure were assessed. The results revealed soil properties predominantly explained the variation, followed by geographic and climate parameters which explained a small fraction independently, while the host plant showed few explanations. Overall, our results indicated that soil and root habitats as well as soil characters, especially pH, nitrogen and micronutrients (Zn and Cu) affected AMF communities significantly.