Bioremediation of atrazine and its metabolites using a novel Bacillus thuringiensis spore-based enzyme display system
Bibliographic record
Abstract
Bacillus thuringiensis spore-based display system has been shown to be an excellent biocatalyst platform to express the high density of the targeted enzymes to catalyze the chemical reactions. To demonstrate Bacillus thuringiensis spore-based display system can be a superior and more cost-effective approach for enzymatic bioremediation of contaminated soils as compared to conventional enzymatic techniques, this study focused on exploring and testing the utility of the biocatalyst system for decontamination of a persistent contaminant, atrazine (ATR). The first three enzymes, AtA, AtzB, and AtzC, in Pseudomonas sp. strain ADP ATR degradation pathway were incorporated into the B. thuringiensis spore display system to decontaminate ATR and its metabolites to less toxic metabolite. The environmental risk of applying the B. thuringiensis spore display system in the field was also investigated. Our findings showed that the AtzA-bearing spore exhibited enhanced enzymatic activity and stability and less washout as compared to free recombinant AtzA enzymes in soil. More than 90 percent of applied ATR (46 M [mu]10 mg L-1]) in the soil was detoxified by AtzA-bearing spores in 24 hours. Furthermore, the optimal ratio of AtzA- and AtzB-bearing spores decontaminate more than 80 percent fortified 34.5 nM (7.5 [mu]g L-1) of ATR in surface water within 24 hours, and the fortified ATR and its metabolite, hydroxy atrazine (HA), in surface water was completely converted to the end metabolite N-isopropylammelide (NiPA) at the end of 96 hours. Additionally, more than 67 percent of applied NiPA was degraded by 1 mg AtzC-bearing spores with the corresponding production of cyanuric acid in water. The enzymatic kinetics study of AtzC-bearing spores provides insightful information for determining the optimal ratio among AtzA-, AtzB-, and AtzC-bearing spores in the one-pot reactions for ATR degradation. Lastly, this is the first study to monitor the germination of B. thuringiensis spore in both surface water and soil. We found little to no germination from AtzA-bearing spores as observed in the sterile surface water incubated in the laboratory, while a small percentage (2.1-2.4 percent) of AtzA- bearing spores germinated in the sterile soil incubated in the laboratory after 4 days. In conclusion, this study demonstrated ATR decontamination by multiple enzymes delivered by B. thuringiensis spore in one-pot reactions in surface water and laid an important foundation for the environmental application of the novel B. thuringiensis spore display system. A review of possible delivery system for the novel enzyme expression platform was also included. Immediately after the outburst of the global COVID-19 pandemic in 2020, the author endeavored to serve the community as a scientist to take on the major challenge of public health. Therefore, the author has decided to suspend the Ph.D. study and direct all the efforts and energy to contribute her scientific knowledge to protect public health in responding to one of the most critical global health crises in human history. Through joining the Missouri Wastewater Surveillance Taskforce since the pandemic, a novel approach was developed to capture the real-time population dynamic for normalizing the SARS-CoV-2 viral load in the wastewater. This strategy was superior to the current approach recommended by the CDC, and it has been successfully used for predicting the infected population within the communities. This novel normalization approach has been adopted by the federal City of Bon, Germany, and the Canadian government agencies for tracking the COVID-19 infection using WBE. This strategy can be implemented to track not only infectious diseases but also to map opioids or other drug usage in the community in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".