An effective bioremediation approach for enhanced microbial degradation of the veterinary antibiotic sulfamethazine in an agricultural soil
Bibliographic record
Abstract
Abstract Background The veterinary antibiotic Sulfamethazine (SMZ) contaminates soils via manure applications. Like other soil contaminants (herbicides, fungicides, and nematicides), it has to be degraded. The main challenge is that SMZ biodegradation with bacteria is impeded, since SMZ is a bacteriostatic antibiotic, designed to block microbes in their growth. Results In this study, we enriched the indigenous soil microbial community (including the single strain Microbacterium sp. C448, adapted to SMZ degradation) from a Canadian soil and we present a suitable approach, for soil remediation by inoculating a German soil with this microbial community established on carrier particles, at environmentally relevant concentrations of 1 mg kg−1. When compared with the isolated SMZ-degrading strain (also obtained from Canada), the microbial community outperformed the mineralization rates of the isolated strain in soil. The negligible soil native SMZ mineralization was successfully increased to 44 and 57 % within 46 days, by the microbial community. The sustainability of this increased SMZ mineralization capacity was proven by the rapid mineralization of a second application of 14C-SMZ 112 days after the first. Conclusions The pronounced SMZ mineralization and the high amount of non-extractable 14C-residues (NER) in the inoculated soil indicate that the NER are mainly of biogenic origin (metabolically fixed 14C). Therefore, the applied inoculation approach decreased the risk of persistent non-extractable SMZ residues. Together with our former studies, this specific soil inoculation approach was tested for three substances with different physico-chemical properties, indicating that this soil bioremediation technique might also be used for other substances. Graphical abstract Biodegradation of sulfamethazine. The veterinary antibiotic sulfamethazine can be excreted by treated animals and introduced to soils via manure application. In our approach we decontaminated soil in a lab experiment with a SMZ degrading strain and the appropriate soil microbial community. With this approach 57 % of the applied SMZ were mineralized within 46 days in soil.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".