Engineering a Cross-Feeding Synthetic Bacterial Consortium for Degrading Mixed PET and Nylon Monomers
Bibliographic record
Abstract
Plastics are indispensable to modern life, but their widespread use has created an environmental crisis due to inefficient waste management. Mixed plastic waste, comprising diverse polymers, presents significant recycling challenges due to the high costs of sorting and processing, leading to ecosystem accumulation and harmful by-product generation. This study addresses this issue by engineering a synthetic bacterial consortium (SBC) designed to degrade mixed plastic monomers. The consortium pairs Escherichia coli Nissle 1917, which uses ethylene glycol (EG), a monomer derived from polyethylene terephthalate (PET), as a carbon source, with Pseudomonas putida KT2440, which metabolizes hexamethylenediamine (HD), a monomer from nylon-6,6, as a nitrogen source. Adaptive evolution of the SBC revealed a novel metabolic interaction where P. putida developed the ability to degrade both EG and HD, while E. coli played a critical role in degrading glycolate, mitigating its by-product toxicity. The evolved cross-feeding pattern enhanced biomass production, metabolic efficiency, and community stability compared to monocultures. The consortium’s performance was validated through flux balance analysis (FBA), high-performance liquid chromatography (HPLC), and growth assays. These findings highlight the potential of cross-feeding SBCs in addressing complex plastic waste, offering a promising avenue for sustainable bioremediation and advancing future polymer degradation strategies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".