Advancing bio-recycling of nylon monomers through CRISPR-assisted engineering
Bibliographic record
Abstract
Plastic waste is a global environmental crisis, and nylon—a widely used polyamide—contributing significantly due to its extensive applications in textiles, automotive components, and packaging. Post-lifecycle degradation of nylon releases monomers like 1,6-hexamethylenediamine (HD) and 6-aminocaproic acid (ACA), which persist in ecosystems, posing toxicity and bioaccumulation risks. In this study, we employed a CRISPR-assisted directed evolution (CDE) to engineer Pseudomonas putida KT2440 for efficient utilization of HD as the sole nitrogen source, coupling its degradation to bacterial growth. Genomic and transcriptomic analyses prioritized potential enzymes involved in HD degradation. Using CRISPR interference (CRISPRi) and expert-guided screening, we identified three key enzymes including KgtP transporter, AlaC transaminase, and FrmA dehydrogenase that are critical to the KAF pathway. The functionality of these enzymes was confirmed in P. putida and further validated through heterologous expression in Escherichia coli . The CDE and growth-coupled strategy, together with the KAF pathway we discovered, is essential for our future efforts to engineer synthetic bacterial consortia capable of degrading mixed plastic monomers. In the long term, we envision integrating these consortia with synthetic biology tools to degrade complex plastic polymers and convert them into valuable chemicals, advancing circular economic efforts for sustainable plastic waste management and environmental protection. Synopsis CRISPR systems engineered Pseudomonas putida for efficient nylon monomer degradation, unveiling a novel pathway and advancing plastic waste recycling and environmental mitigation 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".