Antibiotic-free whole-cell biocatalytic fermentation: <i>Escherichia coli</i> with surface-displayed PETases for sustainable plastic degradation
Bibliographic record
Abstract
Abstract Plastic pollution has increasingly burdened the environment, driving the need for natural degradation platforms that utilize microbial enzymes to break plastics down into monomers. In this study, we introduce a novel approach using Escherichia coli as a fermentative, antibiotic-free whole-cell biocatalyst with surface-displayed, genomically integrated PETases for efficient plastic degradation. PETases, a class of esterases, catalyze the hydrolysis of polyethylene terephthalate (PET) into mono-2-hydroxyethyl terephthalate (MHET). Surface display of these enzymes was achieved via gene fusions with an N-terminal cysteine (Cys) triacylated anchor, mediated by the Braun lipoprotein (Lpp) signal peptide. To circumvent issues associated with plasmids, - such as genetic instability and reliance on antibiotics - we used a Type I-F CRISPR-associated transposase to insert the genes directly into specific E. coli genome sites. Proper enzyme display and activity on the E. coli surface were confirmed through enzyme activity tests, Western blotting, and flow cytometry, with cells retaining PET degradation ability over multiple generations. High-performance liquid chromatography (HPLC) analysis assessed degradation efficiency, identifying byproducts such as bis (2-hydroxyethyl) terephthalate and terephthalic acid. This study establishes a proof-of-concept for efficient plastic degradation using engineered bacteria as robust, sustainable, and genomically stable whole-cell biocatalysts, providing a promising platform for addressing plastic waste management. Graphical Abstract One-sentence Abstract Escherichia coli was engineered as a fermentative, antibiotic-free whole-cell biocatalyst, featuring surface-displayed and genomically integrated PETases for efficient plastic degradation. This innovative approach has the potential to transform plastic recycling by enabling sustainable, large-scale degradation of plastic waste through environmentally friendly microbial systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".