Metabolic redundancy is required for microbial polyethylene assimilation
Bibliographic record
Abstract
ABSTRACT Polyethylene (PE) is amongst the most recalcitrant synthetic polymers, and only a limited number of microbes have been shown to utilise it as their sole carbon and energy source. Here, we investigated the metabolic basis enabling the efficient assimilation of PE oxidised scission products and its prevalence in microbial communities naturally colonising plastic surfaces. Metabolomic profiling of weathered PE (W-PE) leachates revealed a highly diverse pool of oxidised aliphatic compounds varying in chain length and oxidation state. Different plastic-degrading bacteria consumed this complex mix of metabolites to distinct extents, with consumption efficiency positively correlating with the number of redundant genes associated with the β-oxidation pathway in their genomes. Comparative proteomic analysis of two Alcanivorax species exhibiting contrasting PE-leachate consumption capabilities confirmed that this functional redundancy was fully activated in response to the chemically complex PE-derived substrate pool. In contrast, it remained largely uninduced in the presence of the single, structurally simple alkane hexadecane. Hence, our results indicate that efficient PE assimilation requires a broad and redundant enzymatic repertoire capable of funnelling structurally diverse oxidised aliphatic intermediates through β-oxidation. Metagenomic analysis of plastisphere communities further revealed enrichment of fatty acid degradation genes in biofilms colonising both pristine and weathered PE—as expected more strongly in W-PE—compared with wood and surrounding water controls, supporting the ecological relevance of this mechanism for PE biodegradation. Together, these findings identify β-oxidation metabolic redundancy as a key trait underpinning microbial PE assimilation and suggest that plastic degradation may be occurring under natural environmental conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".