Shotgun Metagenomics Reveals the Benthic Microbial Community Response to Plastic and Bioplastic in a Coastal Marine Environment
Bibliographic record
Abstract
Plastic is incredibly abundant in marine environments but little is known about its effects on benthic microbiota and biogeochemical cycling. This study reports the shotgun metagenomic sequencing of biofilms fouling plastic and bioplastic microcosms staged at the sediment-water interface of a coastal lagoon. Community composition analysis revealed that plastic biofilms were indistinguishable in comparison to a ceramic biofilm control. By contrast, bioplastic biofilms were distinct and dominated by sulfate-reducing bacteria (SRB). Analysis of bioplastic gene pools revealed the enrichment of esterases, depolymerases, adenylyl sulfate reductases (aprBA), and dissimilatory sulfite reductases (dsrAB). The nearly 20-fold enrichment of a phylogenetically diverse polyhydroxybutyrate (PHB) depolymerase suggests this gene was distributed across a mixed microbial assemblage. The metagenomic reconstruction of genomes identified novel species of Desulfovibrio, Desulfobacteraceae, and Desulfobulbaceae among the abundant SRB, and these genomes contained genes integral to both bioplastic degradation and sulfate reduction. Findings indicate that bioplastic promoted a rapid and significant shift in benthic microbial diversity and gene pools, selecting for microbes that participate in bioplastic degradation and sulfate reduction. If plastic pollution is traded for bioplastic pollution and sedimentary inputs are large, the microbial response could unintentionally affect benthic biogeochemical activities through the stimulation of sulfate reducers.
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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 | 0.001 |
| 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) | 0.000 | 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".