Integrating metagenomics and high-throughput screening for bioprospecting marine producers of polyhydroxyalkanoates
Bibliographic record
Abstract
With the escalating global demand for eco-friendly materials, polyhydroxyalkanoates (PHAs) have emerged as promising biodegradable alternatives to conventional plastics. This study is based on the systematic investigation of microbial community distribution and functional composition associated with PHA biosynthesis across diverse marine environments through metagenomic analysis. Leveraging environmental characteristics and PHA synthesis potential, we developed targeted culture media. We successfully isolated 102 PHA-producing bacterial strains from various marine habitats, including brackish water interfaces and deep-sea sediments. Gas chromatography quantification revealed a significant correlation (R² = 0.67) between Nile red fluorescence intensity and PHA content in metagenomically-predicted strains, effectively addressing false-positive issues in fluorescence-based screening. We subsequently established a high-throughput screening platform combining microplate technology with fluorometric quantification, identifying 10 elite strains for fermentation optimization and PHA characterization. Notably, strain R1-4-2 (identified as Halomonas olivaria) demonstrated exceptional performance with 2.59 g l-1 cell dry weight (CDW) and 52.89% PHA content (1.52 g l-1 CDW), representing both a novel microbial resource for sustainable PHA production and a potential chassis for synthetic biology applications. These findings provide critical scientific insights and technical frameworks for advancing industrial-scale production of biodegradable biopolymers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".