Bioconversion of glycerol to 1,3-propanediol in thin stillage-based media by engineered <i>Lactobacillus panis</i> PM1
Bibliographic record
Abstract
Thin stillage (TS) is a waste residue that remains after bioethanol production, and its disposal reflects the high costs of bioethanol production. Thus, the development of cost-effective ways to process TS is a pending issue in bioethanol plants. The aim of this study was to evaluate the utilization of TS for the production of the valuable chemical, 1,3-propanediol (1,3-PDO), by Lactobacillus panis PM1. Different fermentation parameters, including temperature, pH and strains [wild-type and a recombinant strain expressing a NADPH-dependent aldehyde reductase (YqhD) gene] were tested in batch and fed-batch cultivations. The highest 1,3-PDO concentration (12.85 g/L) and yield (0.84 g/g) were achieved by batch fermentation at pH-4.5/30 °C by the YqhD recombinant strain. Furthermore, pH-controlled batch fermentation reduced the total fermentation period, resulting in the maximal 1,3-PDO concentration of 16.23 g/L and yield of 0.72 g/g in TS without an expensive nutrient or nitrogen (e.g., yeast extract, beef extract, and peptone) supplementation. The addition of two trace elements, Mg(2+) and Mn(2+), in TS increased 1,3-PDO yield (0.74 g/g) without 3-hydroxypropionaldehyde production, the only intermediate of 1,3-PDO biosynthetic pathway in L. panis PM1. Our results suggest that L. panis PM1 can offer a cost-effective process that utilizes the TS to produce a value-added chemical, 1,3-PDO.
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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 | 0.000 |
| 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".