Metabolic Engineering of a Glycerol-Oxidative Pathway in Lactobacillus panis PM1 for Utilization of Bioethanol Thin Stillage: Potential To Produce Platform Chemicals from Glycerol
Bibliographic record
Abstract
Lactobacillus panis PM1 has the ability to produce 1,3-propanediol (1,3-PDO) from thin stillage (TS), which is the major waste material after bioethanol production, and is therefore of significance. However, the fact that L. panis PM1 cannot use glycerol as a sole carbon source presents a considerable problem in terms of utilization of this strain in a wide range of industrial applications. Accordingly, L. panis PM1 was genetically engineered to directly utilize TS as a fermentable substrate for the production of valuable platform chemicals without the need for exogenous nutrient supplementation (e.g., sugars and nitrogen sources). An artificial glycerol-oxidative pathway, comprised of glycerol facilitator, glycerol kinase, glycerol 3-phosphate dehydrogenase, triosephosphate isomerase, and NADPH-dependent aldehyde reductase genes of Escherichia coli, was introduced into L. panis PM1 in order to directly utilize glycerol for the production of energy for growth and value-added chemicals. A pH 6.5 culture converted glycerol to mainly lactic acid (85.43 mM), whereas a significant amount of 1,3-propanediol (59.96 mM) was formed at pH 7.5. Regardless of the pH, ethanol (82.16 to 83.22 mM) was produced from TS fermentations, confirming that the artificial pathway metabolized glycerol for energy production and converted it into lactic acid or 1,3-PDO and ethanol in a pH-dependent manner. This study demonstrates the cost-effective conversion of TS to value-added chemicals by the engineered PM1 strain cultured under industrial conditions. Thus, application of this strain or these research findings can contribute to reduced costs of bioethanol production.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".