Yeast chemogenomic screen identifies distinct metabolic pathways required to tolerate exposure to phenolic fermentation inhibitors ferulic acid, 4-hydroxybenzoic acid and coniferyl aldehyde
Bibliographic record
Abstract
The conversion of plant material into biofuels and high value products is a two-step process of hydrolysing plant lignocellulose and next fermenting the sugars produced. However, lignocellulosic hydrolysis not only frees sugars for fermentation it simultaneously generates toxic chemicals, including phenolic compounds which severely inhibit yeast fermentation. To understand the molecular basis of phenolic compound toxicity, we performed genome-wide chemogenomic screens in Saccharomyces cerevisiae to identify deletion mutants that were either hypersensitive or resistant to three common phenolic compounds found in plant hydrolysates: coniferyl aldehyde, ferulic acid and 4-hydroxybenzoic acid. Despite being similar in structure, our screen revealed that yeast utilizes distinct pathways to tolerate phenolic compound exposure. Furthermore, although each phenolic compound induced reactive oxygen species (ROS), ferulic acid and 4-hydroxybenzoic acid-induced a general cytoplasmic ROS distribution while coniferyl aldehyde-induced ROS partially localized to the mitochondria and to a lesser extent, the endoplasmic reticulum. We found that the glucose-6-phosphate dehydrogenase enzyme Zwf1, which catalyzes the rate limiting step of pentose phosphate pathway, is required for reducing the accummulation of coniferyl aldehyde-induced ROS, potentially through the sequestering of Zwf1 to sites of ROS accumulation. Our novel insights into biological impact of three common phenolic inhibitors will inform the engineering of yeast strains with improved efficiency of biofuel and biochemical production in the presence hydrolysate-derived phenolic compounds.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".