Yeast chemogenomic screen identifies distinct metabolic pathways required to tolerate exposure to phenolic fermentation inhibitors ferulic acid, 4-hydroxybenzoic acid and coniferyl aldehyde
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it