Inactivation of the transcription factor <i>mig1</i> (<i>YGL035C</i>) in <i>Saccharomyces cerevisiae</i> improves tolerance towards monocarboxylic weak acids: acetic, formic and levulinic acid
Bibliographic record
Abstract
Toxic concentrations of monocarboxylic weak acids present in lignocellulosic hydrolyzates affect cell integrity and fermentative performance of Saccharomyces cerevisiae. In this work, we report the deletion of the general catabolite repressor Mig1p as a strategy to improve the tolerance of S. cerevisiae towards inhibitory concentrations of acetic, formic or levulinic acid. In contrast with the wt yeast, where the growth and ethanol production were ceased in presence of acetic acid 5 g/L or formic acid 1.75 g/L (initial pH not adjusted), the m9 strain (Δmig1::kan) produced 4.06 ± 0.14 and 3.87 ± 0.06 g/L of ethanol, respectively. Also, m9 strain tolerated a higher concentration of 12.5 g/L acetic acid (initial pH adjusted to 4.5) without affecting its fermentative performance. Moreover, m9 strain produced 33% less acetic acid and 50-70% less glycerol in presence of weak acids, and consumed acetate and formate as carbon sources under aerobic conditions. Our results show that the deletion of Mig1p provides a single gene deletion target for improving the acid tolerance of yeast strains significantly.
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.
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 | 0.000 | 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".