Supplementing XYR1-mutated Trichoderma reesei strain cultivation with (SO2-ethanol-water) softwood pulp improves cellulase production
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 cellulolytic enzyme cost remains a major bottleneck in converting lignocellulose, especially softwoods, into fuels and chemicals. The aim of this study was to evaluate possibilities to increase enzyme production efficiency by using SO 2 -ethanol-water (SEW) pretreated softwood pulp with a Trichoderma reesei strain that expresses a mutant form of the main transcriptional regulator, XYR1, of cellulase- and hemicellulase genes leading to loss of glucose repression/carbon catabolite repression. The (hemi)cellulase enzyme cocktail of this strain was improved by expressing three heterologous enzymes, a beta-glucosidase, a CEL6 (CBH2) exoglucanase and a lytic polysaccharide mono‑oxygenase. Seven bioreactor cultivations were performed using glucose and different cellulose supplementations and glucose feed strategies. We showed that adding 3 %-w/v cellulose to the glucose medium and starting the glucose feed when the glucose was consumed from the batch medium, improved the protein production rate by over 80 % during the first five days compared to total absence of cellulose. With only 3 % cellulose addition to the batch phase, we estimate that over one third of time and total carbon source, including cellulose, could be saved compared to a production process without cellulosic substrate supplementation. Additionally, enzymes produced with SEW pulp in 119 h and those produced with glucose alone in 193 h both achieved 90 % glucose conversion when used for SEW pulp hydrolysis at a protein loading of 4–5 mg/g cellulose. Herein, we have shown that the M2883 strain can produce more than 29 FPU/mL of the complete set of cellulase enzymes both with and without cellulose supplementation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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