Supplementing XYR1-mutated Trichoderma reesei strain cultivation with (SO2-ethanol-water) softwood pulp improves cellulase production
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.
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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 | 0.000 |
| 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".