Recombinant expression of <i>Aspergillus niger</i> GH10 endo‐xylanase in <i>Pichia pastoris</i> by constructing a double‐plasmid co‐expression system
Bibliographic record
Abstract
Abstract BACKGROUND Previous work has shown that GH10 endo‐xylanase presented better synergistic cooperation with cellulase to achieve effective hydrolysis. However, most currently reported commercial xylanolytic enzymes from Aspergillus niger belong to GH11 and there have been few studies regarding GH10 xylanase. In addition, to increase overall expression of heterologous endo‐xylanase in Pichia pastoris, a double‐plasmid co‐expression method, including the construction of recombinant P. pastoris using one expression vector followed by inserting another vector which targeted to different locus, was introduced. RESULTS A GH10 xylanase gene from A. niger BE‐2 (XynC) was optimized and constitutively expressed in P. pastoris GS115. Compared with the conventional single‐plasmid method, it appeared that the double‐plasmid strategy could considerably increase the XynC yield by ∼33%. To further improve enzyme production, cultivation conditions were optimized at shake‐flask level and then scaled up to a 5‐L bioreactor. Through high cell‐density fermentation, the expression level of GH10 XynC reached 1650 U mL−1. The XynC showed maximum activity at 55 °C and pH 5.0, and exhibited an excellent stability over a wide range of pH from 4.5 to 7.0. The kinetic parameters Km and Vmax values for beechwood xylan were 3.5 mg mL−1 and 2327 U mg−1, respectively. CONCLUSION The double‐plasmid co‐expression strategy introduced herein could greatly improve the XynC yield in P. pastoris. The GH10 xylanase from A. niger BE‐2 shared similar reaction conditions with the existing enzymatic hydrolysis, which might be a good candidate to assist cellulases for industrial application. © 2019 Society of Chemical Industry
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".