Adaptive evolution and metabolic engineering of a cellobiose- and xylose- negative Corynebacterium glutamicum that co-utilizes cellobiose and xylose
Bibliographic record
Abstract
BACKGROUND: An efficient microbial cell factory requires a microorganism that can utilize a broad range of substrates to economically produce value-added chemicals and fuels. The industrially important bacterium Corynebacterium glutamicum has been studied to broaden substrate utilizations for lignocellulose-derived sugars. However, C. glutamicum ATCC 13032 is incapable of PTS-dependent utilization of cellobiose because it has missing genes annotated to β-glucosidases (bG) and cellobiose-specific PTS permease. RESULTS: We have engineered and evolved a cellobiose-negative and xylose-negative C. glutamicum that utilizes cellobiose as sole carbon and co-ferments cellobiose and xylose. NGS-genomic and DNA microarray-transcriptomic analysis revealed the multiple genetic mutations for the evolved cellobiose-utilizing strains. As a result, a consortium of mutated transporters and metabolic and auxiliary proteins was responsible for the efficient cellobiose uptake. Evolved and engineered strains expressing an intracellular bG showed a better rate of growth rate on cellobiose as sole carbon source than did other bG-secreting or bG-displaying C. glutamicum strains under aerobic culture. Our strain was also capable of co-fermenting cellobiose and xylose without a biphasic growth, although additional pentose transporter expression did not enhance the xylose uptake rate. We subsequently assessed the strains for simultaneous saccharification and fermentation of cellulosic substrates derived from Canadian Ponderosa Pine. CONCLUSIONS: The combinatorial strategies of metabolic engineering and adaptive evolution enabled to construct C. glutamicum strains that were able to co-ferment cellobiose and xylose. This work could be useful in development of recombinant C. glutamicum strains for efficient lignocellulosic-biomass conversion to produce value-added chemicals and fuels.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".