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Record W3045208229 · doi:10.1021/acssuschemeng.0c02564

Synergism of Recombinant <i>Podospora anserina</i> <i>Pa</i>AA9B with Cellulases Containing AA9s Can Boost the Enzymatic Hydrolysis of Cellulosic Substrates

2020· article· en· W3045208229 on OpenAlexaff
Lingfeng Long, Huimin Yang, Hongyan Ren, Rukuan Liu, Fubao Sun, Zhihong Xiao, Jinguang Hu, Zhenghong Xu

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Calgary
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsSix Talent Peaks Project in Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceState Administration of Foreign Experts AffairsChina Postdoctoral Science FoundationMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsCellulaseCelluloseCellulosic ethanolPichia pastorisHydrolysisChemistryLignocellulosic biomassBiochemistryEnzymatic hydrolysisPodospora anserinaCarboxymethyl celluloseOrganic chemistryRecombinant DNA

Abstract

fetched live from OpenAlex

To achieve a fast and efficient cellulose hydrolysis with low enzyme loading remains a challenge for an economically feasible biomass biorefinery process. The synergism between cellulase and lytic polysaccharide monooxygenases (LPMOs) has demonstrated great promise, but it appears that such synergistic effects are highly substrate-dependent. In addition, the need for an efficient method of LPMO production has also limited its application. In this study, the production of Podospora anserina AA9B (PaAA9B), one of the most active LPMOs, was optimized by using double-plasmid coexpression in Pichia pastoris, and its hydrolysis-boosting effects on cellulases were assessed on model cellulosic materials and on our in-house-optimized atmospheric glycerol organosolv (AGO)-pretreated lignocellulosic substrates. The results showed that the double-plasmid coexpression technique successfully improved the PaAA9B production, where up to three times more PaAA9B (3.34 g L–1) was expressed in the 5 L bioreactor after 4 days of induction. The addition of recombinant PaAA9B to Cellic CTec2 (CTec2) significantly boosted the cellulose hydrolysis of a filter paper and Avicel by 2.3- and 1.4-folds, respectively, while no effects were observed on carboxymethyl cellulose (CMC). When lignocellulosic substrates were assessed, the PaAA9B also successfully enhanced the cellulose hydrolysis of both acid-catalyzed (ac) and alkali-catalyzed (al) AGO-pretreated sugarcane bagasses by 30 and 20%, respectively, at a 5% solid loading (w/v). At industrially relevant high-solid loading (20% w/v) hydrolysis of an al-AGO-pretreated substrate, the combination of 1.0 mg of PaAA9B and 3 FPU of cellulase per gram of substrate achieved 83% cellulose hydrolysis with 105 g L–1 of the corresponding glucose concentration after 72 h. These results indicated that the double-plasmid coexpression strategy is viable for the high-yield PaAA9B production. The mixture of PaAA9B and cellulase enzymes containing other AA9s exhibited a strong cosynergistic interaction and further boosted the enzymatic hydrolysis of both model cellulosic substrates and our optimized AGO-pretreated lignocellulosic biomass with industrially relevant enzyme loading. This study sheds light on the industrially relevant PaAA9B utilization in the enzymatic hydrolysis of lignocellulosic substrates.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.149
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2020
Admission routes1
Has abstractyes

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