Evidence map›Paper›PMID 27621727›Full record

ArticleFrontiers in microbiology2016

Characterization and Strain Improvement of a Hypercellulytic Variant, Trichoderma reesei SN1, by Genetic Engineering for Optimized Cellulase Production in Biomass Conversion Improvement.

Yuanchao Qian, Lixia Zhong, Yunhua Hou, Yinbo Qu, Yaohua Zhong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.6field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 45 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Yuanchao QianState Key Laboratory of Microbial Technology, School of Life Sciences, Shandong University Jinan, China.
Lixia ZhongShandong Institute for Food and Drug Control Jinan, China.
Yunhua HouBioengineering Institute, Qilu University of Technology Jinan, China.
Yinbo QuState Key Laboratory of Microbial Technology, School of Life Sciences, Shandong University Jinan, China.
Yaohua ZhongState Key Laboratory of Microbial Technology, School of Life Sciences, Shandong University Jinan, China.
Shandong University · CNQilu University of Technology · CNShandong Institute of Food and Drug Inspection · CNState Key Laboratory of Microbial Technology

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The filamentous fungus Trichoderma reesei is a widely used strain for cellulolytic enzyme production. A hypercellulolytic T. reesei variant SN1 was identified in this study and found to be different from the well-known cellulase producers QM9414 and RUT-C30. The cellulose-degrading enzymes of T. reesei SN1 show higher endoglucanase (EG) activity but lower β-glucosidase (BGL) activity than those of the others. A uracil auxotroph strain, SP4, was constructed by pyr4 deletion in SN1 to improve transformation efficiency. The BGL1-encoding gene bgl1 under the control of a modified cbh1 promoter was overexpressed in SP4. A transformant, SPB2, with four additional copies of bgl1 exhibited a 17.1-fold increase in BGL activity and a 30.0% increase in filter paper activity. Saccharification of corncob residues with crude enzyme showed that the glucose yield of SPB2 is 65.0% higher than that of SP4. These results reveal the feasibility of strain improvement through the development of an efficient genetic transformation platform to construct a balanced cellulase system for biomass conversion.

Indexed as

biomass conversionpretreated corncob residuesTrichoderma reeseiuracil auxotrophyβ-glucosidase

Identifiers

PMID27621727
PMCPMC5002442
OpenAlexW2516943296

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.