Evidence map›Paper›PMID 37036250›Full record

ReviewActa biochimica et biophysica Sinica2023

Recent advances in the efficient degradation of lignocellulosic metabolic networks by lytic polysaccharide monooxygenase.

Xinran Yu, Yue Zhao, Junhong Yu, Lushan Wang

Open access · diamondAbstract readReview
In one paragraph

Review in Acta biochimica et biophysica Sinica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
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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

4 authors at 2 institutions in 1 country.

Xinran YuState Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China.
Yue ZhaoState Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China.
Junhong YuState Key Laboratory of Biological Fermentation Engineering of Beer, Qingdao 266035, China.
Lushan WangState Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China.
Shandong University · CNState Key Laboratory of Microbial Technology

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Along with long-term evolution, the plant cell wall generates lignocellulose and other anti-degradation barriers to confront hydrolysis by fungi. Lytic polysaccharide monooxygenase (LPMO) is a newly defined oxidase in lignocellulosic degradation systems that significantly fuels hydrolysis. LPMO accepts electrons from wide sources, such as cellobiose dehydrogenase (CDH), glucose-methanol-choline (GMC) oxidoreductases, and small phenols. In addition, the extracellular cometabolic network formed by cosubstrates improves the degradation efficiency, forming a stable and efficient lignocellulose degradation system. In recent years, using structural proteomics to explore the internal structure and the complex redox system of LPMOs has become a research hotspot. In this review, the diversity of LPMOs, catalytic domains, carbohydrate binding modules, direct electron transfer with CDH, cosubstrates, and degradation networks of LPMOs are explored, which can provide a systematic reference for the application of lignocellulosic degradation systems in industrial approaches.

Indexed as

Fungal ProteinsMixed Function OxygenasesElectron TransportLigninPolysaccharidesFungal ProteinsLigninlignocelluloseMixed Function OxygenasesPolysaccharidescosubstratemetabolic networkphylogenetic classificationredox partnerstructural proteomics

Identifiers

PMID37036250
PMCPMC10195143
OpenAlexW4361853910

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.