Evidence map›Paper›PMID 38724934›Full record

ArticleMicrobial cell factories2024

Screening and genetic engineering of marine-derived Aspergillus terreus for high-efficient production of lovastatin.

Han Na, Yao-Yao Zheng, Yaoning Jia, Jingzhao Feng, Jizi Huang, Jihao Huang, Chang-Yun Wang, Guangshan Yao

Abstract read
In one paragraph

Article in Microbial cell factories, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

8 authors.

Han NaKey Laboratory of Marine Drugs and Key Laboratory of Evolution and Marine Biodiversity (the Ministry of Education of China), Institute of Evolution & Marine Biodiversity, School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China.
Yao-Yao ZhengKey Laboratory of Marine Drugs and Key Laboratory of Evolution and Marine Biodiversity (the Ministry of Education of China), Institute of Evolution & Marine Biodiversity, School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China.
Yaoning JiaKey Laboratory of Marine Drugs and Key Laboratory of Evolution and Marine Biodiversity (the Ministry of Education of China), Institute of Evolution & Marine Biodiversity, School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China.
Jingzhao FengFujian Key Laboratory on Conservation and Sustainable Utilization of Marine Biodiversity, Institute of Oceanography, Minjiang University, Fuzhou, 350108, China.
Jizi HuangFujian Key Laboratory on Conservation and Sustainable Utilization of Marine Biodiversity, Institute of Oceanography, Minjiang University, Fuzhou, 350108, China.
Jihao HuangFujian Key Laboratory on Conservation and Sustainable Utilization of Marine Biodiversity, Institute of Oceanography, Minjiang University, Fuzhou, 350108, China.
Chang-Yun WangKey Laboratory of Marine Drugs and Key Laboratory of Evolution and Marine Biodiversity (the Ministry of Education of China), Institute of Evolution & Marine Biodiversity, School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China. changyun@ouc.edu.cn.
Guangshan YaoFujian Key Laboratory on Conservation and Sustainable Utilization of Marine Biodiversity, Institute of Oceanography, Minjiang University, Fuzhou, 350108, China. 2616@mju.edu.cn.

Funding

Natural Science Foundation of Fujian Province 2021J011043Shandong Provincial Natural Science Foundation (Major Basic Research Projects) ZR2019ZD18the grants from Minjiang University JAT190622the grants from Minjiang University MYK19011the Major Project of Qingdao Marine Science and Technology Center 2022QNLM030003-1the National Natural Science Foundations of China 32100059the National Natural Science Foundations of China 41830535
6 · The paper itself

Abstract

backgroundLovastatin has widespread applications thanks to its multiple pharmacological effects. Fermentation by filamentous fungi represents the major way of lovastatin production. However, the current lovastatin productivity by fungal fermentation is limited and needs to be improved.

resultsIn this study, the lovastatin-producing strains of Aspergillus terreus from marine environment were screened, and their lovastatin productions were further improved by genetic engineering. Five strains of A. terreus were isolated from various marine environments. Their secondary metabolites were profiled by metabolomics analysis using Ultra Performance Liquid Chromatography-Mass spectrometry (UPLC-MS) with Global Natural Products Social Molecular Networking (GNPS), revealing that the production of secondary metabolites was variable among different strains. Remarkably, the strain of A. terreus MJ106 could principally biosynthesize the target drug lovastatin, which was confirmed by High Performance Liquid Chromatography (HPLC) and gene expression analysis. By one-factor experiment, lactose was found to be the best carbon source for A. terreus MJ106 to produce lovastatin. To improve the lovastatin titer in A. terreus MJ106, genetic engineering was applied to this strain. Firstly, a series of strong promoters was identified by transcriptomic and green fluorescent protein reporter analysis. Then, three selected strong promoters were used to overexpress the transcription factor gene lovE encoding the major transactivator for lov gene cluster expression. The results revealed that compared to A. terreus MJ106, all lovE over-expression mutants exhibited significantly more production of lovastatin and higher gene expression. One of them, LovE-b19, showed the highest lovastatin productivity at a titer of 1512 mg/L, which represents the highest production level reported in A. terreus.

conclusionOur data suggested that combination of strain screen and genetic engineering represents a powerful tool for improving the productivity of fungal secondary metabolites, which could be adopted for large-scale production of lovastatin in marine-derived A. terreus.

Indexed as

AspergillusFermentationGenetic EngineeringLovastatinAquatic OrganismsLovastatinGenetic engineeringLovastatinLovEMarine-derived Aspergillus terreusStrong promoter

Identifiers

PMID38724934
PMCPMC11084141

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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.