Evidence map›Paper›PMID 42801406›Full record

ArticleWorld journal of microbiology & biotechnology2026

Bioconversion of agricultural waste to ε-poly-L-lysine by an engineered Streptomyces albulus strain.

Rui Huang, Ziling Yuan, Yujie Liu, Liwen Zhang, Guanhua Li, Ruihua Liu, Chao Yang

Abstract read
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In one paragraph

Article in World journal of microbiology & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Rui HuangKey Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China.
Ziling YuanKey Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China.
Yujie LiuKey Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China.
Liwen ZhangKey Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China.
Guanhua LiInner Mongolia Engineering Technology Research Center of Germplasm Resources Conservation and Utilization, School of Life Sciences, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Ruihua LiuKey Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China. yangyangliu@nankai.edu.cn.
Chao YangKey Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China. yangc20119@nankai.edu.cn.

Funding

the Fundamental Research Funds for the Central Universities 63261171Tianjin Natural Science Foundation Project 25JCYBJC01400
6 · The paper itself

Abstract

The vast majority of agricultural wastes are burned or buried in soil currently, causing environmental pollution and resource waste worldwide. To achieve the eco-friendly bioconversion of agricultural waste, this study utilized an engineered Streptomyces albulus for solid-state fermentation (SSF) to convert rapeseed cake into ε-poly-L-lysine (ε-PL), the L-lysine homopolymer widely applied as a natural and safe food preservative. Firstly, based on genome analysis and the property characterization of agricultural waste, the bioconversion potential of S. albulus toward lignocellulosic waste was revealed, followed by preliminary experimental validation. Secondly, the high-titer engineered strain was obtained by overexpressing key genes eno1, eno2, pepc, ask and pls of ε-PL biosynthesis pathway, thereby promoting the bioconversion of rapeseed cake. Among the mutants, S. albulus PLS got the highest ε-PL titer, which was 9.3-fold that of the control strain in SSF. And the ε-PL titer was further increased to 1.6 times the original via optimizing SSF process. Finally, we characterized the growth and development status and the lignocellulose biodegradation of S. albulus PLS during the bioconversion process. Overall, this study identified a strategy for the valorization of agricultural waste, and the engineered strain offers a promising SSF microbial chassis for ε-PL production from lignocellulosic waste.

Indexed as

PolylysineStreptomycesAgricultureBiodegradation, EnvironmentalBiotransformationFermentationLigninMetabolic EngineeringLigninlignocellulosePolylysineMetabolic engineeringSolid-state fermentationStreptomyces albulusWaste bioconversionε-poly-l-lysine

Identifiers

PMID42801406

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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.