Evidence map›Paper›PMID 41555463›Full record

ReviewJournal of animal science and biotechnology2026

Advancing cellulose degradation through synthetic biology: engineered pathways and microbial systems for sustainable biomass conversion.

Xingqi Liu, Jianping Quan, Ying Li, Xiaofan Wang, Jiangchao Zhao

Abstract readReview
In one paragraph

Review in Journal of animal science and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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.

Xingqi LiuCollege of Animal Science, South China Agricultural University/Guangdong Laboratory for Lingnan Modern Agriculture, Animal Functional Microbiome Lab/State Key Laboratory of Swine and Poultry Breeding Industry/National Engineering Research Center for Breeding Swine Industry/Guangdong Provincial Key Laboratory of Animal Nutrition Control, Guangzhou, Guangdong, 510642, China.
Jianping QuanCollege of Animal Science, South China Agricultural University/Guangdong Laboratory for Lingnan Modern Agriculture, Animal Functional Microbiome Lab/State Key Laboratory of Swine and Poultry Breeding Industry/National Engineering Research Center for Breeding Swine Industry/Guangdong Provincial Key Laboratory of Animal Nutrition Control, Guangzhou, Guangdong, 510642, China.
Ying LiGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Animal Science and Technology, Foshan University, Foshan, 528225, China.
Xiaofan WangCollege of Animal Science, South China Agricultural University/Guangdong Laboratory for Lingnan Modern Agriculture, Animal Functional Microbiome Lab/State Key Laboratory of Swine and Poultry Breeding Industry/National Engineering Research Center for Breeding Swine Industry/Guangdong Provincial Key Laboratory of Animal Nutrition Control, Guangzhou, Guangdong, 510642, China. xxw033@scau.edu.cn.
Jiangchao ZhaoCollege of Animal Science, South China Agricultural University/Guangdong Laboratory for Lingnan Modern Agriculture, Animal Functional Microbiome Lab/State Key Laboratory of Swine and Poultry Breeding Industry/National Engineering Research Center for Breeding Swine Industry/Guangdong Provincial Key Laboratory of Animal Nutrition Control, Guangzhou, Guangdong, 510642, China. jzhao77@scau.edu.cn.

Funding

Double first-class discipline promotion project under grant No. 2023B10564001National Key Research and Development Program of China 2022YFD1300402National Natural Science Foundation of China No. 32202715
6 · The paper itself

Abstract

Fiber, the most abundant organic polymer in nature, is widely recognized as a foundational sustainable material with diverse applications across industrial, medical, and consumer domains. Owing to its renewability and widespread availability, it also serves as a critical alternative energy source in agriculture, enabling more sustainable livestock production through the efficient conversion of fibrous feedstuffs, thereby supporting the principles of a circular bioeconomy. Cellulose, which constitutes up to 80% of plant fiber, contains tightly packed crystalline regions that confer strong resistance to microbial degradation. Other key obstacles to efficient cellulose digestion in the gut include the absence of critical cellulolytic genes, low enzymatic activity, a lack of natural activators, and the presence of cellulase inhibitors. Synthetic biology provides innovative molecular-level strategies to overcome key technical barriers in cellulose degradation. These approaches employ targeted modifications at nucleic acid and protein levels, including the introduction of engineered genes, synthetic regulators, and optimized enzymes, to develop high-performance microbial systems with enhanced cellulose-degrading capabilities. Furthermore, genetic modifications like the knockout of inhibitory genes and knock-in of activator genes, combined with rational redesign of multi-enzyme complexes, can significantly improve the secretion and catalytic efficiency of cellulases. When integrated with artificial intelligence, synthetic biology enables predictive screening and precision engineering of microbial strains for highly efficient cellulose degradation. This review comprehensively summarizes recent advances in synthetic biology approaches for improving cellulose degradation and highlights how these tools can optimize fiber utilization in sustainable agricultural and industrial applications.

Indexed as

Cellulose degradationCellulosomeCRISPR-Cas9Gene editingSynthetic biology

Identifiers

PMID41555463
PMCPMC12817449

What OpenQuestion holds

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LicenceCC BY
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.