Evidence map›Paper›PMID 41863619›Full record

ReviewArchives of microbiology2026

Next-generation strategies for PLA degradation: microbial consortia, metagenomics, enzyme engineering and AI-guided approaches.

Avianto Rio Jonathan, Vignesh Kumar Balasubramanian, Shang-Tse Ho, Yen-Po Chen, Pannida Khunnamwong, Jui-Yu Chou

Abstract readReview
PubMed Publisher
In one paragraph

Review in Archives of microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Avianto Rio JonathanInternational Program for Master of Science in Materials and Biological Technology, and Science Education, National Changhua University of Education, Changhua, 500, Taiwan.
Vignesh Kumar BalasubramanianDepartment of Biology, National Changhua University of Education, Changhua, 500, Taiwan. vigneshkumar591@gmail.com.ORCID http://orcid.org/0000-0002-2461-2648
Shang-Tse HoDepartment of Wood Based Materials and Design, College of Agriculture, National Chiayi University, Chiayi, 600, Taiwan.
Yen-Po ChenDepartment of Animal Science, National Chung Hsing University, 145 Xingda Road, South Dist., Taichung City, 402, Taiwan.
Pannida KhunnamwongDepartment of Microbiology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
Jui-Yu ChouDepartment of Biology, National Changhua University of Education, Changhua, 500, Taiwan. jackyjau@cc.ncue.edu.tw.

Funding

Ministry of Science and Technology, Taiwan MOST 111-2621-B-018-001 to Jui-Yu Chou
6 · The paper itself

Abstract

Polylactic acid (PLA) is one of the most widely used biodegradable bioplastics; however, its slow degradation under natural conditions limits its environmental sustainability. This review summarizes recent advances in microbial and biotechnological strategies that enhance PLA biodegradation across diverse ecosystems. Emerging approaches include screening insect gut microbiota, isolating fungal species with strong adsorption or enzymatic capacities, and exploring soil, compost, and aquatic microbiomes using metagenomics and environmental DNA (eDNA) tools. Microbial consortia, thermophilic degraders, and co-culture systems are highlighted as effective solutions to overcome the intrinsic crystallinity and hydrolysis-dependent breakdown of PLA. Beyond natural systems, this review emphasizes the increasing role of synthetic biology, directed evolution, and artificial intelligence (AI) in engineering high-performance PLA-degrading enzymes. AI-driven structural prediction and machine-learning platforms offer new possibilities for designing robust depolymerases with improved specificity, thermostability, and catalytic efficiency. Collectively, these multidisciplinary strategies provide a roadmap for accelerating PLA degradation in industrial composting, wastewater treatment, and bioremediation. Future integration of ecological screening with computational enzyme engineering is expected to advance scalable and sustainable PLA waste management.

Indexed as

MetagenomicsMicrobial ConsortiaPolyestersAnimalsArtificial IntelligenceBacteriaBiodegradation, EnvironmentalFungiProtein EngineeringPolyestersEnvironmental sustainabilityFungal plastic degradationInsect gut microbiotaMicrobial degradationPLA bioplastics

Identifiers

PMID41863619

What OpenQuestion holds

Textmetadata
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.