ArticleEnvironmental science & technology2026
Machine Learning-Guided Synthetic Microbial Communities Enable Functional and Sustainable Degradation of Persistent Environmental Pollutants.
Article in Environmental science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
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Who cites it
6 citing papers in PubMed.
- Harnessing synthetic microbial communities for desert vegetation restoration.Synthetic and systems biotechnology · 2027Review
- Challenges and Future Directives of Synthetic Biology in Engineering Plant-Microbe Partnerships for Sustainable Agriculture.Biotechnology and bioengineering · 2026Review
- Microbial Responses and Metabolic Mechanisms During Anaerobic Degradation of N,N-Dimethylformamide by Co-Cultured Sludge.Microorganisms · 2026Article
- Convergent GenX biodegradation by genomically designed and functionally screened synthetic bacterial consortia.Frontiers in microbiology · 2026Article
- Synthetic microbial communities for plant growth promotion and soil-borne disease suppression: design principles, mechanisms, and translational challenges.Frontiers in microbiology · 2026Review
- Engineering synthetic microbial communities for soil restoration: from rational design to multi-scale biogeochemical applications.Frontiers in microbiology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Persistent environmental pollutants require diverse microbial metabolic capabilities for effective degradation. While naturally occurring consortia or single strains often fall short in efficiency, synthetic microbial communities (SynComs) hold greater promise for enhanced degradation. To address this challenge, we developed GENIA (Genomically and Environmentally Networked Intelligent Assemblies), a genome-informed, machine learning-guided framework for the rational design of SynComs capable of degrading multiple pollutants. Using a microfluidic high-throughput cultivation platform, 2,155 bacterial strains were isolated from xenobiotic-enriched cotton detritusphere and screened for pollutant-specific growth. Whole-genome sequencing and functional annotation of 45 prioritized strains revealed metabolic traits associated with the degradation of lignin, atrazine, and PFAS. These genomic profiles were encoded into spline-based graph representations and integrated within the GENIA pipeline, which combines graph neural networks, pathway complementarity modeling, and functional redundancy minimization to predict optimal community assemblies. The resulting nine-member community, comprising
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Registered trials
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.