An approach combining metagenomics, metatranscriptomics and semi-supervised learning that selects promising plastic-degrading candidate enzymes from the proteome of relevant microorganisms, highlighting the potential of integrating multi-omics with data-driven methods for enzyme discovery and for accelerating the development of biotechnological solutions to plastic pollution.
Abstract
Plastic pollution poses a major threat to the stability of natural ecosystems as well as human health. Microbial enzymes have long been considered a potential resource for targeted biodegradation but, except for a few successful cases, the discovery of efficient enzymes has proved challenging. Aiming to accelerate the process, we propose an approach combining metagenomics, metatranscriptomics and semi-supervised learning that selects promising plastic-degrading candidate enzymes from the proteome of relevant microorganisms. Tested on a dataset of over 10,000 microbial proteins, ranking models consistently prioritize known plastic-degrading enzymes, achieving an area under the cumulative distribution function curve above 0.96, with leave-one-family-out cross-validation indicating that performance is largely retained across protein families. As a case study, this work focuses on mixed microbial cultures exposed for extended periods to polyethylene, polyethylene terephthalate, and polyurethane substrates. The prevalent species after selective enrichment were functionally characterized, finding Rhodococcus aetherivorans as the most relevant species in two of the five cultures under investigation. Among the top-ranked proteins, several have high structural similarity with known enzymes despite not being identified by sequence similarity search. Moreover, according to metatranscriptomics results, several of these enzymes were found to be expressed at the same level or above that of annotated enzymes, suggesting that they may have functional relevance. Overall, this work highlights the potential of integrating multi-omics with data-driven methods for enzyme discovery and for accelerating the development of biotechnological solutions to plastic pollution.
Abstract Plastic waste pollution is a global issue that threatens biodiversity and human health. Current plastic waste management practices are not sufficient to keep up with increasing plastic production rates. Microorganisms have the capacity to degrade different types of bio-based and synthetic plastics through enzymatic reactions, offering an alternative solution to traditional plastic recycling techniques. A limited number of plastic-degrading enzymes have been identified, sequenced and characterized; however, studies exploring the distribution of homologues of these enzymes across habitats and microbial taxa have remained scarce. Here, we applied analytical techniques to search for genes encoding potential plastic-degrading enzymes in environmental metagenome datasets and genomes of the Genome Taxonomy Database (GTDB) to explore the geographic and taxonomic distribution patterns of plastic-degrading microorganisms. Hidden Markov Models (HMMs) were constructed from amino acid sequences of known, experimentally verified and putative plastic-degrading enzymes. The HMMs were applied to landfill, soil, river, lake and ocean metagenomes and all archaeal and bacterial genomes in the GTDB. An abundance of hits was discovered across aquatic and terrestrial metagenomes with the majority occurring in polluted rivers, polar oceans and deep ocean samples. GTDB hits were mainly consistent with known plastic-degrading microbial lineages, while also revealing potential plastic-degrading archaeal taxa. The results of this study may be able to assist in the discovery of novel plastic-degrading enzymes for application in plastic waste biodegradation solutions.
Harmony Douwes, Zuzanna Dutkiewicz, Christian Rinke· Microbial Genomics· 0 citations
Di-n-butyl phthalate (DBP) pollution poses significant ecological risks, necessitating effective green remediation strategies. This study constructed a robust microbial consortium (DBP-Micro-con) and systematically elucidated its degradation mechanism using an integrated multi-omics approach. Community analysis provided that enrichment culture shifted the dominant phylum from Pseudomonadota, Acidobacteriota and Actinomycetota to Bacillota. The consortium achieved 99.07% DBP removal within 120 h, significantly outperforming isolated single strains. It exhibited remarkable environmental robustness across pH 5-10 and temperatures 20-38 °C, alongside broad-spectrum degradation capabilities against di-n-octyl phthalate (DOP) and various strobilurin fungicides. Non-targeted metabolomics identified mono-butyl phthalate (MBP) as intermediate and show metabolic profile changes, particularly in nucleotide and glycerolipid pathways. Furthermore, homology modeling and molecular docking of the key carboxylesterase CES1281, despite moderate sequence similarity, provided structural insights into the catalytic mechanism. This identified a conserved catalytic triad (Ser113-Asp166-His197) and critical hydrophobic interactions stabilizing substrate binding. Collectively, these findings advance the theoretical understanding of PAE biodegradation and offer a promising microbial resource and mechanistic framework for remediation of composite pollution.
A targeted mining workflow is developed that screens exclusively plastic-associated datasets through multi-step bioinformatic filtering—integrating catalytic-motif screening, disulfide-topology validation, structural-similarity scoring, and phylogenetic profiling—to recover high-confidence PETase candidates, resulting in a thermostable enzyme that depolymerizes PET across a broad temperature range.
Konstantinos Rigkos, Dimitra S Bezantakou, Kyriakos Antoniadis et al.· bioRxiv· 0 citations
Activities from several Industrial, agricultural, and urban settings have led to alarming environmental pollution, with about 2.3 billion tonnes of chemicals being produced annually. In this review, we discuss the use of microbial degradation as an eco-friendly and cost-effective option for the cleanup of the environment. We examine the core mechanisms of pollutant degradation, featuring principal microbes such as bacteria (Bacillus spp, Pseudomonas spp, Rhodococcus spp, Alcanivorax spp), fungi (Phanerochaete spp, Chrysosporium spp.), and their enzyme repertoire (oxygenases, dehalogenases, reductases). This review also considers how multi-omics technologies (proteomics, metagenomics, transcriptomics, and metabolomics) have led to a better understanding of microbial consortia interactions in polluted environments by allowing culture-free approaches. We also discuss the roles of biotechnological innovations such as CRISPR-based environmental engineering, synthetic biology, cell-free systems, engineered microbial consortia and artificial intelligence-driven predictive modelling in addressing the issues facing natural attenuation. However, in spite of the significant achievement that have been made using several clean up procedures to prevent or minimize environmental pollution there are still some challenges such as the effect of environmental complexity, microbial competition, and regulations for the use of genetically engineered organisms. This review discuss past prior and ongoing knowledge about microbial biodegradation using peer-reviewed literature from 2012–2026 to provide a framework for converting laboratory discoveries into field-level deployments to enable precision bioremediation approach for polluted ecosystem rehabilitation and public health safety.
Asenuga Ebunoluwa Racheal, Osemudiamen Anao Edene· Dutse Journal of Pure and Ap...· 0 citations
Polyhydroxyalkanoate (PHA) is a biodegradable polymer accumulated by microorganisms and is considered a promising alternative to petroleum-based plastics. This study investigated the operating conditions governing PHA accumulation and the enrichment of PHA-accumulating microorganisms in mixed microbial culture (MMC) systems. Data from four sequencing batch reactors (SBRs), operated in two experimental runs with three stages each, were integrated, including 1,733 days of process data, 400 PHA measurements, and 16S rRNA gene amplicon sequencing data from 48 samples. A leakage-free machine learning framework was applied to interpret the relationships among operating conditions, PHA accumulation, and microbial community dynamics. Under reactor-wise cross-validation, XGBoost explained PHA content from operating conditions alone (R2 = 0.38), and SHAP analysis identified settling strategy, nutrient decoupling, solids retention time (SRT), and feast/famine duration as key factors. PHA-accumulating genera, including Thauera, Paracoccus, and Azoarcus, were enriched compared with the inoculum, but their relative abundance was not significantly correlated with measured PHA content (Spearman’s ρ = −0.34, p = 0.06). In contrast, Thauera abundance was predicted from operating conditions in unseen reactors with relatively high accuracy (R2 = 0.61), whereas most other taxa were not predictable. These results suggest that operating control selectively shapes key PHA-associated microorganisms rather than the entire community. This study provides a data-driven basis for defining operating windows for MMC-based PHA enrichment and AI-assisted process optimization.
Seongbong Heo, Hyeongjun Park, Young Mo Kim· Journal of Korean Society of...· 0 citations
Encrypted antimicrobial peptides (eAMPs) are bioactive fragments embedded within larger proteins and represent an underexplored source of antimicrobial candidates. We developed a multi-layer proteome-mining framework to identify and prioritise eAMPs from 95%-identity-reduced protein sets derived from 265 high-quality bacterial genomes. Three complementary, layer-specific extraction strategies targeting protein termini, internal cleavage sites, and cationic hotspots yielded 29,251,180 unique peptide candidates. Dual AMP prediction with AMP-scanner v2 and Macrel reduced this space to 3,249,772 consensus candidates. Downstream prioritisation followed two complementary routes: a low-haemolysis branch focused on selectivity-oriented candidates and a high-activity branch that retained predicted haemolytic sequences as mechanistic comparators. Structure prediction and review were performed for 185 candidates, and 18 entered Tier-1 developability, novelty, and membrane-activity assessment. Three sequence-matched representatives were selected for experimental evaluation. Molecular-dynamics simulations supported water-phase stability of GEAMP_71c139393ac596b5 and deep anionic-membrane insertion by GEAMP_12ffb5d589c8cb1b. In replicated colony-count assays against Escherichia coli and Staphylococcus aureus, all three peptides showed concentration-dependent activity over 8 – 128 μM. GEAMP_12ffb5d589c8cb1b was the most active, producing 1.52- and 2.27-log10 reductions, respectively, at 128 μM relative to the matched 8 μM condition. Together, these results establish a sequence-traceable workflow linking proteome-scale eAMP discovery with structural prioritisation and experimental activity assessment.