Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

Metabolomics Investigates the Impact of Plastic Biodegradation on Mealworm Gut Microbiome


Runyu Zhao1*, Mark Blenner2, Yinjie Tang1, Yufei Sun1, Ross Klauer2, Qingbo Yang3, Samira Mahdi3, Ruipu Mu4, Kevin Solomon2


1DOE, Environmental and Chemical Engineering, Washington University; 2Department of Chemical and Biomolecular Engineering, University of Delaware; 3Analytical Chemistry Laboratory, Lincoln University; 4Chemistry Basic Sciences, University of Health Sciences and Pharmacy–St. Louis


This study delves into the plastic-degrading prowess of yellow mealworm (Tenebrio molitor) gut microbiomes, surpassing known microbial isolates in breaking down plastics like polyethylene and polystyrene without pre-treatment. Identified bacterial contributors play a role, but the enhancement of degradation rates—potentially up to 200% through co-feeding with alternative diets—points to unexplored biodegradation mechanisms. To bridge these gaps, the research team utilized metabolomics and computational analyses to dissect the metabolic pathways involved in plastic degradation. Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) were employed for untargeted metabolomic analysis of mealworm gut samples (e.g., C18 reverse phase LC and Orbitrap MS analysis). Given the complexity of the data, advanced data processing was applied with Compound Discoverer for multivariable statistical analysis. Python was also used for principal component analysis (PCA), complemented by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst for pathway enrichment, providing deep insights into the enzymatic and metabolic underpinnings of this process.

To enhance differentiation between standard (i.e., oat) and plastic diets during sample preparation, microbial samples were separated from large pieces of insect tissue using a 900μm pore size filter paper. LC-high resolution mass spectrometry matching results from samples of three diets (i.e., oat, polystyrene, and polyethylene) reveal 217 matched compounds with 31 compounds achieving match scores exceeding 90. Subsequent metabolite identification highlights that 35% of identified metabolites exhibit significantly smaller normalized peak areas in mealworms consuming polystyrene compared to those on a standard diet (p<0.05; fold change >10). Meanwhile, 2% of identified metabolites exhibited significantly larger normalized peak areas. The differential analysis indicates reduced metabolic activity in polystyrene-fed mealworms.

Pathway enrichment analysis, using the KEGG database and MetaboAnalyst 6.0, assesses the impact of metabolites with significant differences. The top-most relevant pathways that may be involved in the mealworm’s response to the plastic diet are starch and sucrose metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and histidine metabolism. Computational algorithm hierarchical cluster analysis and PCA serve as statistical clustering methods for grouping detected metabolites. While these methods provide statistical insights, the biological implications of the clustering results remain unclear. Currently, the team is working on connecting these clusters with pathway databases and biological network methodologies. This metabolomic research collaborates with Lincoln University, one of the oldest historically black colleges in the United States and provides modern analytical technology and data science training to students from underprivileged communities.

Future work will focus on three areas: analyzing microbial metabolism changes with different feeding strategies for deeper insights into plastic biodegradation, enhancing data analysis with the reactome pathway database and biological networks, and integrating machine learning with metabolic interaction network analysis to better understand plastic degradation by insect microbial consortia. The research team aims to develop a predictive model for identifying plastic-degrading enzymes in the yellow mealworm gut microbiome using machine learning algorithms and a context-aware enzyme sequence representation. This strategy, inspired by termite gut microbiota research, aims to discover new enzymatic candidates and metabolic pathways crucial for biodegradation, advancing microbial engineering and environmental remediation while illuminating the complex interactions involved in the role of insect microbial consortia in plastic degradation.

Funding Information

This material is based upon work supported by the DOE Office of Science, BER Program, GSP under award no. DE-SC0023085.