Plastic debris are accumulating in the marine environment and aggregate microorganisms that form a new ecosystem called the plastisphere. Better understanding the plastisphere is crucial as it has self-sufficient organization and carries pathogens or organisms that may be involved in the pollutant adsorption and/or plastic degradation. To date, the plastisphere is mainly described at the taxonomic level and the functioning of its microbial communities still remains poorly documented. In this work, metagenomic and metaproteomic analyzes were performed on the plastisphere of polypropylene and polyethylene plastic debris sampled on a pebble beach from the Mediterranean Sea. Our results confirmed that the plastisphere was organized as self-sufficient ecosystems containing highly active primary producers, heterotrophs and predators such as nematode. Interestingly, the chemical composition of the polymer did not impact the structure of the microbial communities but rather influenced the functions expressed. Despite the fact that the presence of hydrocarbon-degrading bacteria was observed in the metagenomes, polymer degradation metabolisms were not detected at the protein level. Finally, hydrocarbon degrader (i.e., Alcanivorax) and pathogenic bacteria (i.e., Vibrionaceae) were observed in the plastispheres but were not very active as no proteins involved in polymer degradation or pathogeny were detected. This work brings new insights into the functioning of the microbial plastisphere developed on plastic marine debris.☆ This paper has been recommended for acceptance by Maria Cristina Fossi.

Unraveling the complex structure and functioning of microbial communities is essential to accurately predict the impact of perturbations and/or environmental changes. From all molecular tools available today to resolve the dynamics of microbial communities, metaproteomics stands out, allowing the establishment of phenotype–genotype linkages. Despite its rapid development, this technology has faced many technical challenges that still hamper its potential power. How to maximize the number of protein identification, improve quality of protein annotation, and provide reliable ecological interpretation are questions of immediate urgency. In our study, we used a robust metaproteomic workflow combining two protein fractionation approaches (gel-based versus gel-free) and four protein search databases derived from the same metagenome to analyze the same seawater sample. The resulting eight metaproteomes provided different outcomes in terms of (i) total protein numbers, (ii) taxonomic structures, and (iii) protein functions. The characterization and/or representativeness of numerous proteins from ecologically relevant taxa such as Pelagibacterales, Rhodobacterales, and Synechococcales, as well as crucial environmental processes, such as nutrient uptake, nitrogen assimilation, light harvesting, and oxidative stress response, were found to be particularly affected by the methodology. Our results provide clear evidences that the use of different protein search databases significantly alters the biological conclusions in both gel-free and gel-based approaches. Our findings emphasize the importance of diversifying the experimental workflow for a comprehensive metaproteomic study.

Metaproteomics allows to decipher the structure and functionality of microbial communities. Despite its rapid development, crucial steps such as the creation of standardized protein search databases and reliable protein annotation remain challenging. To overcome those critical steps, we developed a new program named mPies (metaProteomics in environmental sciences). mPies allows the creation of protein databases derived from assembled or unassembled metagenomes, and/or public repositories based on taxon IDs, gene or protein names. For the first time, mPies facilitates the automatization of reliable taxonomic and functional consensus annotations at the protein group level, minimizing the well-known protein inference issue, which is commonly encountered in metaproteomics. mPies’ workflow is highly customizable with regards to input data, workflow steps, and parameter adjustment. mPies is implemented in Python 3/Snakemake and freely available on GitHub: https://github.com/johanneswerner/mPies/.ReviewerThis article was reviewed by Dr. Wilson Wen Bin Goh.