Background: There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine. Results: Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called "phylopeptidomics" principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset. We illustrate its efficiency using artificial mixtures of two closely related pathogens of clinical interest, and with more complex microbiota models. Conclusions: This approach paves the way to a new vision of taxonomic changes and accurate label-free quantitative metaproteomics for fine-tuned functional characterization.

Background Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples. Results The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases. Conclusions A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information.

Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the full range of biomasses and metabolic activities of the holobiont components, and differentiates between the intestine and hepatopancreatic caecum.

The microbial diversity encompassed by the environmental biosphere is largely unexplored, although it represents an extensive source of new knowledge and potentially of novel enzymatic catalysts for biotechnological applications. To determine the taxonomy of microorganisms, proteotyping by tandem mass spectrometry has proved its efficiency. Its latest extension, phylopeptidomics, adds a biomass quantitation perspective for mixtures of microorganisms. Here, we present an application of phylopeptidomics to rapidly and sensitively screen microorganisms sampled from an industrial environment, i.e., a pool where radioactive material is stored. The power of this methodology is demonstrated through the identification of both prokaryotes and eukaryotes, whether as pure isolates or present as mixtures or consortia. In this study, we established accurate taxonomical identification of environmental prokaryotes belonging to the Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria phyla, as well as eukaryotes from the Ascomycota phylum. The results presented illustrate the potential of tandem mass spectrometry proteotyping, in particular phylopeptidomics, to screen for and rapidly identify microorganisms.

The present study focuses on the use of a metaproteomic approach to analyze Black Extrinsic Tooth Stains, a specific type of pigmented extrinsic substance. Metaproteomics is a powerful emerging technology that successfully enabled human protein and bacterial identification of this specific dental biofilm using high-resolution tandem mass spectrometry. A total of 1600 bacterial proteins were identified in black stain (BS) samples and 2058 proteins in dental plaque (DP) samples, whereas 607 and 582 human proteins were identified in BS and DP samples, respectively. A large diversity of bacteria genera (142) in BS and DP was identified, showing a high prevalence of Rothia, Kingella, Neisseria, and Pseudopropionibacterium in black stain samples. In this work, the high diversity of the dental microbiota and its proteome is highlighted, including significant differences between black stain and dental plaque samples.

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