BackgroundElucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics, the study of the whole protein complement of a microbial community, can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification.ResultsHere, we present a systematic investigation of variables concerning database construction and annotation and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. In particular, the contribution of experimental metagenomic databases was revealed to be mandatory when dealing with mouse samples. Moreover, the use of a “merged” database, containing all metagenomic sequences from the population under study, was found to be generally preferable over the use of sample-matched databases. We also observed that taxonomic and functional results are strongly database-dependent, in particular when analyzing the mouse gut microbiota. As a striking example, the Firmicutes/Bacteroidetes ratio varied up to tenfold depending on the database used. Finally, assembling reads into longer contigs provided significant advantages in terms of functional annotation yields.ConclusionsThis study contributes to identify host- and database-specific biases which need to be taken into account in a metaproteomic experiment, providing meaningful insights on how to design gut microbiota studies and to perform metaproteomic data analysis. In particular, the use of multiple databases and annotation tools has to be encouraged, even though this requires appropriate bioinformatic resources.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0196-8) contains supplementary material, which is available to authorized users.

Metaproteomics enables the investigation of the protein repertoire expressed by complex microbial communities. However, to unleash its full potential, refinements in bioinformatic approaches for data analysis are still needed. In this context, sequence databases selection represents a major challenge.This work assessed the impact of different databases in metaproteomic investigations by using a mock microbial mixture including nine diverse bacterial and eukaryotic species, which was subjected to shotgun metaproteomic analysis. Then, both the microbial mixture and the single microorganisms were subjected to next generation sequencing to obtain experimental metagenomic- and genomic-derived databases, which were used along with public databases (namely, NCBI, UniProtKB/SwissProt and UniProtKB/TrEMBL, parsed at different taxonomic levels) to analyze the metaproteomic dataset. First, a quantitative comparison in terms of number and overlap of peptide identifications was carried out among all databases. As a result, only 35% of peptides were common to all database classes; moreover, genus/species-specific databases provided up to 17% more identifications compared to databases with generic taxonomy, while the metagenomic database enabled a slight increment in respect to public databases. Then, database behavior in terms of false discovery rate and peptide degeneracy was critically evaluated. Public databases with generic taxonomy exhibited a markedly different trend compared to the counterparts. Finally, the reliability of taxonomic attribution according to the lowest common ancestor approach (using MEGAN and Unipept software) was assessed. The level of misassignments varied among the different databases, and specific thresholds based on the number of taxon-specific peptides were established to minimize false positives. This study confirms that database selection has a significant impact in metaproteomics, and provides critical indications for improving depth and reliability of metaproteomic results. Specifically, the use of iterative searches and of suitable filters for taxonomic assignments is proposed with the aim of increasing coverage and trustworthiness of metaproteomic data.

BackgroundThe study of the gut microbiota (GM) is rapidly moving towards its functional characterization by means of shotgun meta-omics. In this context, there is still no consensus on which microbial functions are consistently and constitutively expressed in the human gut in physiological conditions. Here, we selected a cohort of 15 healthy subjects from a native and highly monitored Sardinian population and analyzed their GMs using shotgun metaproteomics, with the aim of investigating GM functions actually expressed in a healthy human population. In addition, shotgun metagenomics was employed to reveal GM functional potential and to compare metagenome and metaproteome profiles in a combined taxonomic and functional fashion.ResultsMetagenomic and metaproteomic data concerning the taxonomic structure of the GM under study were globally comparable. On the contrary, a considerable divergence between genetic potential and functional activity of the human healthy GM was observed, with the metaproteome displaying a higher plasticity, compared to the lower inter-individual variability of metagenome profiles. The taxon-specific contribution to functional activities and metabolic tasks was also examined, giving insights into the peculiar role of several GM members in carbohydrate metabolism (including polysaccharide degradation, glycan transport, glycolysis, and short-chain fatty acid production). Noteworthy, Firmicutes-driven butyrogenesis (mainly due to Faecalibacterium spp.) was shown to be the metabolic activity with the highest expression rate and the lowest inter-individual variability in the study cohort, in line with the previously reported importance of the biosynthesis of this microbial product for the gut homeostasis.ConclusionsOur results provide detailed and taxon-specific information regarding functions and pathways actively working in a healthy GM. The reported discrepancy between expressed functions and functional potential suggests that caution should be used before drawing functional conclusions from metagenomic data, further supporting metaproteomics as a fundamental approach to characterize the human GM metabolic functions and activities.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0293-3) contains supplementary material, which is available to authorized users.

BackgroundThe massive characterization of host-associated and environmental microbial communities has represented a real breakthrough in the life sciences in the last years. In this context, metaproteomics specifically enables the transition from assessing the genomic potential to actually measuring the functional expression of a microbiome. However, significant research efforts are still required to develop analysis pipelines optimized for metaproteome characterization.ResultsThis work presents an efficient analytical pipeline for shotgun metaproteomic analysis, combining bead-beating/freeze-thawing for protein extraction, filter-aided sample preparation for cleanup and digestion, and single-run liquid chromatography-tandem mass spectrometry for peptide separation and identification. The overall procedure is more time-effective and less labor-intensive when compared to state-of-the-art metaproteomic techniques. The pipeline was first evaluated using mock microbial mixtures containing different types of bacteria and yeasts, enabling the identification of up to over 15,000 non-redundant peptide sequences per run with a linear dynamic range from 104 to 108 colony-forming units. The pipeline was then applied to the mouse fecal metaproteome, leading to the overall identification of over 13,000 non-redundant microbial peptides with a false discovery rate of <1%, belonging to over 600 different microbial species and 250 functionally relevant protein families. An extensive mapping of the main microbial metabolic pathways actively functioning in the gut microbiome was also achieved.ConclusionsThe analytical pipeline presented here may be successfully used for the in-depth and time-effective characterization of complex microbial communities, such as the gut microbiome, and represents a useful tool for the microbiome research community.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-014-0049-2) contains supplementary material, which is available to authorized users.

Unipept (https://unipept.ugent.be) is a web application for metaproteome data analysis, with an initial focus on tryptic-peptide-based biodiversity analysis of MS/MS samples. Because the true potential of metaproteomics lies in gaining insight into the expressed functions of complex environmental samples, the 4.0 release of Unipept introduces complementary functional analysis based on GO terms and EC numbers. Integration of this new functional analysis with the existing biodiversity analysis is an important asset of the extended pipeline. As a proof of concept, a human faecal metaproteome data set from 15 healthy subjects was reanalyzed with Unipept 4.0, yielding fast, detailed, and straightforward characterization of taxon-specific catalytic functions that is shown to be consistent with previous results from a BLAST-based functional analysis of the same data.

Certain MHC-II or HLA-D alleles dominantly protect from particular autoimmune diseases. For example, expression of the MHC-II Eα:Eβ complex potently protects nonobese diabetic (NOD) mice, which normally lack this isotype, from spontaneous development of type 1 diabetes. However, the underlying mechanisms remain debated. We investigated MHC-II–mediated protection from type 1 diabetes using a previously reported NOD mouse line expressing an Eα transgene and, thereby, the Eα:Eβ complex. Eα16/NOD females vertically protected their NOD offspring from diabetes and insulitis, an effect that was dependent on the intestinal microbiota; moreover, they developed autoimmunity when treated with certain antibiotics or raised in a germ-free environment. Genomic and proteomic analyses revealed NOD and Eα16/NOD mice to host mild but significant differences in the intestinal microbiotas during a critical early window of ontogeny, and transfer of cecal contents from the latter to the former suppressed insulitis. Thus, protection from autoimmunity afforded by particular MHC/HLA alleles can operate via intestinal microbes, highlighting potentially important societal implications of treating infants, or even just their pregnant mothers, with antibiotics.

To date, most metaproteomic studies of the gut microbiota employ stool sample pretreatment methods to enrich for microbial components. However, a specific investigation aimed at assessing if, how, and to what extent this may impact on the final taxonomic and functional results is still lacking. Here, stool replicates were either pretreated by differential centrifugation (DC) or not centrifuged. Protein extracts were then processed by filter-aided sample preparation, single-run LC, and high-resolution MS, and the metaproteomic data were compared by spectral counting. DC led to a higher number of identifications, a significantly richer microbial diversity, as well as to reduced information on the nonmicrobial components (host and food) when compared to not centrifuged. Nevertheless, dramatic differences in the relative abundance of several gut microbial taxa were also observed, including a significant change in the Firmicutes/Bacteroidetes ratio. Furthermore, some important microbial functional categories, including cell surface enzymes, membrane-associated proteins, extracellular proteins, and flagella, were significantly reduced after DC. In conclusion, this work underlines that a critical evaluation is needed when selecting the appropriate stool sample processing protocol in the context of a metaproteomic study, depending on the specific target to which the research is aimed. All MS data have been deposited in the ProteomeXchange with identifier PXD001573 (http://proteomecentral.proteomexchange.org/dataset/PXD001573).

SummaryLittle is currently known on the microbial populations colonizing the sheep large intestine, despite their expected key role in host metabolism, physiology and immunity. This study reports the first characterization of the sheep faecal microbiota composition and functions, obtained through the application of a multi‐omic strategy. An optimized protocol was first devised for DNA extraction and amplification from sheep stool samples. Then, 16S rDNA sequencing, shotgun metagenomics and shotgun metaproteomics were applied to unravel taxonomy, genetic potential and actively expressed functions and pathways respectively. Under a taxonomic perspective, the sheep faecal microbiota appeared globally comparable to that of other ruminants, with Firmicutes being the main phylum. In functional terms, we detected 2097 gene and 441 protein families, finding that the sheep faecal microbiota was primarily involved in catabolism. We investigated carbohydrate transport and degradation activities and identified phylum‐specific pathways, such as methanogenesis for Euryarchaeota and acetogenesis for Firmicutes. Furthermore, our approach enabled the identification of proteins expressed by the eukaryotic component of the microbiota. Taken together, these findings unveil structure and role of the distal gut microbiota in sheep, and open the way to further studies aimed at elucidating its connections with management and dietary variables in sheep farming.